task2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 2\n",
    "\n",
    "---\n",
    "\n",
    "## Predictive modeling of customer bookings\n",
    "\n",
    "This Jupyter notebook includes some code to get you started with this predictive modeling task. We will use various packages for data manipulation, feature engineering and machine learning.\n",
    "\n",
    "### Exploratory data analysis\n",
    "\n",
    "First, we must explore the data in order to better understand what we have and the statistical properties of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_passengers</th>\n",
       "      <th>sales_channel</th>\n",
       "      <th>trip_type</th>\n",
       "      <th>purchase_lead</th>\n",
       "      <th>length_of_stay</th>\n",
       "      <th>flight_hour</th>\n",
       "      <th>flight_day</th>\n",
       "      <th>route</th>\n",
       "      <th>booking_origin</th>\n",
       "      <th>wants_extra_baggage</th>\n",
       "      <th>wants_preferred_seat</th>\n",
       "      <th>wants_in_flight_meals</th>\n",
       "      <th>flight_duration</th>\n",
       "      <th>booking_complete</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>262</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>Sat</td>\n",
       "      <td>AKLDEL</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>112</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>Sat</td>\n",
       "      <td>AKLDEL</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>243</td>\n",
       "      <td>22</td>\n",
       "      <td>17</td>\n",
       "      <td>Wed</td>\n",
       "      <td>AKLDEL</td>\n",
       "      <td>India</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5.52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>96</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>Sat</td>\n",
       "      <td>AKLDEL</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>68</td>\n",
       "      <td>22</td>\n",
       "      <td>15</td>\n",
       "      <td>Wed</td>\n",
       "      <td>AKLDEL</td>\n",
       "      <td>India</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   num_passengers sales_channel  trip_type  purchase_lead  length_of_stay  \\\n",
       "0               2      Internet  RoundTrip            262              19   \n",
       "1               1      Internet  RoundTrip            112              20   \n",
       "2               2      Internet  RoundTrip            243              22   \n",
       "3               1      Internet  RoundTrip             96              31   \n",
       "4               2      Internet  RoundTrip             68              22   \n",
       "\n",
       "   flight_hour flight_day   route booking_origin  wants_extra_baggage  \\\n",
       "0            7        Sat  AKLDEL    New Zealand                    1   \n",
       "1            3        Sat  AKLDEL    New Zealand                    0   \n",
       "2           17        Wed  AKLDEL          India                    1   \n",
       "3            4        Sat  AKLDEL    New Zealand                    0   \n",
       "4           15        Wed  AKLDEL          India                    1   \n",
       "\n",
       "   wants_preferred_seat  wants_in_flight_meals  flight_duration  \\\n",
       "0                     0                      0             5.52   \n",
       "1                     0                      0             5.52   \n",
       "2                     1                      0             5.52   \n",
       "3                     0                      1             5.52   \n",
       "4                     0                      1             5.52   \n",
       "\n",
       "   booking_complete  \n",
       "0                 0  \n",
       "1                 0  \n",
       "2                 0  \n",
       "3                 0  \n",
       "4                 0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/customer_booking.csv\", encoding=\"ISO-8859-1\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `.head()` method allows us to view the first 5 rows in the dataset, this is useful for visual inspection of our columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50000 entries, 0 to 49999\n",
      "Data columns (total 14 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   num_passengers         50000 non-null  int64  \n",
      " 1   sales_channel          50000 non-null  object \n",
      " 2   trip_type              50000 non-null  object \n",
      " 3   purchase_lead          50000 non-null  int64  \n",
      " 4   length_of_stay         50000 non-null  int64  \n",
      " 5   flight_hour            50000 non-null  int64  \n",
      " 6   flight_day             50000 non-null  object \n",
      " 7   route                  50000 non-null  object \n",
      " 8   booking_origin         50000 non-null  object \n",
      " 9   wants_extra_baggage    50000 non-null  int64  \n",
      " 10  wants_preferred_seat   50000 non-null  int64  \n",
      " 11  wants_in_flight_meals  50000 non-null  int64  \n",
      " 12  flight_duration        50000 non-null  float64\n",
      " 13  booking_complete       50000 non-null  int64  \n",
      "dtypes: float64(1), int64(8), object(5)\n",
      "memory usage: 5.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `.info()` method gives us a data description, telling us the names of the columns, their data types and how many null values we have. Fortunately, we have no null values. It looks like some of these columns should be converted into different data types, e.g. flight_day.\n",
    "\n",
    "To provide more context, below is a more detailed data description, explaining exactly what each column means:\n",
    "\n",
    "- `num_passengers` = number of passengers travelling\n",
    "- `sales_channel` = sales channel booking was made on\n",
    "- `trip_type` = trip Type (Round Trip, One Way, Circle Trip)\n",
    "- `purchase_lead` = number of days between travel date and booking date\n",
    "- `length_of_stay` = number of days spent at destination\n",
