capstone_torrente_castillo_lachman.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Capstone project : Bicing dock availability prediction\n",
        "\n",
        "### Authors: Anderson Castillo, Rohit Lachman, Pau Torrente\n",
        "\n",
        "In this notebook we present the code used to do the last step of data preprocessing (all the merging of the data obtained from Barcelona OpenData has been done using tableau) and to train 2 different models to predict the availability of free docks in Barcelona's public bike sharing network. Finally, we have also created a few heatmaps that display the usage of the network in the city, and possible placements for new stations.\n",
        "\n",
        "# Warning\n",
        "The following code, aswell as all the folder containing the data needed to run this notebook, have been built on top of Google Drive and Colab, due to the huge dataset sizes and the need for GPU's. We suggest copying the project's folder (https://drive.google.com/drive/folders/1ZIY2ZMhsCITuSFC1bnIDJrbZh-sS63HP?usp=drive_link) and making shure that it is saved in your drive as /content/drive/MyDrive/CapstoneProject"
      ],
      "metadata": {
        "id": "w0zwpnQihosm"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2sOZGnPX674P",
        "outputId": "ff9095a7-5028-41b4-8531-47835236300c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "# google drive mounting for running on colab --> All the code contains drive folders\n",
        "# that might have to be adjusted, depending on the user"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u5qLyVzX7IeH",
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        "outputId": "003839ab-f8c8-4e0b-f358-796641d7dfa8"
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            "Successfully installed dask-glm-0.2.0 dask_ml-2023.3.24\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# pip install required for the ml part of dask\n",
        "! pip install dask_ml"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data importing and initial cleaning\n",
        "\n",
        "The initial data merging and reshaping has been done using tableau due to RAM limitations. In the following code cell the data is imported and cleaned with the following criteria:\n",
        "- We will only consider location of the station, timestamp information, and previous % of occupation.\n",
        "- We ignore 2020 data to not work with the data most affected by covid\n",
        "- We use the all data from 2019 through 2022 as training data and the first two months of 2023 as validation data. March 2023 will be used as test data, checked in the kaggle competition\n",
        "- We only consider data that corresponds to active station (marked with status = 1) and with an availability percentage between 0 and 1 (many register had higer % and we have consdiered them non-valid/corrupted data)\n",
        "\n",
        "Apart from the data from the from BCNOpenData we have also gathered information about the weather using the meteo.cat api. The script used, that can be found in the weather folder on this repo alongside the csv file that it generates, gathers the weather data regarding temperature and precipitation in intervals of 30 minutes from the station located in Plaça Universitat (we chose this station due to its location in the center of the city, which we felt like would give a pretty good estimate of the overall weather in the city). The dataframe that this script generates is also imported."
      ],
      "metadata": {
        "id": "-oJdMNZ4EQFt"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T5WnTh7r6e2-"
      },
      "outputs": [],
      "source": [
        "import dask.dataframe as dd\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.preprocessing import FunctionTransformer\n",
        "from dask_ml.preprocessing import StandardScaler, MinMaxScaler\n",
        "from dask_ml.impute import SimpleImputer\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.base import BaseEstimator, TransformerMixin\n",
        "from IPython.display import Markdown\n",
        "from xgboost import plot_importance\n",
        "from matplotlib import pyplot\n",
        "import json\n",
        "\n",
        "\n",
        "\n",
        "station_dataframe = dd.read_csv('/content/drive/MyDrive/CapstoneProject/merged_data/data_bicing_joined_HX.csv', assume_missing=True, delimiter=';')\n",
        "\n",
        "weather_dataframe = dd.read_csv('/content/drive/MyDrive/CapstoneProject/weather/weather.csv', assume_missing=True, delimiter=',')\n",
        "\n",
        "####################################################################################################\n",
        "\n",
        "station_dataframe = station_dataframe.loc[station_dataframe['status'] == 'IN_SERVICE']\n",
        "\n",
        "##################################################################################################\n",
        "\n",
        "train_df = station_dataframe.loc[(station_dataframe['year'] != 2020) & (station_dataframe['year'] != 2023)]\n",
        "\n",
        "train_df = train_df[['station_id', 'lat', 'lon', 'year', 'month', 'day', 'hour', '% Docks Availlable',  '% Docks Available H-4','% Docks Available H-3', '% Docks Available H-2', '% Docks Available H-1']]\n",
        "train_df = train_df.rename(columns={'% Docks Availlable': 'percentage'})\n",
        "for i in range(1, 5):\n",
        "    train_df = train_df.rename(columns={f'% Docks Available H-{i}': f'ctx-{i}'})\n",
        "\n",
        "# Print the head of the updated DataFrame\n",
        "train_df = train_df[['station_id', 'lat', 'lon', 'year', 'month', 'day', 'hour', 'ctx-4', 'ctx-3', 'ctx-2',\t'ctx-1', 'percentage']]\n",
        "\n",
        "train_df = train_df.reset_index()\n",
        "\n",
        "##################################################################################################\n",
        "\n",
        "validation_df = station_dataframe.loc[(station_dataframe['year'] == 2023)]\n",
        "\n",
        "validation_df = validation_df[['station_id', 'lat', 'lon', 'year', 'month', 'day', 'hour', '% Docks Availlable',  '% Docks Available H-4','% Docks Available H-3', '% Docks Available H-2', '% Docks Available H-1']]\n",
        "validation_df = validation_df.rename(columns={'% Docks Availlable': 'percentage'})\n",
        "for i in range(1, 5):\n",
        "    validation_df = validation_df.rename(columns={f'% Docks Available H-{i}': f'ctx-{i}'})\n",
        "\n",
        "# Print the head of the updated DataFrame\n",
        "validation_df = validation_df[['station_id', 'lat', 'lon', 'year', 'month', 'day', 'hour', 'ctx-4', 'ctx-3', 'ctx-2',\t'ctx-1', 'percentage']]\n",
        "\n",
        "validation_df = validation_df.reset_index()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zEr_rYe7IO-H",
        "outputId": "9b2d77a1-8ebb-4bbf-e87a-b5d599cf423f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Count of values > 1:\n",
            "ctx-4:  60482\n",
            "ctx-3:  60509\n",
            "ctx-2:  60539\n",
            "ctx-1:  60558\n",
            "percentage:  60615\n"
          ]
        }
      ],
      "source": [