    "- `flight_hour` = hour of flight departure\n",
    "- `flight_day` = day of week of flight departure\n",
    "- `route` = origin -> destination flight route\n",
    "- `booking_origin` = country from where booking was made\n",
    "- `wants_extra_baggage` = if the customer wanted extra baggage in the booking\n",
    "- `wants_preferred_seat` = if the customer wanted a preferred seat in the booking\n",
    "- `wants_in_flight_meals` = if the customer wanted in-flight meals in the booking\n",
    "- `flight_duration` = total duration of flight (in hours)\n",
    "- `booking_complete` = flag indicating if the customer completed the booking\n",
    "\n",
    "Before we compute any statistics on the data, lets do any necessary data conversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Sat', 'Wed', 'Thu', 'Mon', 'Sun', 'Tue', 'Fri'], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"flight_day\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapping = {\n",
    "    \"Mon\": 1,\n",
    "    \"Tue\": 2,\n",
    "    \"Wed\": 3,\n",
    "    \"Thu\": 4,\n",
    "    \"Fri\": 5,\n",
    "    \"Sat\": 6,\n",
    "    \"Sun\": 7,\n",
    "}\n",
    "\n",
    "df[\"flight_day\"] = df[\"flight_day\"].map(mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 3, 4, 1, 7, 2, 5])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"flight_day\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_passengers</th>\n",
       "      <th>purchase_lead</th>\n",
       "      <th>length_of_stay</th>\n",
       "      <th>flight_hour</th>\n",
       "      <th>flight_day</th>\n",
       "      <th>wants_extra_baggage</th>\n",
       "      <th>wants_preferred_seat</th>\n",
       "      <th>wants_in_flight_meals</th>\n",
       "      <th>flight_duration</th>\n",
       "      <th>booking_complete</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.00000</td>\n",
       "      <td>50000.00000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.591240</td>\n",
       "      <td>84.940480</td>\n",
       "      <td>23.04456</td>\n",
       "      <td>9.06634</td>\n",
       "      <td>3.814420</td>\n",
       "      <td>0.668780</td>\n",
       "      <td>0.296960</td>\n",
       "      <td>0.427140</td>\n",
       "      <td>7.277561</td>\n",
       "      <td>0.149560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.020165</td>\n",
       "      <td>90.451378</td>\n",
       "      <td>33.88767</td>\n",
       "      <td>5.41266</td>\n",
       "      <td>1.992792</td>\n",
       "      <td>0.470657</td>\n",
       "      <td>0.456923</td>\n",
       "      <td>0.494668</td>\n",
       "      <td>1.496863</td>\n",
       "      <td>0.356643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.670000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.620000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>17.00000</td>\n",
       "      <td>9.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.570000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>28.00000</td>\n",
       "      <td>13.00000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.830000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>867.000000</td>\n",
       "      <td>778.00000</td>\n",
       "      <td>23.00000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_passengers  purchase_lead  length_of_stay  flight_hour  \\\n",
       "count    50000.000000   50000.000000     50000.00000  50000.00000   \n",
       "mean         1.591240      84.940480        23.04456      9.06634   \n",
       "std          1.020165      90.451378        33.88767      5.41266   \n",
       "min          1.000000       0.000000         0.00000      0.00000   \n",
       "25%          1.000000      21.000000         5.00000      5.00000   \n",
       "50%          1.000000      51.000000        17.00000      9.00000   \n",
       "75%          2.000000     115.000000        28.00000     13.00000   \n",
       "max          9.000000     867.000000       778.00000     23.00000   \n",
       "\n",
       "         flight_day  wants_extra_baggage  wants_preferred_seat  \\\n",
       "count  50000.000000         50000.000000          50000.000000   \n",
       "mean       3.814420             0.668780              0.296960   \n",
       "std        1.992792             0.470657              0.456923   \n",
       "min        1.000000             0.000000              0.000000   \n",
       "25%        2.000000             0.000000              0.000000   \n",
       "50%        4.000000             1.000000              0.000000   \n",
       "75%        5.000000             1.000000              1.000000   \n",
       "max        7.000000             1.000000              1.000000   \n",
       "\n",
       "       wants_in_flight_meals  flight_duration  booking_complete  \n",
       "count           50000.000000     50000.000000      50000.000000  \n",
       "mean                0.427140         7.277561          0.149560  \n",
       "std                 0.494668         1.496863          0.356643  \n",
       "min                 0.000000         4.670000          0.000000  \n",
       "25%                 0.000000         5.620000          0.000000  \n",
       "50%                 0.000000         7.570000          0.000000  \n",
       "75%                 1.000000         8.830000          0.000000  \n",
       "max                 1.000000         9.500000          1.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `.describe()` method gives us a summary of descriptive statistics over the entire dataset (only works for numeric columns). This gives us a quick overview of a few things such as the mean, min, max and overall distribution of each column.\n",
    "\n",
    "From this point, you should continue exploring the dataset with some visualisations and other metrics that you think may be useful. Then, you should prepare your dataset for predictive modelling. Finally, you should train your machine learning model, evaluate it with performance metrics and output visualisations for the contributing variables. All of this analysis should be summarised in your single slide."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier  # Example model\n",