        "# We have found in previous test that our data contains many registers where the\n",
        "# occupation percentage is over 1. Let's see how many do we have in our 5 percentage columns\n",
        "\n",
        "count_ctx_4 = train_df['ctx-4'].apply(lambda x: x > 1, meta=('ctx-4', 'boolean')).sum().compute()\n",
        "count_ctx_3 = train_df['ctx-3'].apply(lambda x: x > 1, meta=('ctx-3', 'boolean')).sum().compute()\n",
        "count_ctx_2 = train_df['ctx-2'].apply(lambda x: x > 1, meta=('ctx-2', 'boolean')).sum().compute()\n",
        "count_ctx_1 = train_df['ctx-1'].apply(lambda x: x > 1, meta=('ctx-1', 'boolean')).sum().compute()\n",
        "count_percentage = train_df['percentage'].apply(lambda x: x > 1, meta=('percentage', 'boolean')).sum().compute()\n",
        "\n",
        "print(\"Count of values > 1:\")\n",
        "print(\"ctx-4: \", count_ctx_4)\n",
        "print(\"ctx-3: \", count_ctx_3)\n",
        "print(\"ctx-2: \", count_ctx_2)\n",
        "print(\"ctx-1: \", count_ctx_1)\n",
        "print(\"percentage: \", count_percentage)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EbjLl0f3NWhW"
      },
      "outputs": [],
      "source": [
        "# There are many X%>100% --> We remove all of them, also from the validation set\n",
        "\n",
        "train_df = train_df[(train_df['ctx-4'] <= 1) & (train_df['ctx-3'] <= 1) & (train_df['ctx-2'] <= 1) & (train_df['ctx-1'] <= 1) & (train_df['percentage'] <= 1)]\n",
        "validation_df = validation_df[(validation_df['ctx-4'] <= 1) & (validation_df['ctx-3'] <= 1) & (validation_df['ctx-2'] <= 1) & (validation_df['ctx-1'] <= 1) & (validation_df['percentage'] <= 1)]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We also import the submission dataframe from the competition's kaggle webpage. To be able to work with the location of the stations, and since this dataframe initially does not contain information about the latitude and the longitude of the stations, we also import the information dataframe from february (we will not use any data from march 2023) to merge the latitude and longitude onto the submission dataframe using the station id"
      ],
      "metadata": {
        "id": "OB5bVkcwH1qs"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KfFdtx8C6e3A"
      },
      "outputs": [],
      "source": [
        "# We now import the submission dataframe to be predicted\n",
        "\n",
        "data_2_predict = dd.read_csv('/content/drive/MyDrive/CapstoneProject/merged_data/metadata_sample_submission.csv', delimiter=',')\n",
        "\n",
        "data_2_predict.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ogHxEwH86rUQ"
      },
      "outputs": [],
      "source": [
        "# Import febrary 2023 station info to get the lat and lon of the stations --> March 2023 info could be used as well\n",
        "# but to not even touch the data that we aim to predict and submit, we will take the previous month\n",
        "\n",
        "\n",
        "february2023_info = pd.read_csv('/content/drive/MyDrive/CapstoneProject/data_bicing/2023_02_Febrer_BicingNou_INFORMACIO.csv', delimiter=',')\n",
        "\n",
        "# From the info dataset, we retrieve the unique the location of each unique station\n",
        "final_loc = february2023_info.drop_duplicates(subset=['station_id'], keep='last')[['station_id', 'lat', 'lon']]"
      ]
    },
    {
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        "id": "COGEvPuP6rUR",
        "outputId": "77e1dc5a-8f1c-4fa5-89ab-4f569f2f434a"
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          },
          "metadata": {},
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      ],
      "source": [
        "final_loc.head() # this dataframe containing the lat and lon for each station will be used to perform the merge"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We finally do some final processing on the weather dataframe. The api returns data in intervals of 30 minutes, and since we will work in hourly intervals, we sum precipitation over two registers and average the temperature. We also modify the time format to be able to merge with the stations dataframe."
      ],
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              "    timestamp  mm_precip  temperature            datetime\n",
              "0  1546297200        0.0         9.00 2018-12-31 23:00:00\n",
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      "source": [
        "def weather_prep(weather_df:dd) -> dd:\n",
        "\n",
        "    # weather dataframe preparator to leave it in the desired format to merge with the station dataframe\n",
        "    # We convert the half hourly data to hourly data by averaging temperature and summing precipitation\n",
        "    # We also add a column with the datetime in order to merge the dataframes\n",
        "\n",
        "    weather = weather_df.copy()\n",
        "\n",
        "    weather = weather.groupby(weather.index//2).mean()\n",
        "\n",
        "    weather['mm_precip'] = weather['mm_precip']*2\n",
        "    weather['timestamp'] = (weather['timestamp']-900).astype(int)\n",
        "    weather['datetime'] = weather['timestamp'].map(lambda x: pd.to_datetime(x, unit='s'))\n",
        "\n",
        "\n",
        "    weather['timestamp'] = weather['timestamp'].astype(int)\n",
        "\n",
        "    weather['datetime'] = weather['timestamp'].map(lambda x: pd.to_datetime(x, unit='s'))\n",
        "\n",
        "    weather = weather.drop_duplicates(subset=['timestamp'])\n",
        "\n",
        "    return weather\n",
        "\n",
        "\n",
        "weather_prepped = weather_prep(weather_dataframe)\n",
        "\n",
        "weather_prepped.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Processing pipelines\n",
        "\n",
        "Apart from the preprocessing done using tableu to create the initial dataframe, everything else will be done using sklearn pipelines. We will have 3 different pipelines to prepare all the data, that work in the following way:\n",
        "- train_preparator: used on training and one set of validation data. It adds the weather information, selects only register with hours 0, 4, 9, 14, 19, 23 to not have overlap between data rows and remove any possibilty of the model seeing % that will later predict and lastly it codifies the time data using trigonometric functions to account for the periodicity (years 2019 to 2023 are normalized to 0-1). In this pieline we also mark the data depending on if they correspond to a weekend day (friday, saturday and sunday) and lastly we remove the station id.\n",
        "- validation preparator: same procedure as train_preparator but not filtering hours, to create a dataframe as similar as possible to the submission.\n",
        "- submission preparator: similar procedure to validation_preparator but also adding the location of the stations into the submission dataframe.\n",
        "\n",
        "After we apply these pipelines, all the data is passed through another pipeline that fills nan's and scales all the data regarding weather and location (the rest already belong to intervals between 0 and 1 or between .1 and +1), that is fitted only with the training dataset."
      ],
      "metadata": {
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        "\n",
        "def weather_merge(weather_df: dd, station_data: dd) -> dd:\n",
        "    weather = weather_df.copy().compute()\n",
        "    stations = station_data.copy().compute()\n",
        "\n",
        "    if 'year' not in stations.columns:  # the submission df does not contain year, and we need will use that to predict\n",
        "        stations['year'] = 2023\n",
        "\n",
        "    stations[['year', 'month', 'day', 'hour']] = stations[['year', 'month', 'day', 'hour']].astype(int)\n",
        "\n",
        "    stations['datetime'] = pd.to_datetime(stations['year'].astype(str) + '-' +\n",
        "                                        stations['month'].astype(str) + '-' +\n",
        "                                        stations['day'].astype(str) + ' ' +\n",
        "                                        stations['hour'].astype(str) + ':00:00')\n",
        "\n",
        "    weather['datetime'] = pd.to_datetime(weather['datetime'])\n",
        "\n",
        "    # Sorting dataframes by datetime for the asof merge\n",
        "    weather = weather.sort_values('datetime')\n",
        "    stations = stations.sort_values('datetime')\n",
        "\n",
        "    # Merge for the current datetime\n",
        "    stations = pd.merge_asof(stations, weather[['datetime', 'temperature', 'mm_precip']], left_on='datetime', right_on='datetime', direction='backward')\n",
        "\n",
        "    # Iterate to merge for the preceding datetimes\n",
        "    for i in range(1, 5):\n",
        "        df_weather_shifted = weather.copy()\n",
        "        df_weather_shifted['datetime'] += pd.Timedelta(hours=i) # shift weather data i hours forward\n",
        "        stations = pd.merge_asof(stations, df_weather_shifted[['datetime', 'temperature', 'mm_precip']], left_on='datetime', right_on='datetime', direction='backward', suffixes=('', f'-{i}'))\n",
        "\n",
        "    return dd.from_pandas(stations, npartitions=5)  # Convert back to Dask DataFrame\n",
        "\n",
        "\n",
        "####################################################################################################\n",
        "\n",
        "\n",
        "class weather_merge_transformer(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self, weather_df:dd, merging_function:callable=weather_merge):\n",
        "        self.weather_df = weather_df\n",
        "        self.func = merging_function\n",
        "\n",
        "    def fit(self, X, y=None):\n",
        "        return self\n",
        "\n",
        "    def transform(self, X):\n",
        "        return self.func(self.weather_df, X)\n",
        "\n",
        "####################################################################################################\n",
        "####################################################################################################\n",
        "\n",
        "\n",
        "def extra_time_info(df:dd) -> dd:\n",
        "\n",
        "    def is_weekend(day_of_week):\n",
        "        return 1 if day_of_week >= 5 else 0\n",
        "\n",
        "    # Create a column to distinguish id ii's a weekend or not\n",
        "    df['is_weekend'] = df['datetime'].dt.dayofweek.map(is_weekend, meta=('is_weekend', 'int64'))\n",
        "\n",
        "    # Since we will only be working with 5 hours in the training dataset, we add 5 binary columns to indicate in which part of the day we are\n",
        "    # to sort of solve the problem of the missing hours for training\n",
        "\n",
        "    df['timeframe1'] = df['datetime'].dt.hour.map(lambda x: 1 if x <= 4 else 0, meta=('timeframe1', 'int64'))\n",
        "    df['timeframe2'] = df['datetime'].dt.hour.map(lambda x: 1 if x >= 5 and x <=9 else 0, meta=('timeframe1', 'int64'))\n",
        "    df['timeframe3'] = df['datetime'].dt.hour.map(lambda x: 1 if x >= 10 and x <=14 else 0, meta=('timeframe1', 'int64'))\n",
        "    df['timeframe4'] = df['datetime'].dt.hour.map(lambda x: 1 if x >= 15 and x <=19 else 0, meta=('timeframe1', 'int64'))\n",
        "    df['timeframe5'] = df['datetime'].dt.hour.map(lambda x: 1 if x >= 20 else 0, meta=('timeframe1', 'int64'))\n",
        "\n",
        "    df = df.drop(['datetime'], axis = 1)\n",
        "\n",
        "    return df\n",
        "\n",
        "####################################################################################################\n",
        "\n",
        "\n",
        "def station_loc(id_lat_lon:dd, df:dd) -> dd: #this should be applied for the 2023 march dataset to predict from station_id-location paris from february 2023\n",
        "\n",
        "    # station location adder from the february 2023 dataset to substitute station_id for locations\n",
        "\n",
        "    assert all(item in list(id_lat_lon.columns) for item in ['station_id', 'lat', 'lon']), 'id_lat_lon must contain station_id, lat and lon columns'\n",
        "    id_locator = id_lat_lon.copy()\n",
        "    data = df.copy()\n",
        "    id_locator = id_locator.drop_duplicates(subset=['station_id'])\n",
        "\n",
        "    data = data.merge(id_locator[['station_id', 'lat', 'lon']], on='station_id', how='left')\n",
        "\n",
        "    return data\n",
        "\n",
        "class station_loc_transformer(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self, id_lat_lon:dd, merging_function:callable=station_loc):\n",
        "        self.id_lat_lon = id_lat_lon\n",
        "        self.func = merging_function\n",
        "\n",
        "    def fit(self, X, y=None):\n",
        "        return self\n",
        "\n",
        "    def transform(self, X):\n",
        "        return self.func(self.id_lat_lon, X)\n",
        "\n",
        "####################################################################################################\n",
        "\n",
        "\n",
        "\n",
        "def hour_selector(df:dd, hour_list:list) -> dd:\n",
        "\n",
        "    # Hour selector function to select only the hours we want to train on\n",
        "    # in our case we will only train on 5 hours of the day, but a more complicated function\n",
        "    # that would cover all hours could also be constructed\n",
        "\n",
        "    data = df.copy()\n",
        "    data = data.loc[data['hour'].isin(hour_list)]\n",
        "    return data\n",
        "\n",
        "class hour_selector_transformer(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self, hour_list:list):\n",
        "        self.hour_list = hour_list\n",
        "\n",
        "    def fit(self, X, y=None):\n",
        "        return self\n",
        "\n",
        "    def transform(self, X):\n",
        "        return hour_selector(X, self.hour_list)\n",
        "\n",
        "####################################################################################################\n",
        "\n",
        "\n",
        "def time_norm(df:dd, columns:list) -> dd:\n",
        "    # Time normalizer function to normalize the time columns to a 0-1 scale with periodicity\n",
        "    # This is done by applying a sin and cos transformation to the columns\n",
        "\n",
        "    data = df.copy()\n",
        "    for col in columns:\n",
        "        data['cos_'+col] = np.cos(2*np.pi*data[col]/data[col].max())\n",
        "        data['sin_'+col] = np.sin(2*np.pi*data[col]/data[col].max())\n",
        "        data = data.drop([col], axis=1)\n",
        "\n",
        "    data['year_normed'] = (data['year']-2019)/4\n",
        "    data = data.drop(['year'], axis = 1)\n",
        "\n",
        "    return data\n",
        "\n",
        "class time_norm_transformer(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self, columns: list, norm_function: callable=time_norm):\n",
        "        self.columns = columns\n",
        "        self.func = norm_function\n",
        "\n",
        "    def fit(self, X, y=None):\n",
        "        return self\n",
        "\n",
        "    def transform(self, X):\n",
        "        return self.func(X, self.columns)\n",
        "\n",
        "####################################################################################################\n",
        "\n",
        "class station_id_dropper(BaseEstimator, TransformerMixin):\n",
        "\n",
        "    # Station id dropper function to drop the station_id column from the dataset at the end of the pipeline\n",
        "    # prediction will be done on the location of the stations, not on the id\n",
        "\n",
        "    def fit(self, X, y=None):\n",
        "        return self\n",
        "    def transform(self, X):\n",
        "        return X.drop(['station_id'], axis=1)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
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      "metadata": {
        "id": "_8l_pNkx6e3C"
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      "source": [
        "# Creation of the 3 pipelines:\n",
        "\n",
        "train_preparator = Pipeline([\n",
        "    ('weather_merge', weather_merge_transformer(weather_df=weather_prepped)),\n",
        "    ('extra_time_info', FunctionTransformer(func=extra_time_info)),\n",
        "    ('hour_selector', hour_selector_transformer(hour_list=[4, 9, 14, 19, 23])), # This obviously restricts the training set, but it prevents\n",
        "                                                                                # the model from seeing data labels before predicting them afterwards\n",
        "                                                                                # It also prevents running out of RAM. A better approach would need to sample all\n",
        "                                                                                # all hours, but it is quite more complicated than this\n",
        "\n",
        "    ('time_normalization', time_norm_transformer(columns=['month', 'day', 'hour'])),\n",
        "    ('station_id_dropper', station_id_dropper())\n",
        "])\n",
        "\n",
        "val_preparator = Pipeline([\n",
        "    ('weather_merge', weather_merge_transformer(weather_df=weather_prepped)),\n",
        "    ('extra_time_info', FunctionTransformer(func=extra_time_info)),\n",
        "    ('hour_selector', hour_selector_transformer(hour_list=[i for i in range(24)])), # For the validation part, we will leave one dataset with all the hours and another one\n",
        "                                                                                    # with just the four that appear in the training set to evaluate how this decision affects results\n",
        "    ('time_normalization', time_norm_transformer(columns=['month', 'day', 'hour'])),\n",
        "    ('station_id_dropper', station_id_dropper())\n",
        "])\n",
        "\n",
        "submission_preparator = Pipeline([\n",
        "    ('weather_merge', weather_merge_transformer(weather_df=weather_prepped)),\n",
        "    ('extra_time_info', FunctionTransformer(func=extra_time_info)),\n",
        "    ('hour_selector', hour_selector_transformer(hour_list=[i for i in range(24)])),\n",
        "    ('time_normalization', time_norm_transformer(columns=['month', 'day', 'hour'])),\n",
        "    ('station_loc', station_loc_transformer(id_lat_lon=final_loc)),\n",
        "    ('station_id_dropper', station_id_dropper())\n",
        "])\n",
        "\n",
        "\n",
        "\n",
        "total_columns = ['index','lat', 'lon', 'ctx-4', 'ctx-3', 'ctx-2', 'ctx-1',\n",
        "       'temperature', 'mm_precip', 'temperature-1', 'mm_precip-1',\n",
        "       'temperature-2', 'mm_precip-2', 'temperature-3', 'mm_precip-3',\n",