    "from sklearn.datasets import load_iris  # Example dataset\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "# seperate X and y\n",
    "X = df.drop(\"booking_complete\", axis=1)\n",
    "y = df[\"booking_complete\"]\n",
    "\n",
    "categorical_cols = [x for x in df.columns if df[x].dtype==\"object\"]\n",
    "categorical_cols\n",
    "encoder = OrdinalEncoder()\n",
    "\n",
    "X[categorical_cols] = encoder.fit_transform(X[categorical_cols])\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of class 1 14.99%\n"
     ]
    }
   ],
   "source": [
    "y_train_series = pd.Series(y_train)\n",
    "percent_classes = (y_train_series.value_counts()[1] /( y_train_series.value_counts()[0] + y_train_series.value_counts()[1])) * 100\n",
    "print(f\"Percentage of class 1 {percent_classes:.2f}%\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(random_state=42)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1 = RandomForestClassifier(random_state=42)\n",
    "model1.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC-ROC: 0.5450593198832635\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.98      0.92      8520\n",
      "           1       0.54      0.11      0.18      1480\n",
      "\n",
      "    accuracy                           0.85     10000\n",
      "   macro avg       0.70      0.55      0.55     10000\n",
      "weighted avg       0.82      0.85      0.81     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score, confusion_matrix, classification_report\n",
    "y_pred = model1.predict(X_test)\n",
    "print(\"AUC-ROC:\", roc_auc_score(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the confusion matrix \n",
    "import seaborn as sns\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "plt.figure(figsize=(10, 7))\n",
    "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Greens\")\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Actual\")\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances = model1.feature_importances_\n",
    "features = X_train.columns\n",
    "# sort values by importance\n",
    "features_importance = pd.DataFrame(importances, index=features, columns=[\"importance\"]).sort_values(by=\"importance\", ascending=False)\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.barplot(x=features_importance[\"importance\"], y=features_importance.index)\n",
    "plt.title(\"Feature Importance\")\n",
    "plt.ylabel(\"Feature\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The class imbalance in the target variable makes it difficult to distinguish between classes 0 and 1 in the 'booking confirmed' prediction, resulting in a low AUC score. Undersampling may help improve the model's performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest with Undersampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC-ROC: 0.543103667047329\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.99      0.92      8520\n",
      "           1       0.58      0.10      0.17      1480\n",
      "\n",
      "    accuracy                           0.86     10000\n",
      "   macro avg       0.72      0.54      0.54     10000\n",
      "weighted avg       0.82      0.86      0.81     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Bagging With Random Undersampling\n",
    "model2 = RandomForestClassifier(random_state=42, class_weight=\"balanced\")\n",
    "model2.fit(X_train, y_train)\n",
    "y_pred_cw = model2.predict(X_test)\n",
    "print(\"AUC-ROC:\", roc_auc_score(y_test, y_pred_cw))\n",
    "print(classification_report(y_test, y_pred_cw))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1NVVut1u9e/e26xzHTTK3LKvcvuMdX3Oi+opcxxuNBgAAAOCtCt+j4XQ6T9pYHO+RRx7Ro48+qrvuukuS1KJFC23btk3p6enq3bu3IiMjJR1NJBo2bGifl5+fb6cckZGRKikpUUFBgU+qkZ+frzZt2tg1u3fvLnf/PXv2lEtLToVHpwAAAIAa4JdfflGtWr6/vvv5+dnL20ZHRysyMlLLli2zj5eUlGjFihV2ExEXFyd/f3+fml27dmnDhg12TUJCggoLC7V27Vq7Zs2aNSosLLRrKoJEAwAAAPBWyfdbVJXu3bvrL3/5ixo1aqQrr7xS69ev1+TJk9W3b19JRx93Sk1NVVpammJiYhQTE6O0tDQFBQUpOTlZkuRyudSvXz+NGDFCoaGhCgkJ0ciRI9WiRQt16NBBktS0aVN16dJFAwYM0MyZMyVJAwcOVGJiYoVXnJJoNAAAAIAaYerUqXryySc1ePBg5efny+12a9CgQXrqqafsmlGjRqmoqEiDBw9WQUGB4uPjtXTpUtWtW9eumTJlimrXrq2ePXuqqKhI7du319y5c+Xn52fXLFiwQMOGDbNXp0pKSlJGRkalxst7NACgBuA9GgDON+f0ezSSY6rsXtab31bZvaoaczQAAAAAGEejAQAAAMA45mgAAAAA3s7NueA1DokGAAAAAONINAAAAABv5+jytjUNiQYAAAAA40g0AAAAAG8EGkaQaAAAAAAwjkQDAAAA8FaLSMMEEg0AAAAAxpFoAAAAAN4INIwg0QAAAABgHIkGAAAA4I33aBhBogEAAADAOBINAAAAwBt/ijeCrxEAAACAcSQaAAAAgDfmaBhBogEAAADAOBINAAAAwBuBhhEkGgAAAACMI9EAAAAAvDFHwwgSDQAAAADG0WgAAAAAMI5HpwAAAABv/CneCL5GAAAAAMaRaAAAAADemAxuBIkGAAAAAONINAAAAABvBBpGkGgAAAAAMI5EAwAAAPBWi0jDBBINAAAAAMaRaAAAAADeWHXKCBINAAAAAMaRaAAAAADeCDSMINEAAAAAYByJBgAAAODFwRwNI0g0AAAAABhHogEAAAB4IdEwg0QDAAAAgHEkGgAAAIAXAg0zSDQAAAAAGEejAQAAAMA4Hp0CAAAAvNTi2SkjSDQAAAAAGEeiAQAAAHhheVszSDQAAAAAGEeiAQAAAHgh0TCDRAMAAACAcSQaAAAAgBcSDTNINAAAAAAYR6IBAAAAeCHQMINEAwAAAIBxJBoAAACAF+ZomEGiAQAAAMA4Gg0AAADAi8PhqLKtMi699NITXmPIkCGSJMuyNG7cOLndbgUGBqpdu3bauHGjzzWKi4s1dOhQhYWFKTg4WElJSdqxY4dPTUFBgVJSUuRyueRyuZSSkqL9+/dX+nuk0QAAAABqgJycHO3atcveli1bJkm68847JUmTJk3S5MmTlZGRoZycHEVGRqpjx446ePCgfY3U1FQtXLhQmZmZWrlypQ4dOqTExESVlZXZNcnJycrLy1NWVpaysrKUl5enlJSUSo/XYVmW9Rs/8znH0fHi6h4CABhVlPVNdQ8BAIyq4xdU3UM4qbqPXldl99r7589UXFzss8/pdMrpdJ723NTUVH3wwQf69ttvJUlut1upqakaPXq0pKPpRUREhCZOnKhBgwapsLBQDRo00Lx589SrVy9J0s6dOxUVFaUlS5aoc+fO2rRpk5o1a6bs7GzFx8dLkrKzs5WQkKDNmzcrNja2wp+NRAMAAACoJunp6fYjSse29PT0055XUlKi+fPnq2/fvnI4HNqyZYs8Ho86depk1zidTrVt21arVq2SJOXm5qq0tNSnxu12q3nz5nbN6tWr5XK57CZDklq3bi2Xy2XXVBSrTgEAAABeqnLVqTFjxmj48OE++yqSZixatEj79+9Xnz59JEkej0eSFBER4VMXERGhbdu22TUBAQGqX79+uZpj53s8HoWHh5e7X3h4uF1TUTQaAAAAQDWp6GNSx5s1a5a6du0qt9vts//4JsmyrNM2TsfXnKi+Itc5Ho9OAQAAAF4cjqrbzsS2bdv00UcfqX///va+yMhISSqXOuTn59spR2RkpEpKSlRQUHDKmt27d5e75549e8qlJadDowEAAADUIHPmzFF4eLi6detm74uOjlZkZKS9EpV0dB7HihUr1KZNG0lSXFyc/P39fWp27dqlDRs22DUJCQkqLCzU2rVr7Zo1a9aosLDQrqkoHp0CAAAAaogjR45ozpw56t27t2rX/t+v8g6HQ6mpqUpLS1NMTIxiYmKUlpamoKAgJScnS5JcLpf69eunESNGKDQ0VCEhIRo5cqRatGihDh06SJKaNm2qLl26aMCAAZo5c6YkaeDAgUpMTKzUilMSjQYAAADgo1YVTgavrI8++kjbt29X3759yx0bNWqUioqKNHjwYBUUFCg+Pl5Lly5V3bp17ZopU6aodu3a6tmzp4qKitS+fXvNnTtXfn5+ds2CBQs0bNgwe3WqpKQkZWRkVHqsvEcDAGoA3qMB4HxzLr9Ho/7jravsXgV/ya6ye1U1Eg0AAADAS1Uub3s+YzI4AAAAAONINAAAAAAvJBpmkGgAAAAAMI5EAwAAAPBCoGEGiQYAAAAA40g0AAAAAC/M0TCDRAMAAACAcSQaAAAAgBcSDTNINAAAAAAYR6IBAAAAeCHRMINEAwAAAIBxJBoAAACAFxINM0g0AAAAABhHogEAAAB4IdAwg0QDAAAAgHE0GgAAAACM49EpAAAAwAuTwc0g0QAAAABgHIkGAAAA4IVEwwwSDQAAAADGkWgAAAAAXmqRaBhBogEAAADAOBINAAAAwAuBhhkkGgAAAACMI9EAAAAAvLDqlBkkGgAAAACMI9EAAAAAvDhEomECiQYAAAAA40g0AAAAAC/M0TCDRAO/a361/PRMn0f0wxur9MsH3+n7N/6tJ+9NPek/MDMemiBr2Q499H/9fPYPuPUeffzcX1W4aJOsZTvkCq530nsG+Ado/Yx/ylq2Q39o0szo5wGAE8ldl6uhgx9Sh7Yd9YdmLbX8o499jluWpekZM9ShbUdd17K1+vXur+++/b7cdT7P+1z9/zRQ8XEJuiH+RvXr3V+//vprVX0MADUMjQZ+10bfNVj3J6bowYwn1LRfO416NU2P3Hm/hvboW672tjadFd+0pX7a6yl3LMhZR1k5nyjtrYzT3nPSgMe1c99uI+MHgIoo+qVIsbGX69EnHj3h8Tmz5mre6/P16BOPasE78xUaFqr7+9+vw4cP2zWf532uwQMfVEKb1lqQOV8L3p6vu5J7qVYtfpXA+cfhcFTZdj7j0Sn8riU0jdPfVy3VkrXLJUnbdu/Q3TffplaXX+VT5w6NVMaD49V5zD36x/jXy13nxYWzJEltr0o45f26XHuzOsXdpDv+PFC3XneLoU8BAKd2w0036IabbjjhMcuytOCNN9V/UD916NhekjQ+/RndcmN7LfngQ93Z64+SpGcnPK+7771L/Qb87w8xl1x6ydkfPIAaiz9D4Hdt5YYctW95vWIuipYkXdW4qW5ofq3deEhH/6oxb/SLevavM/TVtm/O+F7hF4bp1YcnKWXiQ/qluOg3jx0ATPhpx0/au3evEtr87w8lAQEBimsVp8/zPpck7dv3s7784kuFhITovuTeuvnG9up7Xz/9J3d9dQ0bOKscjqrbzmfVmmjs2LFD06dP16pVq+TxeORwOBQREaE2bdro/vvvV1RU1GmvUVxcrOLiYt+dRyyp1nn+/xyMmPj2NLmC62rz7BUqO1Imv1p+enzORGV+/He7ZnSvwfrvkf/qpf+fWpypuY9M0YwP5in3my90ScTFv3XoAGDE3r17JUmhYSE++0PDQrVz5y5J0k87dkiSZkybqeGPPKzYK2L1weIPNLDvIL3797+SbAA4oWprNFauXKmuXbsqKipKnTp1UqdOnWRZlvLz87Vo0SJNnTpVH374oa6//vpTXic9PV1//vOffXdG15WanHwyLnBMr3ZJurf97UpOf1Abt36jqy+7Ui88ME479+3WG8v+pmtiWuih/+unawZ3/U33Gdqjr+oFX6D0zNPP4QCA6nD8s+KWZdn7jhw5Ikn6Y8871OP22yRJTZtdoTXZa7Xovb/roeHDqnawwFl2vs+dqCrV1mg8/PDD6t+/v6ZMmXLS46mpqcrJyTnldcaMGaPhw4f77HP9X1Nj48T57dkBT2jC29P09ieLJUkbtm7WJeEXacxdD+qNZX/Tjc2vU/iFYdq+YI19Tm2/2np+0FNKvb2/olNOPSfjmFuubqPWV1yj4iU/+OxfN22JFvxrofo8+7C5DwUAlRAWFiZJ2rtnnxo0aGDv/3nfzwoNPZpyhP3//Y2bNPY5N7pxtDy7yi+QAQBSNTYaGzZs0Pz58096fNCgQZoxY8Zpr+N0OuV0On138tgUKiioTqD9l7pjyo6U2auozPvoXX20fqXP8X+mL9C8j97VnH++XeH7DJv2lJ6Y+6z9szs0QksnvKle4wdrzWaecQZQfS66+CKFhYUpe3W2mja7QpJUWlKq3HW5emj4Q0drLnKrQXgDbd261efcbVu36YYbT/3kAYDfr2prNBo2bKhVq1YpNjb2hMdXr16thg0bVvGo8HvzfvYyPZ48TNvzf9LGbd+o5WXNNfyOgZr9/5uInw/u188H9/ucU/rfUnl+ztc3O/6XTkTUb6DIkAa67KJLJUktoq/QwaJD2p6/UwUH9+vHPTulPf+7xqGio0tGfr9rq37au+usfkYA+OXwL9q+/Uf7559++kmbN30tl6ueGrob6p77kjXrlVlqdEkjNbqkkWa9Mkt16tTRrYlHHxt1OBzq07e3pmfMUGzs5Yq9IlaL//6+tm7ZqudfePZktwVqLB6dMqPaGo2RI0fq/vvvV25urjp27KiIiAg5HA55PB4tW7ZMr732ml544YXqGh5+J4ZmPKln+jyil4elKfzCMO3c59HMf8zX0/NfqNR17k9M0bj7/vcI32dT3pMk9Xn2Yb2+9K8mhwwAlbZx41fq32eA/fNzE5+XJCX16K5n0p7Wn/r1UfGvxUp7Ol0HDhxQi6uaa/pr0xUcHGyfc+9996i4uFjPTnxehYWFio29XDNem66oRqdfuAXA75PDsiyrum7+9ttva8qUKcrNzVVZWZkkyc/PT3FxcRo+fLh69ux5Rtd1dGRFHwDnl6KsM19aGQDORXX8gqp7CCd1+eQuVXavb4ZnVdm9qlq1Lm/bq1cv9erVS6WlpfbyemFhYfL396/OYQEAAAD4jc6JN4P7+/szHwMAAADnBKZomMGbwQEAAAAYd04kGgAAAMC5glWnzCDRAAAAAGAciQYAAADghUTDDBINAAAAAMaRaAAAAABeSDTMINEAAAAAYByJBgAAAOCFQMMMEg0AAAAAxpFoAAAAAF6Yo2EGiQYAAABQQ/z000+69957FRoaqqCgIF199dXKzc21j1uWpXHjxsntdiswMFDt2rXTxo0bfa5RXFysoUOHKiwsTMHBwUpKStKOHTt8agoKCpSSkiKXyyWXy6WUlBTt37+/UmOl0QAAAAC8OByOKtsqo6CgQNdff738/f314Ycf6quvvtLzzz+vCy+80K6ZNGmSJk+erIyMDOXk5CgyMlIdO3bUwYMH7ZrU1FQtXLhQmZmZWrlypQ4dOqTExESVlZXZNcnJycrLy1NWVpaysrKUl5enlJSUyn2PlmVZlTqjBnB0vLi6hwAARhVlfVPdQwAAo+r4BVX3EE6qxbTuVXavL4e8X+HaRx99VP/+97/12WefnfC4ZVlyu91KTU3V6NGjJR1NLyIiIjRx4kQNGjRIhYWFatCggebNm6devXpJknbu3KmoqCgtWbJEnTt31qZNm9SsWTNlZ2crPj5ekpSdna2EhARt3rxZsbGxFRoviQYAAABQTYqLi3XgwAGfrbi4+IS1ixcvVqtWrXTnnXcqPDxcLVu21Kuvvmof37Jlizwejzp16mTvczqdatu2rVatWiVJys3NVWlpqU+N2+1W8+bN7ZrVq1fL5XLZTYYktW7dWi6Xy66pCBoNAAAAwEtVPjqVnp5uz4M4tqWnp59wXD/88IOmT5+umJgY/fOf/9T999+vYcOG6Y033pAkeTweSVJERITPeREREfYxj8ejgIAA1a9f/5Q14eHh5e4fHh5u11QEq04BAAAA1WTMmDEaPny4zz6n03nC2iNHjqhVq1ZKS0uTJLVs2VIbN27U9OnTdd9999l1x8/9sCzrtPNBjq85UX1FruONRAMAAADw4nBU3eZ0OlWvXj2f7WSNRsOGDdWsWTOffU2bNtX27dslSZGRkZJULnXIz8+3U47IyEiVlJSooKDglDW7d+8ud/89e/aUS0tOhUYDAAAAqAGuv/56ff311z77vvnmG11yySWSpOjoaEVGRmrZsmX28ZKSEq1YsUJt2rSRJMXFxcnf39+nZteuXdqwYYNdk5CQoMLCQq1du9auWbNmjQoLC+2aiuDRKQAAAMDLufrCvocfflht2rRRWlqaevbsqbVr1+qVV17RK6+8IunouFNTU5WWlqaYmBjFxMQoLS1NQUFBSk5OliS5XC7169dPI0aMUGhoqEJCQjRy5Ei1aNFCHTp0kHQ0JenSpYsGDBigmTNnSpIGDhyoxMTECq84JdFoAAAAADXCtddeq4ULF2rMmDF6+umnFR0drRdeeEH33HOPXTNq1CgVFRVp8ODBKigoUHx8vJYuXaq6devaNVOmTFHt2rXVs2dPFRUVqX379po7d678/PzsmgULFmjYsGH26lRJSUnKyMio1Hh5jwYA1AC8RwPA+eZcfo/G1TN7VNm98gYtqrJ7VTXmaAAAAAAwjkenAAAAAC/n6hyNmoZEAwAAAIBxJBoAAACAFwINM0g0AAAAABhHogEAAAB4YY6GGSQaAAAAAIwj0QAAAAC8kGiYQaIBAAAAwDgSDQAAAMALiYYZJBoAAAAAjCPRAAAAALwQaJhBogEAAADAOBoNAAAAAMbx6BQAAADghcngZpBoAAAAADCORAMAAADwQqJhBokGAAAAAONINAAAAAAvJBpmkGgAAAAAMI5EAwAAAPBComEGiQYAAAAA40g0AAAAAC8EGmaQaAAAAAAwjkQDAAAA8MIcDTNINAAAAAAYR6IBAAAAeCHRMINEAwAAAIBxJBoAAACAFxINM0g0AAAAABhHogEAAAB4IdAwg0QDAAAAgHE0GgAAAACM49EpAAAAwAuTwc0g0QAAAABgHIkGAAAA4I1EwwgSDQAAAADGkWgAAAAAXpijYQaJBgAAAADjSDQAAAAAL7UINIwg0QAAAABgHIkGAAAA4IU5GmaQaAAAAAAwjkQDAAAA8FKLRMMIEg0AAAAAxpFoAAAAAF6Yo2EGiQYAAAAA40g0AAAAAC/8Jd4MvkcAAAAAxpFoAAAAAF5YdcoMEg0AAAAAxpFoAAAAAF5YdcoMEg0AAAAAxtFoAAAAADCORgMAAADwUsvhqLKtMsaNGyeHw+GzRUZG2scty9K4cePkdrsVGBiodu3aaePGjT7XKC4u1tChQxUWFqbg4GAlJSVpx44dPjUFBQVKSUmRy+WSy+VSSkqK9u/fX/nvsdJnAAAAAKgWV155pXbt2mVvX375pX1s0qRJmjx5sjIyMpSTk6PIyEh17NhRBw8etGtSU1O1cOFCZWZmauXKlTp06JASExNVVlZm1yQnJysvL09ZWVnKyspSXl6eUlJSKj1WJoMDAAAAXs7lyeC1a9f2STGOsSxLL7zwgh5//HHdfvvtkqTXX39dERERevPNNzVo0CAVFhZq1qxZmjdvnjp06CBJmj9/vqKiovTRRx+pc+fO2rRpk7KyspSdna34+HhJ0quvvqqEhAR9/fXXio2NrfBYSTQAAACAalJcXKwDBw74bMXFxSet//bbb+V2uxUdHa277rpLP/zwgyRpy5Yt8ng86tSpk13rdDrVtm1brVq1SpKUm5ur0tJSnxq3263mzZvbNatXr5bL5bKbDElq3bq1XC6XXVNRNBoAAACAl1pVuKWnp9tzIY5t6enpJxxXfHy83njjDf3zn//Uq6++Ko/HozZt2mjfvn3yeDySpIiICJ9zIiIi7GMej0cBAQGqX7/+KWvCw8PL3Ts8PNyuqSgenQIAAACqyZgxYzR8+HCffU6n84S1Xbt2tf+7RYsWSkhIUJMmTfT666+rdevWkso/9mVZ1mkfBTu+5kT1FbnO8Ug0AAAAAC9VueqU0+lUvXr1fLaTNRrHCw4OVosWLfTtt9/a8zaOTx3y8/PtlCMyMlIlJSUqKCg4Zc3u3bvL3WvPnj3l0pLTfo+VqgYAAABwTiguLtamTZvUsGFDRUdHKzIyUsuWLbOPl5SUaMWKFWrTpo0kKS4uTv7+/j