        "       'temperature-4', 'mm_precip-4', 'is_weekend', 'timeframe1',\n",
        "       'timeframe2', 'timeframe3', 'timeframe4', 'timeframe5', 'cos_month',\n",
        "       'sin_month', 'cos_day', 'sin_day', 'cos_hour', 'sin_hour',\n",
        "       'year_normed']\n",
        "\n",
        "\n",
        "to_fill = ['index', 'ctx-4', 'ctx-3', 'ctx-2', 'ctx-1', 'is_weekend', 'timeframe1',\n",
        "                   'timeframe2', 'timeframe3', 'timeframe4', 'timeframe5', 'cos_month',\n",
        "                   'sin_month', 'cos_day', 'sin_day', 'cos_hour', 'sin_hour',\n",
        "                   'year_normed']\n",
        "\n",
        "\n",
        "to_fill_and_scale = ['lat', 'lon', 'temperature', 'mm_precip', 'temperature-1', 'mm_precip-1',\n",
        "       'temperature-2', 'mm_precip-2', 'temperature-3', 'mm_precip-3',\n",
        "       'temperature-4', 'mm_precip-4']\n",
        "\n",
        "\n",
        "filler = Pipeline([\n",
        "    ('nan_filler', SimpleImputer(missing_values = np.nan, strategy='median'))\n",
        "])\n",
        "\n",
        "filler_scaler= Pipeline([\n",
        "    ('nan_filler', SimpleImputer(missing_values = np.nan, strategy='median')),\n",
        "    ('min_max', MinMaxScaler(feature_range = (-1,1)))\n",
        "])\n",
        "\n",
        "scaling_pipeline = ColumnTransformer([\n",
        "    ('fill', filler, to_fill),\n",
        "    ('fill_scale', filler_scaler, to_fill_and_scale)\n",
        "])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Prepare all data and save it accordingly\n",
        "--> The following code takes a while to run and the results of it have been saved in the prepped_data folder, so there is no need to run this code again. We will just load it from our folder to work with the models\n"
      ],
      "metadata": {
        "id": "zOjhcbj01ePF"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9JJY9DhM6rUS"
      },
      "outputs": [],
      "source": [
        "# Complete the data for training/validation\n",
        "\n",
        "train_prepared = train_preparator.transform(train_df)\n",
        "\n",
        "train_prepared.compute().to_csv('/content/drive/MyDrive/CapstoneProject/prepped_data/full_training.csv', index=False)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "validation_prepared = train_preparator.transform(validation_df)  # the set with only the 5 hours can be transformed with the same transformer as the train set\n",
        "\n",
        "validation_prepared.compute().to_csv('/content/drive/MyDrive/CapstoneProject/prepped_data/full_validation.csv', index=False)\n",
        "\n",
        "validation_prepared24h = val_preparator.transform(validation_df)\n",
        "\n",
        "validation_prepared24h.compute().to_csv('/content/drive/MyDrive/CapstoneProject/prepped_data/full_validation24h.csv', index=False)"
      ],
      "metadata": {
        "id": "JMuT1TI8EV6c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D7ns251y6rUS"
      },
      "outputs": [],
      "source": [
        "# Apply the scaling pipelines to both sets of data and them into csv files\n",
        "\n",
        "y_train = train_prepared['percentage'].values.compute()\n",
        "ydf_train = pd.DataFrame({'percentage': y_train})\n",
        "ydf_train.to_csv('/content/drive/MyDrive/CapstoneProject/prepped_data/y_train.csv', index=False)\n",
        "\n",
        "X_train = pd.DataFrame(scaling_pipeline.fit_transform(train_prepared), columns= to_fill + to_fill_and_scale)\n",
        "X_train.to_csv('/content/drive/MyDrive/CapstoneProject/prepped_data/X_train.csv', index=False)\n",
        "\n",
        "#########################################################################################################\n",
        "\n",
        "y_val = validation_prepared['percentage'].values.compute()\n",
        "ydf_val = pd.DataFrame({'percentage': y_val})\n",
        "ydf_val.to_csv('/content/drive/MyDrive/CapstoneProject/prepped_data/y_val.csv', index=False)\n",
        "\n",
        "X_val = pd.DataFrame(scaling_pipeline.transform(validation_prepared), columns= to_fill + to_fill_and_scale)\n",
        "X_val.to_csv('/content/drive/MyDrive/CapstoneProject/prepped_data/X_val.csv', index=False)\n",
        "\n",
        "#########################################################################################################\n",
        "\n",
        "y_val24h = validation_prepared['percentage'].values.compute()\n",
        "ydf_val24h = pd.DataFrame({'percentage': y_val24h})\n",
        "ydf_val24h.to_csv('/content/drive/MyDrive/CapstoneProject/prepped_data/y_val24.csv', index=False)\n",
        "\n",
        "X_val24h = pd.DataFrame(scaling_pipeline.transform(validation_prepared24h), columns= to_fill + to_fill_and_scale)\n",
        "X_val24h.to_csv('/content/drive/MyDrive/CapstoneProject/prepped_data/X_val24.csv', index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X8OQrDwS6rUS"
      },
      "outputs": [],
      "source": [
        "# Complete the data for submission and save it to a csv file\n",
        "\n",
        "data_2_predict_prepared = submission_preparator.fit_transform(data_2_predict)\n",
        "data_2_predict_prepared = data_2_predict_prepared.sort_values(by = 'index')\n",
        "data_2_predict_prepared.compute().to_csv('/content/drive/MyDrive/CapstoneProject/prepped_data/full_submission.csv', index=False)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1pvWl_iA6rUT"
      },
      "outputs": [],
      "source": [
        "# Apply the pipeline to the submission data and save it to a csv file\n",
        "\n",
        "X_submission = pd.DataFrame(scaling_pipeline.transform(data_2_predict_prepared), columns= to_fill + to_fill_and_scale)\n",
        "X_submission.to_csv('/content/drive/MyDrive/CapstoneProject/prepped_data/X_submission.csv', index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load data that has been processed and saved\n"
      ],
      "metadata": {
        "id": "aq6BggtO130r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_submission = pd.read_csv('/content/drive/MyDrive/CapstoneProject/prepped_data/X_submission.csv')\n",
        "X_train = pd.read_csv('/content/drive/MyDrive/CapstoneProject/prepped_data/X_train.csv', delimiter = ',').drop('index', axis = 1)\n",
        "y_train = pd.read_csv('/content/drive/MyDrive/CapstoneProject/prepped_data/y_train.csv', delimiter = ',')\n",
        "\n",
        "X_val = pd.read_csv('/content/drive/MyDrive/CapstoneProject/prepped_data/X_val.csv', delimiter = ',').drop('index', axis = 1)\n",
        "y_val = pd.read_csv('/content/drive/MyDrive/CapstoneProject/prepped_data/y_val.csv', delimiter = ',')\n",
        "\n",
        "X_val24 = pd.read_csv('/content/drive/MyDrive/CapstoneProject/prepped_data/X_val24.csv', delimiter = ',').drop('index', axis = 1)\n",
        "y_val24 = pd.read_csv('/content/drive/MyDrive/CapstoneProject/prepped_data/y_val24.csv', delimiter = ',')\n",
        "\n"
      ],
      "metadata": {
        "id": "8tKOIF0e122l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "id": "stfgmKKD3xh2",
        "outputId": "b7c7d6f0-6e76-434c-df47-cfba2bc4095c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      ctx-4     ctx-3     ctx-2     ctx-1  is_weekend  timeframe1  timeframe2  \\\n",
              "0  0.859259  0.925926  0.882716  0.885802         0.0         0.0         0.0   \n",
              "1  0.987500  1.000000  0.673611  0.708333         0.0         0.0         0.0   \n",
              "2  0.703704  0.589506  0.521605  0.864198         0.0         0.0         0.0   \n",
              "3  0.793750  0.846354  0.963542  0.997396         0.0         0.0         0.0   \n",
              "4  0.054545  0.030303  0.106061  0.060606         0.0         0.0         0.0   \n",
              "\n",
              "   timeframe3  timeframe4  timeframe5  ...  temperature  mm_precip  \\\n",
              "0         0.0         0.0         1.0  ...    -0.397143       -1.0   \n",
              "1         0.0         0.0         1.0  ...    -0.397143       -1.0   \n",
              "2         0.0         0.0         1.0  ...    -0.397143       -1.0   \n",
              "3         0.0         0.0         1.0  ...    -0.397143       -1.0   \n",
              "4         0.0         0.0         1.0  ...    -0.397143       -1.0   \n",
              "\n",
              "   temperature-1  mm_precip-1  temperature-2  mm_precip-2  temperature-3  \\\n",
              "0      -0.348378         -1.0      -0.366762         -1.0       -0.38307   \n",
              "1      -0.348378         -1.0      -0.366762         -1.0       -0.38307   \n",
              "2      -0.348378         -1.0      -0.366762         -1.0       -0.38307   \n",
              "3      -0.348378         -1.0      -0.366762         -1.0       -0.38307   \n",
              "4      -0.348378         -1.0      -0.366762         -1.0       -0.38307   \n",
              "\n",
              "   mm_precip-3  temperature-4  mm_precip-4  \n",
              "0         -1.0      -0.359773         -1.0  \n",
              "1         -1.0      -0.359773         -1.0  \n",
              "2         -1.0      -0.359773         -1.0  \n",
              "3         -1.0      -0.359773         -1.0  \n",
              "4         -1.0      -0.359773         -1.0  \n",
              "\n",
              "[5 rows x 29 columns]"
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              "  <div id=\"df-a2ae80f7-6092-4396-8fea-fc530cd651e2\">\n",
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              "      <div>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ctx-4</th>\n",
              "      <th>ctx-3</th>\n",
              "      <th>ctx-2</th>\n",
              "      <th>ctx-1</th>\n",
              "      <th>is_weekend</th>\n",
              "      <th>timeframe1</th>\n",
              "      <th>timeframe2</th>\n",
              "      <th>timeframe3</th>\n",
              "      <th>timeframe4</th>\n",
              "      <th>timeframe5</th>\n",
              "      <th>...</th>\n",
              "      <th>temperature</th>\n",
              "      <th>mm_precip</th>\n",
              "      <th>temperature-1</th>\n",
              "      <th>mm_precip-1</th>\n",
              "      <th>temperature-2</th>\n",
              "      <th>mm_precip-2</th>\n",
              "      <th>temperature-3</th>\n",
              "      <th>mm_precip-3</th>\n",
              "      <th>temperature-4</th>\n",
              "      <th>mm_precip-4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>0.859259</td>\n",
              "      <td>0.925926</td>\n",
              "      <td>0.882716</td>\n",
              "      <td>0.885802</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.987500</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.673611</td>\n",
              "      <td>0.708333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>-0.397143</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.703704</td>\n",
              "      <td>0.589506</td>\n",
              "      <td>0.521605</td>\n",
              "      <td>0.864198</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.397143</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.348378</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.366762</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.38307</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.359773</td>\n",
              "      <td>-1.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.793750</td>\n",
              "      <td>0.846354</td>\n",
              "      <td>0.963542</td>\n",
              "      <td>0.997396</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>-1.0</td>\n",
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              "      <th>4</th>\n",
              "      <td>0.054545</td>\n",
              "      <td>0.030303</td>\n",
              "      <td>0.106061</td>\n",
              "      <td>0.060606</td>\n",
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              "      ctx-4     ctx-3     ctx-2     ctx-1  is_weekend  timeframe1  timeframe2  \\\n",
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              "4  0.820513  0.820513  0.820513  0.820513         1.0         1.0         0.0   \n",
              "\n",
              "   timeframe3  timeframe4  timeframe5  ...  temperature  mm_precip  \\\n",
              "0         0.0         0.0         0.0  ...        -0.48       -1.0   \n",
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              "   temperature-1  mm_precip-1  temperature-2  mm_precip-2  temperature-3  \\\n",
              "0      -0.444288         -1.0      -0.515759         -1.0      -0.497848   \n",
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              "4      -0.444288         -1.0      -0.515759         -1.0      -0.497848   \n",
              "\n",
              "   mm_precip-3  temperature-4  mm_precip-4  \n",
              "0         -1.0      -0.458924         -1.0  \n",
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              "      ctx-4     ctx-3     ctx-2     ctx-1  is_weekend  timeframe1  timeframe2  \\\n",
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              "      <td>-0.377331</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.359773</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.632812</td>\n",
              "      <td>0.601562</td>\n",
              "      <td>0.593750</td>\n",
              "      <td>0.600962</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.457143</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.407616</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.383954</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.377331</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.359773</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.829545</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>0.840909</td>\n",
              "      <td>0.840909</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.457143</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.407616</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.383954</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.377331</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.359773</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 29 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6bd16e04-2def-4168-8374-f4d76aed99ce')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "    .colab-df-container {\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6bd16e04-2def-4168-8374-f4d76aed99ce button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6bd16e04-2def-4168-8374-f4d76aed99ce');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_submission.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "id": "hVRZz3dyoNqn",
        "outputId": "d0a6c379-b0d6-4b49-8d63-0bff5f99ada8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "   index     ctx-4     ctx-3     ctx-2     ctx-1  is_weekend  timeframe1  \\\n",
              "0    0.0  0.753086  0.780864  0.799383  0.824074         0.0         0.0   \n",
              "1    1.0  0.463768  0.536232  0.532609  0.601449         0.0         0.0   \n",
              "2    2.0  0.787037  0.709877  0.611111  0.601852         0.0         1.0   \n",
              "3    3.0  0.753472  0.809028  0.819444  0.736111         0.0         0.0   \n",
              "4    4.0  0.861111  0.802469  0.814815  0.827160         1.0         0.0   \n",
              "\n",
              "   timeframe2  timeframe3  timeframe4  ...   temperature  mm_precip  \\\n",
              "0         1.0         0.0         0.0  ... -2.057143e-01       -1.0   \n",
              "1         0.0         1.0         0.0  ... -2.220446e-16       -1.0   \n",
              "2         0.0         0.0         0.0  ... -3.142857e-01       -1.0   \n",
              "3         0.0         0.0         1.0  ... -1.542857e-01       -1.0   \n",
              "4         0.0         0.0         0.0  ... -5.771429e-01       -1.0   \n",
              "\n",
              "   temperature-1  mm_precip-1  temperature-2  mm_precip-2  temperature-3  \\\n",
              "0      -0.275035         -1.0      -0.438395         -1.0      -0.618364   \n",
              "1       0.018336         -1.0       0.011461         -1.0      -0.032999   \n",
              "2      -0.325811         -1.0      -0.318052         -1.0      -0.302726   \n",
              "3      -0.139633         -1.0      -0.080229         -1.0      -0.012912   \n",
              "4      -0.517630         -1.0      -0.518625         -1.0      -0.474892   \n",
              "\n",
              "   mm_precip-3  temperature-4  mm_precip-4  \n",
              "0         -1.0      -0.620397         -1.0  \n",
              "1         -1.0      -0.090652         -1.0  \n",
              "2         -1.0      -0.294618         -1.0  \n",
              "3         -1.0       0.016997         -1.0  \n",
              "4         -1.0      -0.393768         -1.0  \n",
              "\n",
              "[5 rows x 30 columns]"
            ],
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              "\n",
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              "      <div>\n",
              "<style scoped>\n",
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              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>ctx-4</th>\n",
              "      <th>ctx-3</th>\n",
              "      <th>ctx-2</th>\n",
              "      <th>ctx-1</th>\n",
              "      <th>is_weekend</th>\n",
              "      <th>timeframe1</th>\n",
              "      <th>timeframe2</th>\n",
              "      <th>timeframe3</th>\n",
              "      <th>timeframe4</th>\n",
              "      <th>...</th>\n",
              "      <th>temperature</th>\n",
              "      <th>mm_precip</th>\n",
              "      <th>temperature-1</th>\n",
              "      <th>mm_precip-1</th>\n",
              "      <th>temperature-2</th>\n",
              "      <th>mm_precip-2</th>\n",
              "      <th>temperature-3</th>\n",
              "      <th>mm_precip-3</th>\n",
              "      <th>temperature-4</th>\n",
              "      <th>mm_precip-4</th>\n",
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              "      <td>0.780864</td>\n",
              "      <td>0.799383</td>\n",
              "      <td>0.824074</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>-0.438395</td>\n",
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              "      <td>-1.0</td>\n",
              "      <td>-0.620397</td>\n",
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              "      <td>0.536232</td>\n",
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              "      <td>-1.0</td>\n",
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              "      <td>-0.032999</td>\n",
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              "      <td>-0.090652</td>\n",
              "      <td>-1.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.0</td>\n",
              "      <td>0.787037</td>\n",
              "      <td>0.709877</td>\n",
              "      <td>0.611111</td>\n",
              "      <td>0.601852</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>-3.142857e-01</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.325811</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.318052</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.302726</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.294618</td>\n",
              "      <td>-1.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.0</td>\n",
              "      <td>0.753472</td>\n",
              "      <td>0.809028</td>\n",
              "      <td>0.819444</td>\n",
              "      <td>0.736111</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>-1.542857e-01</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.139633</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.080229</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.012912</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.016997</td>\n",
              "      <td>-1.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.0</td>\n",
              "      <td>0.861111</td>\n",
              "      <td>0.802469</td>\n",
              "      <td>0.814815</td>\n",
              "      <td>0.827160</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>-5.771429e-01</td>\n",
              "      <td>-1.0</td>\n",
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              "      <td>-1.0</td>\n",
              "      <td>-0.518625</td>\n",
              "      <td>-1.0</td>\n",
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              "      <td>-1.0</td>\n",
              "      <td>-0.393768</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 30 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-12bc8c2e-3a8e-4e66-afbf-c3865fad4444')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-12bc8c2e-3a8e-4e66-afbf-c3865fad4444 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-12bc8c2e-3a8e-4e66-afbf-c3865fad4444');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# We confirm that we are retaining the same number of rows after proecessing --> Earlier versions of the code gave problems regarding to this\n",
        "\n",
        "X_submission.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yTcHHMNMrNhX",
        "outputId": "b6bcc581-c40c-4c2c-e010-394107e25727"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(54999, 30)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fX91x0-Qn_tA"
      },
      "source": [
        "# Model selection and training\n",
        "\n",
        "For this project we will use 2 different models. The first one is an xgboost regressor, that has been chosen following other similar projects to this one and that has outperformed with less training times other similar ensemble models.\n",
        "Then, a neural network containing LSTM layers is used to capture the temporal features and their ordering.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## XGBregressor\n",
        "\n",
        "We will first use a data subset to perform a grid search on the parameter space to find the ones that fit the best our xgboost model."
      ],
      "metadata": {
        "id": "RmYozEtRtBo_"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GM07uC4l6e3E"
      },
      "outputs": [],
      "source": [
        "import xgboost as xgb\n",
        "from xgboost import XGBRegressor\n",
        "from sklearn.metrics import mean_squared_error\n",
        "import json\n",