41u3bt0oYNG+yahIQEFRYWau3atXbNmjVrVFhYaNdUFI9OAQAAAF7O1VWnRo4cqe7du6tRo0bKz8/X+PHjdeDAAfXu3VsOh0OpqalKS0tTTEyMYmJilJaWpqCgICUnJ0uSXC6X+vXrpxEjRig0NFQhISEaOXKkWrRoYa9C1bRpU3Xp0kUDBgzQzJkzJUkDBw5UYmJipVackmg0AAAAgBphx44duvvuu7V37141aNBArVu3VnZ2ti655BJJ0qhRo1RUVKTBgweroKBA8fHxWrp0qerWrWtfY8qUKapdu7Z69uypoqIitW/fXnPnzpWfn59ds2DBAg0bNsxenSopKUkZGRmVHq/DsizrN37mc46j48XVPQQAMKoo65vqHgIAGFXHL6i6h3BSPZfcX2X3eufWGVV2r6rGHA0AAAAAxvHoFAAAAODl3JyhUfOQaAAAAAAwjkQDAAAA8FLrHF11qqYh0QAAAABgHIkGAAAA4IVEwwwSDQAAAADG0WgAAAAAMI5HpwAAAAAvDh6dMoJEAwAAAIBxJBoAAACAFyaDm0GiAQAAAMA4Eg0AAADAC3mGGSQaAAAAAIwj0QAAAAC8MEfDDBINAAAAAMaRaAAAAABeSDTMINEAAAAAYByJBgAAAOCFN4ObQaIBAAAAwDgSDQAAAMALczTMINEAAAAAYByJBgAAAOCFPMOMCjUaixcvrvAFk5KSzngwAAAAAM4PFWo0evToUaGLORwOlZWV/ZbxAAAAANWKORpmVKjROHLkyNkeBwAAAIDzCHM0AAAAAC8kGmacUaNx+PBhrVixQtu3b1dJSYnPsWHDhhkZGAAAAICaq9KNxvr163Xrrbfql19+0eHDhxUSEqK9e/cqKChI4eHhNBoAAAAAKv8ejYcffljdu3fXzz//rMDAQGVnZ2vbtm2Ki4vTc889dzbGCAAAAFQZh8NRZdv5rNKNRl5enkaMGCE/Pz/5+fmpuLhYUVFRmjRpkh577LGzMUYAAAAANUylGw1/f3+7+4qIiND27dslSS6Xy/5vAAAAoKaqVYXb+azSczRatmypdevW6fLLL9fNN9+sp556Snv37tW8efPUokWLszFGAAAAADVMpRuptLQ0NWzYUJL0zDPPKDQ0VA888IDy8/P1yiuvGB8gAAAAUJWYo2FGpRONVq1a2f/doEEDLVmyxOiAAAAAANR8vLAPAAAA8MIL+8yodKMRHR19ypjnhx9++E0DAgAAAFDzVbrRSE1N9fm5tLRU69evV1ZWlh555BFT4wIAAACqBYmGGZVuNB566KET7p82bZrWrVv3mwcEAAAAoOYztnxv165d9e6775q6HAAAAFAtWHXKDGONxt/+9jeFhISYuhwAAACAGuyMXtjn3X1ZliWPx6M9e/bo5ZdfNjq4M7V14SfVPQQAMMqSVd1DAIDfjVo6v5OGqlLpRuO2227zaTRq1aqlBg0aqF27drriiiuMDg4AAABAzVTpRmPcuHFnYRgAAADAueF8nztRVSo9R8PPz0/5+fnl9u/bt09+fn5GBgUAAACgZqt0omFZJ35OuLi4WAEBAb95QAAAAEB14j0aZlS40XjppZckHY2SXnvtNV1wwQX2sbKyMn366afM0QAAAAAgqRKNxpQpUyQdTTRmzJjh85hUQECALr30Us2YMcP8CAEAAADUOBVuNLZs2SJJuvnmm/Xee++pfv36Z21QAAAAQHVxsLytEZWeo/Hxxx+fjXEAAAAAOI9UetWpP/7xj5owYUK5/c8++6zuvPNOI4MCAAAAqovD4aiy7XxW6UZjxYoV6tatW7n9Xbp00aeffmpkUAAAAABqtko/OnXo0KETLmPr7++vAwcOGBkUAAAAUF1Y3taMSicazZs319tvv11uf2Zmppo1a2ZkUAAAAABqtko3Gk8++aSeeeYZ9e7dW6+//rpef/113XfffRo/fryefPLJszFGAAAAoMo4VKvKtjOVnp4uh8Oh1NRUe59lWRo3bpzcbrcCAwPVrl07bdy40ee84uJiDR06VGFhYQoODlZSUpJ27NjhU1NQUKCUlBS5XC65XC6lpKRo//79lR5jpT9dUlKSFi1apO+++06DBw/WiBEj9NNPP2n58uW69NJLKz0AAAAAABWXk5OjV155RVdddZXP/kmTJmny5MnKyMhQTk6OIiMj1bFjRx08eNCuSU1N1cKFC5WZmamVK1fq0KFDSkxMVFlZmV2TnJysvLw8ZWVlKSsrS3l5eUpJSan0OB2WZVln/jGl/fv3a8GCBZo1a5Y+//xzn0FWl22HvqvuIQCAUeGBDat7CABgVKBfcHUP4aTGrR1Xdfe6rnL3OnTokK655hq9/PLLGj9+vK6++mq98MILsixLbrdbqampGj16tKSj6UVERIQmTpyoQYMGqbCwUA0aNNC8efPUq1cvSdLOnTsVFRWlJUuWqHPnztq0aZOaNWum7OxsxcfHS5Kys7OVkJCgzZs3KzY2tsJjPeO8Zvny5br33nvldruVkZGhW2+9VevWrTvTywEAAAC/O8XFxTpw4IDPVlxcfNL6IUOGqFu3burQoYPP/i1btsjj8ahTp072PqfTqbZt22rVqlWSpNzcXJWWlvrUuN1uNW/e3K5ZvXq1XC6X3WRIUuvWreVyueyaiqpUo7Fjxw6NHz9ejRs31t1336369eurtLRU7777rsaPH6+WLVtW6uYAAADAuaYq36ORnp5uz4U4tqWnp59wXJmZmfrPf/5zwuMej0eSFBER4bM/IiLCPubxeBQQEKD69eufsiY8PLzc9cPDw+2aiqpwo3HrrbeqWbNm+uqrrzR16lTt3LlTU6dOrdTNAAAAAPzPmDFjVFhY6LONGTOmXN2PP/6ohx56SPPnz1edOnVOer3jXwJoWdZpXwx4fM2J6ityneNV+D0aS5cu1bBhw/TAAw8oJiamUjcBAAAAagqHqu49Gk6nU06n87R1ubm5ys/PV1xcnL2vrKxMn376qTIyMvT1119LOppINGz4v3l9+fn5dsoRGRmpkpISFRQU+KQa+fn5atOmjV2ze/fucvffs2dPubTkdCqcaHz22Wc6ePCgWrVqpfj4eGVkZGjPnj2VuhkAAACAymvfvr2+/PJL5eXl2VurVq10zz33KC8vT40bN1ZkZKSWLVtmn1NSUqIVK1bYTURcXJz8/f19anbt2qUNGzbYNQkJCSosLNTatWvtmjVr1qiwsNCuqagKJxoJCQlKSEjQiy++qMzMTM2ePVvDhw/XkSNHtGzZMkVFRalu3bqVujkAAABwrjkX3wxet25dNW/e3GdfcHCwQkND7f2pqalKS0tTTEyMYmJilJaWpqCgICUnJ0uSXC6X+vXrpxEjRig0NFQhISEaOXKkWrRoYU8ub9q0qbp06aIBAwZo5syZkqSBAwcqMTGxUitOSWew6lRQUJD69u2rlStX6ssvv9SIESM0YcIEhYeHKykpqbKXAwAAAGDAqFGjlJqaqsGDB6tVq1b66aeftHTpUp8wYMqUKerRo4d69uyp66+/XkFBQXr//ffl5+dn1yxYsEAtWrRQp06d1KlTJ1111VWaN29epcfzm9+jIR19Puz999/X7NmztXjx4t96ud+M92gAON/wHg0A55tz+T0af8kdX2X3ejzuiSq7V1U78/eee/Hz81OPHj3OiSYDAAAAQPWr8BwNAAAA4Peglpm/xf/u8S0CAAAAMI5GAwAAAIBxPDoFAAAAeKnsG7BxYiQaAAAAAIwj0QAAAAC8kGiYQaIBAAAAwDgSDQAAAMBLLZFomECiAQAAAMA4Eg0AAADAC3M0zCDRAAAAAGAciQYAAADgpRaJhhEkGgAAAACMI9EAAAAAvDhYdcoIEg0AAAAAxpFoAAAAAF5qOfhbvAl8iwAAAACMI9EAAAAAvPAeDTNINAAAAAAYR6IBAAAAeGHVKTNINAAAAAAYR6MBAAAAwDgenQIAAAC81GIyuBEkGgAAAACMI9EAAAAAvDAZ3AwSDQAAAADGkWgAAAAAXpijYQaJBgAAAADjSDQAAAAALw4Hf4s3gW8RAAAAgHEkGgAAAIAXVp0yg0QDAAAAgHEkGgAAAIAXVp0yg0QDAAAAgHEkGgAAAIAXB4mGESQaAAAAAIwj0QAAAAC81GLVKSNINAAAAAAYR6IBAAAAeGGOhhkkGgAAAACMI9EAAAAAvDgc/C3eBL5FAAAAAMbRaAAAAAAwjkenAAAAAC8sb2sGiQYAAAAA40g0AAAAAC8sb2sGiQYAAAAA40g0AAAAAC8O5mgYQaIBAAAAwDgSDQAAAMALczTMINEAAAAAYByJBgAAAOCF92iYQaIBAAAAwDgaDQAAAMCLw1GryrbKmD59uq666irVq1dP9erVU0JCgj788EP7uGVZGjdunNxutwIDA9WuXTtt3LjR5xrFxcUaOnSowsLCFBwcrKSkJO3YscOnpqCgQCkpKXK5XHK5XEpJSdH+/fsr/T3SaAAAAAA1wMUXX6wJEyZo3bp1WrdunW655RbddtttdjMxadIkTZ48WRkZGcrJyVFkZKQ6duyogwcP2tdITU3VwoULlZmZqZUrV+rQoUNKTExUWVmZXZOcnKy8vDxlZWUpKytLeXl5SklJqfR4HZZlWb/9Y59bth36rrqHAABGhQc2rO4hAIBRgX7B1T2Ek3rn+/lVdq+eTe79TeeHhITo2WefVd++feV2u5WamqrRo0dLOppeREREaOLEiRo0aJAKCwvVoEEDzZs3T7169ZIk7dy5U1FRUVqyZIk6d+6sTZs2qVmzZsrOzlZ8fLwkKTs7WwkJCdq8ebNiY2MrPDYSDQAAAKCaFBcX68CBAz5bcXHxac8rKytTZmamDh8+rISEBG3ZskUej0edOnWya5xOp9q2batVq1ZJknJzc1VaWupT43a71bx5c7tm9erVcrlcdpMhSa1bt5bL5bJrKopGAwAAAPDicDiqbEtPT7fnQhzb0tPTTzq2L7/8UhdccIGcTqfuv/9+LVy4UM2aNZPH45EkRURE+NRHRETYxzwejwICAlS/fv1T1oSHh5e7b3h4uF1TUSxvCwAAAFSTMWPGaPjw4T77nE7nSetjY2OVl5en/fv3691331Xv3r21YsUK+/jxLxu0LOu0LyA8vuZE9RW5zvFoNAAAAAAvjip8j4bT6TxlY3G8gIAAXXbZZZKkVq1aKScnRy+++KI9L8Pj8ahhw//N68vPz7dTjsjISJWUlKigoMAn1cjPz1ebNm3smt27d5e77549e8qlJafDo1MAAABADWVZloqLixUdHa3IyEgtW7bMPlZSUqIVK1bYTURcXJz8/f19anbt2qUNGzbYNQkJCSosLNTatWvtmjVr1qiwsNCuqSgSDQAAAKAGeOyxx9S1a1dFRUXp4MGDyszM1CeffKKsrCw5HA6lpqYqLS1NMTExiomJUVpamoKCgpScnCxJcrlc6tevn0aMGKHQ0FCFhIRo5MiRatGihTp06CBJatq0qbp06aIBAwZo5syZkqSBAwcqMTGxUitOSTQaAAAAgI/KzkWoKrt371ZKSop27doll8ulq666SllZWerYsaMkadSoUSoqKtLgwYNVUFCg+Ph4LV26VHXr1rWvMWXKFNWuXVs9e/ZUUVGR2rdvr7lz58rPz8+uWbBggYYNG2avTpWUlKSMjIxKj5f3aABADcB7NACcb87l92i8u+WtKrvXHdF3V9m9qhqJBgAAAOClVhVOBj+fMRkcAAAAgHEkGgAAAICXc3WORk1DogEAAADAOBINAAAAwIuDv8UbwbcIAAAAwDgSDQAAAMALczTMINEAAAAAYByJBgAAAODFwXs0jCDRAAAAAGAciQYAAADgpRZzNIwg0QAAAABgHIkGAAAA4IU5GmaQaAAAAAAwjkQDAAAA8MJ7NMwg0QAAAABgHIkGAAAA4MXB3+KN4FsEAAAAYByNBgAAAADjeHQKAAAA8MJkcDNINAAAAAAYR6IBAAAAeKnFC/uMINEAAAAAYByJBgAAAOCFORpmkGgAAAAAMI5EAwAAAPDiYI6GESQaAAAAAIwj0QAAAAC8MEfDDBINAAAAAMaRaAAAAABeHPwt3gi+RQAAAADGkWgAAAAAXmoxR8MIEg0AAAAAxpFoAAAAAF54j4YZJBoAAAAAjCPRAAAAALzwHg0zSDQAAAAAGEejAQAAAMA4Hp0CAAAAvDAZ3AwSDQAAAADGkWgAAAAAXpgMbgaNBn7XvvjPBv31jXf17abv9PPenzX2uSd0/c0J9vE3Zi7QJ//8VHt275G/f23FNL1MfQbfp6YtrrBrXvjLVK1fk6d9e39WYGAdNftDU/Ub+ic1io4qd7+SklIN6/2wfvhmi6a/+ZKaxDapks8J4Pctd12uXp/9hjZt3KQ9e/Zq8kvP65YON5+w9pmx4/XuX9/TyEdH6N777vE59nne58p4cZq+/GKDateurdgrYjVt5lTVqVOnKj4GgBqGR6fwu/Zr0a9qfHm0Hhx9/wmPX9zoIj04+n698vY0TZ71rCIaRmjMkCe1v6DQrolpeplGjHtYr/1thtIynpFlWRoz5EmVlZWVu95rL85WaIPQs/Z5AOBEin75VZfHXq5Hnxh9yrrlH32sL7/YoAbhDcod+zzvcw0ZOFQJbRI0P3OeFrw9X3cl91KtWvwqgfNPrSr83/mMRAO/a9dd30rXXd/qpMdv6drO5+dBwwco6+9LteXbLWp53dWSpG63d7WPR7oj1Gfwfbr/rge1e2e+3FEN7WNr/71Oudn/0VPPPq6cf68z+jkA4FRuuOl63XDT9aes2b07XxP+MlEvvzJNQx8YVu74cxOe19333qW+A/5k77vk0kbGxwrg/HF+t1GAQaWlpVry3ocKviBYjWOiT1hTVPSr/rl4mSIvilCDyDB7f8G+Ar0w/iWNfmaknHWcVTVkAKiQI0eO6IlHn1Dvvvfpspjyj3T+vO9nffnFBoWEhOi+5D665cYO6ndff63PXV8NowXOPofDUWXb+eycbjR+/PFH9e3b95Q1xcXFOnDggM9WXFxcRSPE70H2p2uVdMMdSkz4P7335t814eXxctV3+dQsfucDJd1wh2674Q6tW5WrCdP+In9/f0mSZVl6dtwUdbvjVl3eLKY6PgIAnNKc1+bKz6+2ku+9+4THd+zYIUmaMW2mbv/j/+nlmRm6otkVGtj3fm3bur0qhwqgBjmnG42ff/5Zr7/++ilr0tPT5XK5fLaXn59ZRSPE78Efrr1K09+aqhfmPKdWba7R+EcnqODn/T417bverOlvvqTnXp2oixq5Nf7RdJUUl0iSFmW+r18O/6K7/nRnNYweAE7tq41f6c15b+nptD+f9K+rR45YkqQ7et6uHrffpiuaXaFHHh2pS6Mv0d/f+3tVDheoEo4q/N/5rFrnaCxevPiUx3/44YfTXmPMmDEaPny4zz5P6Y+/aVyAt8DAOrooyq2Lotxq2uIK9ekxQFmLluruvj3tmuC6wQquG6yLGl2kpi1idXu7Xvr3x6t0c5d2ysv5XJu//FrdEnr4XHdISqpu6XKzRj09XABQXf6Tu14///yzura/1d5XVlamyZOmaMEbb+rDj/6hBg2OPgrapEljn3OjG0dr1y5PlY4XQM1RrY1Gjx495HA4ZFnWSWtO9+ya0+mU0+n7zHvBIZ6Bx1lkWSotLT1NjVRacrRmyCOD1Gdwin1o356f9diDT+rx9Ed1RfPYszlSADitxKRuap0Q77PvgQFDlJjUTbf9X5IkyX2RWw3CG2jr1m0+ddu2btf1N7apsrECVeV8nztRVaq10WjYsKGmTZumHj16nPB4Xl6e4uLiqnZQ+F0p+qVIO3/caf/s2enR919/r7r16qruhfX01qy3ldA2XiFhITqw/4De/+s/tCd/r27qcIMkadeOXfpk6WeKS2ipCy90ae+efXp77t8UUCdA195wrSQpvGG4zz0DgwIlSe6LI9UgIkwAcLb9cvgXbd/+v7T/p59+0uZNX8vlqqeG7oa68MILfepr166t0LBQXRp9qaSjv3T17nufZmTM1OWxlyv2isv1/t8/0NYtW/XcC5Oq8JMAqEmqtdGIi4vTf/7zn5M2GqdLO4Df6puvvtUjg8bYP8+c/JokqWNiez302IP6ceuPWvbBv3Rgf6Hquuop9soYTX5tki5tcokkKcAZoA15G7Xwrb/r0IFDujD0QrVo2VwvzH5O9UMurI6PBADlbNz4lQb0GWj//PzEyZKk7j2665m0P1foGvfed49Kikv03MTnVVhYqMtjL9eM115WVKPyLycFarrzfe5EVXFY1fib/GeffabDhw+rS5cuJzx++PBhrVu3Tm3btq3Udbcd+s7E8ADgnBEe2PD0RQBQgwT6BVf3EE4qZ8/KKrvXtQ1uqLJ7VbVqTTRuvPHGUx4PDg6udJMBAAAA/BYkGmac08vbAgAAAKiZaDQAAAAAbw5H1W2VkJ6ermuvvVZ169ZVeHi4evTooa+//tqnxrIsjRs3Tm63W4GBgWrXrp02btzoU1NcXKyhQ4cqLCxMwcHBSkpKsl/MeUxBQYFSUlLs99SlpKRo//79lRovjQYAAABQA6xYsUJDhgxRdna2li1bpv/+97/q1KmTDh8+bNdMmjRJkydPVkZGhnJychQZGamOHTvq4MGDdk1qaqoWLlyozMxMrVy5UocOHVJiYqLKysrsmuTkZOXl5SkrK0tZWVnKy8tTSkqKKqNaJ4OfLUwGB3C+YTI4gPPNuTwZPHfv6iq7V1xYwhmfu2fPHoWHh2vFihW66aabZFmW3G63UlNTNXr0aElH04uIiAhNnDhRgwYNUmFhoRo0aKB58+apV69ekqSdO3cqKipKS5YsUefOnbVp0yY1a9ZM2dnZio8/+p6d7OxsJSQkaPPmzYqNrdh7wEg0AAAAgGpSXFysAwcO+GzFxcUVOrewsFCSFBISIknasmWLPB6POnXqZNc4nU61bdtWq1atkiTl5uaqtLTUp8btdqt58+Z2zerVq+VyuewmQ5Jat24tl8tl11QEjQYAAABQTdLT0+15EMe29PT0055nWZaGDx+uG264Qc2bN5ckeTweSVJERIRPbUREhH3M4/EoICBA9evXP2VNeLjvC4clKTw83K6piGpd3hYAAAA41zgqOUn7txgzZoyGDx/us8/pdJ72vAcffFBffPGFVq4s/86P48dvWdZpP9PxNSeqr8h1vJFoAAAAANXE6XSqXr16PtvpGo2hQ4dq8eLF+vjjj3XxxRfb+yMjIyWpXOqQn59vpxyRkZEqKSlRQUHBKWt2795d7r579uwpl5acCo0GAAAA4MVRhf+rDMuy9OCDD+q9997T8uXLFR0d7XM8OjpakZGRWrZsmb2vpKREK1asUJs2bSRJcXFx8vf396nZtWuXNmzYYNckJCSosLBQa9eutWvWrFmjwsJCu6YieHQKAAAAqAGGDBmiN998U3//+99Vt25dO7lwuVwKDAyUw+FQamqq0tLSFBMTo5iYGKWlpSkoKEjJycl2bb9+/TRixAiFhoYqJCREI0eOVIsWLdShQwdJUtOmTdWlSxcNGDBAM2fOlCQNHDhQiYmJFV5xSqLRAAAAAHxUNmmoKtOnT5cktWvXzmf/nDlz1KdPH0nSqFGjVFRUpMGDB6ugoEDx8fFaunSp6tata9dPmTJFtWvXVs+ePVVUVKT27dtr7ty58vPzs2sWLFigYcOG2atTJSUlKSMjo1Lj5T0aAFAD8B4NAOebc/k9Gnn71p6+yJCrQ6+rsntVNRINAAAAwEtVrjp1PmMyOAAAAADjSDQAAAAAL+fqHI2ahkQDAAAAgHEkGgAAAIAXEg0zSDQAAAAAGEeiAQAAAHhh1SkzSDQAAAAAGEeiAQAAAHhhjoYZJBoAAAAAjCPRAAAAALwwR8MMEg0AAAAAxtFoAAAAADCOR6cAAAAAL0wGN4NEAwAAAIBxJBoAAACAFxINM0g0AAAAABhHogEAAAB4YXlbM0g0AAAAABhHogEAAAB4YY6GGSQaAAAAAIwj0QAAAAC8kGiYQaIBAAAAwDgSDQAAAMALq06ZQaIBAAAAwDgSDQAAAMAHiYYJJBoAAAAAjCPRAAAAALwwR8MMEg0AAAAAxpFoAAAAAF54j4YZJBoAAAAAjKPRAAAAAGAcj04BAAAAXnh0ygwSDQAAAADGkWgAAAAAXlje1gwSDQAAAADGkWgAAAAAXpijYQaJBgAAAADjSDQAAAAALyQaZpBoAAAAADCORAMAAADwwqpTZpBoAAAAADCORAMAAADwwhwNM0g0AAAAABhHogEAAAB4YY6GGSQaAAAAAIwj0QAAAAC8MEfDDBINAAAAAMaRaAAAAAA+SDRMINEAAAAAYByJBgAAAOCFPMMMEg0AAAAAxtFoAAAAADCOR6cAAAAAL7ywzwwSDQAAAKAG+PTTT9W9e3e53W45HA4tWrTI57hlWRo3bpzcbrcCAwPVrl07bdy40aemuLhYQ4cOVVhYmIKDg5WUlKQdO3b41BQUFCglJUUul0sul0spKSnav39/pcdLowEAAAD4cFThVnGHDx/WH/7wB2VkZJzw+KRJkzR58mRlZGQoJydHkZGR6tixow4ePGjXpKamauHChcrMzNTKlSt16NAhJSYmqqyszK5JTk5WXl6esrKylJWVpby8PKWkpFRqrJLksCzLqvRZ57hth76r7iEAgFHhgQ2rewgAYFSgX3B1D+GkPEU7Tl9kSGTgxWd0nsPh0MKFC9WjRw9JR9MMt9ut1NRUjR49WtLR9CIiIkITJ07UoEGDVFhYqAYNGmjevHnq1auXJGnnzp2KiorSkiVL1LlzZ23atEnNmjVTdna24uPjJUnZ2dlKSEjQ5s2bFRsbW+ExkmgAAAAAXqoyzyguLtaBAwd8tuLi4kqPecuWLfJ4POrUqZO9z+l0qm3btlq1apUkKTc3V6WlpT41brdbzZs3t2tWr14tl8tlNxmS1Lp1a7lcLrumomg0AAAAgGqSnp5uz4U4tqWnp1f6Oh6PR5IUERHhsz8iIsI+5vF4FBAQoPr165+yJjw8vNz1w8PD7ZqKYtUpAAAAwEfVrTo1ZswYDR8+3Gef0+k84+sdv2KWZVmnXUXr+JoT1VfkOscj0QAAAACqidPpVL169Xy2M2k0IiMjJalc6pCfn2+nHJGRkSopKVFBQcEpa3bv3l3u+nv27CmXlpwOjQYAAADgxeFwVNlmSnR0tCIjI7Vs2TJ7X0lJiVasWKE2bdpIkuLi4uTv7+9Ts2vXLm3YsMGuSUhIUGFhodauXWvXrFmzRoWFhXZNRfHoFAAAAFADHDp0SN9997/VVbds2aK8vDyFhISoUaNGSk1NVVpammJiYhQTE6O0tDQFBQUpOTlZkuRyudSvXz+NGDFCoaGhCgkJ0ciRI9WiRQt16NBBktS0aVN16dJFAwYM0MyZMyVJAwcOVGJiYqVWnJJoNAAAAIAaYd26dbr55pvtn4/N7ejdu7fmzp2rUaNGqaioSIMHD1ZBQYHi4+O1dOlS1a1b1z5nypQpql27tnr27KmioiK1b99ec+fOlZ+fn12zYMECDRs2zF6dKikp6aTv7jgV3qMBADUA79EAcL45l9+jkf/rziq7V3gdd5Xdq6qRaAAAAABeHFW46tT5jMngAAAAAIwj0QAAAAC8kGiYQaIBAAAAwDgaDQAAAADG0WgAAAAAMI5GAwAAAIBxTAYHAAAAvDgcTAY3gUQDAAAAgHE0GgAAAACMo9EAAAAAYBxzNAAAAAAvvLDPDBINAAAAAMaRaAAAAAA+SDRMINEAAAAAYByJBgAAAOCFPMMMEg0AAAAAxpFoAAAAAF54M7gZJBoAAAAAjCPRAAAAAHyQaJhAogEAAADAOBINAAAAwAt5hhkkGgAAAACMI9EAAAAAfJBpmECiAQAAAMA4Eg0AAADAC+/RMINEAwAAAIBxNBoAAAAAjKPRAAAAAGAcjQYAAAAA45gMDgAAAHhxsLytESQaAAAAAIwj0QAAAAB8kGiYQKIBAAAAwDgSDQAAAMALeYYZJBoAAAAAjCPRAAAAALw4HGQaJpBoAAAAADCORAMAAADwQaJhAokGAAAAAONINAAAAAAv5BlmkGgAAAAAMI5EAwAAAPBBpmECiQYAAAAA40g0AAAAAC+8R8MMEg0AAAAAxtFoAAAAADCORgMAAACAcTQaAAAAAIxjMjgAAADgxcHytkaQaAAAAAAwzmFZllXdgwBqouLiYqWnp2vMmDFyOp3VPRwA+M34dw2ASTQawBk6cOCAXC6XCgsLVa9eveoeDgD8Zvy7BsAkHp0CAAAAYByNBgAAAADjaDQAAAAAGEejAZwhp9OpsWPHMmESwHmDf9cAmMRkcAAAAADGkWgAAAAAMI5GAwAAAIBxNBoAAAAAjKPRAAAAAGAcjQZwhl5++WVFR0erTp06iouL02effVbdQwKAM/Lpp5+qe/fucrvdcjgcWrRoUXUPCcB5gEYDOANvv/22UlNT9fjjj2v9+vW68cYb1bVrV23fvr26hwYAlXb48GH94Q9/UEZGRnUPBcB5hOVtgTMQHx+va665RtOnT7f3NW3aVD169FB6eno1jgwAfhuHw6GFCxeqR48e1T0UADUciQZQSSUlJcrNzVWnTp189nfq1EmrVq2qplEBAACcW2g0gErau3evysrKFBER4bM/IiJCHo+nmkYFAABwbqHRAM6Qw+Hw+dmyrHL7AAAAfq9oNIBKCgsLk5+fX7n0Ij8/v1zKAQAA8HtFowFUUkBAgOLi4rRs2TKf/cuWLVObNm2qaVQAAADnltrVPQCgJho+fLhSUlLUqlUrJSQk6JVXXtH27dt1//33V/fQAKDSDh06pO+++87+ecuWLcrLy1NISIgaNWpUjSMDUJOxvC1whl5++WVNmjRJu3btUvPmzTVlyhTddNNN1T0sAKi0Tz75RDfffHO5/b1799bcuXOrfkAAzgs0GgAAAACMY44GAAAAAONoNAAAAAAYR6MBAAAAwDgaDQAAAADG0WgAAAAAMI5GAwAAAIBxNBoAAAAAjKPRAAAAAGAcjQYAnGPGjRunq6++2v65T58+6tGjR5WPY+vWrXI4HMrLy6vyewMAaj4aDQCooD59+sjhcMjhcMjf31+NGzfWyJEjdfjw4bN63xdffFFz586tUC3NAQDgXFG7ugcAADVJly5dNGfOHJWWluqzzz5T//79dfjwYU2fPt2nrrS0VP7+/kbu6XK5jFwHAICqRKIBAJXgdDoVGRmpqKgoJScn65577tGiRYvsx51mz56txo0by+l0yrIsFRYWauDAgQoPD1e9evV0yy236PPPP/e55oQJExQREaG6deuqX79++vXXX32OH//o1JEjRzRx4kRddtllcjqdatSokf7yl79IkqKjoyVJLVu2lMPhULt27ezz5syZo6ZNm6pOnTq64oor9PLLL/vcZ+3atWrZsqXq1KmjVq1aaf369Qa/OQDA7w2JBgD8BoGBgSotLZUkfffdd3rnnXf07rvvys/PT5LUrVs3hYSEaMmSJXK5XJo5c6bat2+vb775RiEhIXrnnXc0duxYTZs2TTfeeKPmzZunl156SY0bNz7pPceMGaNXX31VU6ZM0Q033KBdu3Zp8+bNko42C9ddd50++ugjXXnllQoICJAkvfrqqxo7dqwyMjLUsmVLrV+/XgMGDFBwcLB69+6tw4cPKzExUbfccovmz5+vLVu26KGHHjrL3x4A4HxGowEAZ2jt2rV688031b59e0lSSUmJ5s2bpwYNGkiSli9fri+//FL5+flyOp2SpOeee06LFi3S3/72Nw0cOFAvvPCC+vbtq/79+0uSxo8fr48++qhcqnHMwYMH9eKLLyojI0O9e/eWJDVp0kQ33HCDJNn3Dg0NVWRkpH3eM888o+eff1633367pKPJx1dffaWZM2eqd+/eWrBggcrKyjR79mwFBQXpyiuv1I4dO/TAAw+Y/toAAL8TPDoFAJXwwQcf6IILLlCdOnWUkJCgm266SVOnTpUkXXLJJfYv+pKUm5urQ4cOKTQ0VBdccIG9bdmyRd9//70kadOmTUpISPC5x/E/e9u0aZOKi4vt5qYi9uzZox9//FH9+vXzGcf48eN9xvGHP/xBQUFBFRoHAACnQ6IBAJVw8803a/r06fL395fb7faZ8B0cHOxTe+TIETVs2FCffPJJuetceOGFZ3T/wMDASp9z5MgRSUcfn4qPj/c5duwRL8uyzmg8AACcDI0GAFRCcHCwLrvssgrVXnPNNfJ4PKpdu7YuvfTSE9Y0bdpU2dnZuu++++x92dnZJ71mTEyMAgMD9a9//ct+3MrbsTkZZWVl9r6IiAhddNFF+uGHH3TPPfec8LrNmjXTvHnzVFRUZDczpxoHAACnw6NTAHCWdOjQQQkJCerRo4f++c9/auvWrVq1apWeeOIJrVu3TpL00EMPafbs2Zo9e7a++eYbjR07Vhs3bjzpNevUqaPRo0dr1KhReuONN/T9998rOztbs2bNkiSFh4crMDBQWVlZ2r17twoLCyUdfQlgenq6XnzxRX3zzTf68ssvNWfOHE2ePFmSlJycrFq1aqlfv3766quvtGTJEj333HNn+RsCAJzPaDQA4CxxOBxasmSJbrrpJvXt21eXX3657rrrLm3dulURERGSpF69eumpp57S6NGjFRcXp23btp12AvaTTz6pESNG6KmnnlLTpk3Vq1cv5efnS5Jq166tl156STNnzpTb7dZtt90mSerfv79ee+01zZ07Vy1atFDbtm01d+5cezncCy64QO+//76++uortWzZUo8//rgmTpx4Fr8dAMD5zmHxYC4AAAAAw0g0AAAAABhHowEAAADAOBoNAAAAAMbRaAAAAAAwjkYDAAAAgHE0GgAAAACMo9EAAAAAYByNBgAAAADjaDQAAAAAGEejAQAAAMA4Gg0AAAAAxv0/S02tx4di6+0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred_cw)\n",
    "plt.figure(figsize=(10, 7))\n",
    "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Greens\")\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Actual\")\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances_cw = model2.feature_importances_\n",
    "features_cw = X_train.columns\n",
    "# sort values by importance\n",
    "features_importance_cw = pd.DataFrame(importances_cw, index=features_cw, columns=[\"importance\"]).sort_values(by=\"importance\", ascending=False)\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.barplot(x=features_importance_cw[\"importance\"], y=features_importance_cw.index)\n",
    "plt.title(\"Feature Importance\")\n",
    "plt.ylabel(\"Feature\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Undersampling did not improve the model's performance. However, using synthetic bagging techniques like SMOTE (Synthetic Minority Over-sampling Technique) may help enhance the model's performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest with SMOTE "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    34002\n",
       "1    34002\n",
       "Name: booking_complete, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "smote=SMOTE(sampling_strategy='minority') \n",
    "X_train_s,y_train_s=smote.fit_resample(X_train,y_train )\n",
    "y_train_s.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(random_state=42)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3 = RandomForestClassifier(random_state=42)\n",
    "model3.fit(X_train_s,y_train_s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC-ROC: 0.6250015860931354\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.90      0.89      8520\n",
      "           1       0.37      0.35      0.36      1480\n",
      "\n",
      "    accuracy                           0.82     10000\n",
      "   macro avg       0.63      0.63      0.63     10000\n",
      "weighted avg       0.81      0.82      0.81     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred_smote = model3.predict(X_test)\n",
    "print(\"AUC-ROC:\", roc_auc_score(y_test, y_pred_smote))\n",
    "print(classification_report(y_test, y_pred_smote))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 7))\n",
    "cm = confusion_matrix(y_test, y_pred_smote)\n",
    "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Greens\")\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Actual\")\n",
    "plt.title(\"Confusion Matrix of SMOTE\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances_smote = model3.feature_importances_\n",
    "features_smote = X_train.columns\n",
    "# sort values by importance\n",
    "features_importance_smote = pd.DataFrame(importances_smote, index=features_smote, columns=[\"importance\"]).sort_values(by=\"importance\", ascending=False)\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.barplot(x=features_importance_smote[\"importance\"], y=features_importance_smote.index)\n",
    "plt.title(\"Feature Importance\")\n",
    "plt.ylabel(\"Feature\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With SMOTE, the model demonstrates proficiency in identifying the majority class (class 0), but it faces challenges in effectively discriminating between the positive (class 1) and negative (class 0) classes. Change in model to XGB classifier and validation test may improve further. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGB Classifier with Validation and Hypertuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 729 candidates, totalling 3645 fits\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.9s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   1.0s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.8s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.9s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   1.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   1.0s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.8s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.7, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   1.0s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   1.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   1.8s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.9s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.8s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.8s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.9s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.8s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.7s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=0.8, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.9s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.7s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.7s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.7s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.8s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=0, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.7s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.7s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.7s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.7s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=1, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.6s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.01, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.1, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=100, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=3, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=5, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=50, subsample=1.0; total time=   0.1s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=100, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.2s\n",
      "[CV] END colsample_bytree=1.0, gamma=5, learning_rate=0.2, max_depth=7, n_estimators=200, subsample=1.0; total time=   0.2s\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "param_grid = {'n_estimators': [50, 100, 200],\n",
    "    'learning_rate': [0.01, 0.1, 0.2],\n",
    "    'max_depth': [3, 5, 7],\n",
    "    'colsample_bytree': [0.7, 0.8, 1.0],\n",
    "    'subsample': [0.7, 0.8, 1.0], 'gamma': [0, 1, 5]}\n",
    "# Create a based model\n",
    "xgb = XGBClassifier(random_state=42)\n",
    "# Instantiate the grid search model\n",
    "grid_search = GridSearchCV(estimator = xgb, param_grid = param_grid, \n",
    "                          cv = 5, scoring='roc_auc', verbose = 2)\n",
    "grid_search.fit(X_train_s, y_train_s)\n",
    "best_xgb_model = grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters found:  {'colsample_bytree': 1.0, 'gamma': 0, 'learning_rate': 0.2, 'max_depth': 7, 'n_estimators': 200, 'subsample': 0.7}\n",
      "AUC-ROC: 0.6356839233599798\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.87      0.88      8520\n",
      "           1       0.34      0.41      0.37      1480\n",
      "\n",
      "    accuracy                           0.80     10000\n",
      "   macro avg       0.62      0.64      0.63     10000\n",
      "weighted avg       0.81      0.80      0.80     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Best parameters found: \", grid_search.best_params_)\n",
    "y_pred_xgb = best_xgb_model.predict(X_test)\n",
    "print(\"AUC-ROC:\", roc_auc_score(y_test, y_pred_xgb))\n",
    "print(classification_report(y_test, y_pred_xgb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 7))\n",
    "cm = confusion_matrix(y_test, y_pred_xgb)\n",
    "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Greens\")\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Actual\")\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "importances_xgb = best_xgb_model.feature_importances_\n",
    "features_xgb = X_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features_importance_xgb = pd.DataFrame(importances_xgb, index=features_xgb, columns=[\"importance\"]).sort_values(by=\"importance\", ascending=False)\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.barplot(x=features_importance_xgb[\"importance\"], y=features_importance_xgb.index, palette=\"coolwarm\")\n",
    "plt.title(\"Feature Importance\")\n",
    "plt.ylabel(\"Feature\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on both accuracy and AUC-ROC score, Model 4 performs the best overall among the three models you've shared. It has the highest AUC-ROC score and a reasonably good accuracy compared to the other models.\n",
    "\n",
    "Given the significant class imbalance in the dataset, each model faced notable difficulties in accurately predicting instances belonging to the positive class. This imbalance skewed predictions towards the majority class, affecting the models' ability to correctly identify instances where the outcome was positive. This observation was consistent across all models evaluated, highlighting the ongoing challenge posed by class imbalance in predictive modeling tasks. This model can be improved by further addressing class imbalance, adding more customer-centric features into the model."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.13 ('venv': venv)",
   "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": "3.11.4"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "4f7924c4c56b083e0e50eadfe7ef592a7a8ef70df33a0047f82280e6be1afe15"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}