        "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0n69xbBg6e3E",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "42998256-52b9-40f2-99f4-4e6d5922e533"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 2 folds for each of 972 candidates, totalling 1944 fits\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.8s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   1.7s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.5s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   2.5s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   3.0s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.4s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.4s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.4s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.4s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.5s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   3.1s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.7s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.7s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   3.5s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.7s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.7s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.7s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.9s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   3.3s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.7s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.7s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.7s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.7s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   3.7s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.7s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.7s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.7s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.7s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.7s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.4s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.7s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   2.5s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   1.5s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   1.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   1.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   1.5s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   2.1s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.5s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.5s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.5s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.5s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.5s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.5s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   3.4s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   3.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.7s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   2.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   1.8s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   1.1s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   1.1s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   1.1s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   1.1s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   1.0s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   1.0s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   1.1s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   1.1s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   1.0s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   4.1s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   1.0s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   1.1s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   1.1s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   1.1s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   1.1s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   1.1s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   1.0s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   1.0s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   4.0s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   1.1s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   1.1s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   1.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   1.1s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   1.1s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   1.1s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   1.1s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   1.1s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   1.3s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   3.8s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   1.1s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   1.1s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   1.1s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   1.1s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   1.1s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   1.1s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   1.1s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   1.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   4.1s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   1.1s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   1.1s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.9s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.9s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   1.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   1.1s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   1.1s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   1.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   3.6s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   1.1s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   1.1s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   1.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   1.1s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   1.2s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   1.1s\n",
            "[CV] END learning_rate=0.1, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.9s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.5s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   3.0s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.4s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   3.1s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.5s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.5s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.5s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.5s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.5s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.5s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.5s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.5s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.5s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.5s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.5s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   3.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.5s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.5s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.5s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.5s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.5s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.5s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.5s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.5s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.5s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.5s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.5s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.5s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.4s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.4s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.5s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.5s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   3.2s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.5s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.5s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.5s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.4s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.5s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.5s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.5s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.4s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.8s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.8s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.8s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.8s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.8s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   3.8s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.8s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.8s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.8s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.8s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.8s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.9s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.8s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.8s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.8s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.8s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.8s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   1.0s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   3.7s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.9s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.9s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.9s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.9s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.8s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.9s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.8s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.8s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.9s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.8s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.9s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   3.7s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.9s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.9s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.9s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.8s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.8s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.8s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.8s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.9s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.8s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.9s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.8s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   1.7s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   3.0s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.8s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   1.0s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   1.7s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   1.2s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   1.0s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   1.1s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   1.0s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   1.2s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   3.4s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   1.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   1.5s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   1.6s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   1.5s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   1.6s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   1.5s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   1.6s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   4.0s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   1.6s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   1.6s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   1.6s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   1.5s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   1.5s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   4.0s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   1.7s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   1.6s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   1.6s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   1.5s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   1.6s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   1.7s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   4.0s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   1.7s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   1.7s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   1.7s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   1.7s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   1.7s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   4.0s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   1.7s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   1.7s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   1.7s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   1.7s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   1.7s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   4.0s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   1.6s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   1.6s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   1.7s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   1.6s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   1.6s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   3.0s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   2.7s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   1.7s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   1.0s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   1.0s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   1.6s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   1.6s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   1.7s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   3.9s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   1.1s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   1.0s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   1.6s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   1.6s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   1.6s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   1.9s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   3.3s\n",
            "[CV] END learning_rate=0.1, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   1.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   1.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.9s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   1.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   1.9s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   2.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   1.1s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   2.6s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.9s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   2.8s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   3.0s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   3.0s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   2.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   1.5s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   1.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   2.9s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.8s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   2.8s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   2.4s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   1.0s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.8s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   3.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   1.1s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   1.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   1.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   1.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   1.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   1.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   1.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   3.4s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   1.1s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   1.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   1.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   1.1s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   1.1s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   1.1s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   1.1s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   1.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   3.6s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   1.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   1.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   1.1s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   1.1s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   1.1s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   1.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   1.1s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   1.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   3.5s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   1.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   1.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   1.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   1.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   1.1s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   1.1s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   1.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   1.1s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   3.4s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   1.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   1.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   1.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   1.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   1.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   1.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   1.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   1.1s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   3.5s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   1.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   1.1s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   1.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   1.1s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   1.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   1.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   1.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   1.2s\n",
            "[CV] END learning_rate=0.01, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   3.5s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   1.0s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   1.7s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.5s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.5s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.5s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.5s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.5s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.5s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.5s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.7s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   2.6s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.5s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.5s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.5s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.5s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.5s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.5s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.5s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.5s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.5s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.5s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.5s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.5s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.5s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.5s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   1.8s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   1.4s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.5s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.5s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.5s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.5s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.4s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.9s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   3.2s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.9s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.9s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.9s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.9s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.9s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.8s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.9s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.9s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.9s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.9s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.8s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   3.3s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.9s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.9s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.8s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.9s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.9s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.8s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.9s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.9s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.9s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.9s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.9s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   3.2s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.9s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.9s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.9s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.9s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.8s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.8s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.8s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.9s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.8s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.9s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.8s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   3.0s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.9s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.8s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.8s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.8s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.8s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.8s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.8s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.8s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.9s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.9s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.8s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.9s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   3.1s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.8s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.8s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.8s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   1.7s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   1.7s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   1.8s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   1.8s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   4.2s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   1.7s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   1.7s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   1.7s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   1.7s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   1.7s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   4.1s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   1.8s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   1.7s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   1.7s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   1.7s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   1.7s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   4.0s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   1.7s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   1.7s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   1.8s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   1.8s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   1.8s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   4.0s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   1.8s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   1.7s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   1.7s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   1.7s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   1.7s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   4.1s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   1.7s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   1.8s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   1.7s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   1.7s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   1.7s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   4.1s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   1.8s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   1.6s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   1.6s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   1.7s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   1.7s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   4.1s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   1.7s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   1.6s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   1.7s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   1.7s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   1.7s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   4.0s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   1.7s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   1.6s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   1.6s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   1.6s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   1.7s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   1.9s\n",
            "[CV] END learning_rate=0.01, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   3.9s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.1s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.9s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   1.9s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   1.5s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   1.5s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   2.4s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   2.2s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   1.5s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   2.8s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.9s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.8s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   3.0s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   3.0s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=3, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.7s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.5s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   2.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.5s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   2.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.8s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   2.7s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.8s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   2.7s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.8s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   2.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   3.0s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   1.1s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   1.1s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   1.1s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   1.1s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   1.1s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   1.1s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   1.1s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   1.1s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   3.5s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   1.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   1.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   1.1s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   1.1s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   1.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   1.1s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   1.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   1.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   3.5s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   1.1s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   1.1s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   1.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   1.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   1.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   1.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   1.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   1.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   3.5s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   1.1s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   1.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   1.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   1.1s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   1.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   1.1s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   1.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   1.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   3.4s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   1.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   1.1s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   1.1s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   1.1s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   1.1s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   1.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   1.1s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   1.1s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   2.1s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   2.6s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   1.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   1.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   1.1s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   1.1s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   1.1s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   1.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   1.2s\n",
            "[CV] END learning_rate=0.001, max_depth=5, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   2.6s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   1.2s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.5s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   2.0s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=30, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.3s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.5s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.5s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.5s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.5s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.5s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.5s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.5s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.5s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.5s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.8s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   2.4s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.5s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.5s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.5s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.5s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.5s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.5s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.5s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.5s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.5s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.5s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.5s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.5s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   2.4s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.6s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.5s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.5s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=50, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.4s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.9s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   0.9s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   3.2s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   0.9s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.9s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   0.9s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.9s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   0.8s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.9s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   0.9s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.9s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   0.9s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.8s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   0.8s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   3.2s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   0.8s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.9s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   0.9s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.9s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   0.9s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.9s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   0.8s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.9s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   0.9s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.9s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   0.8s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   3.2s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   0.9s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.9s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   0.9s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.8s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   0.8s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.8s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   0.8s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.8s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   0.8s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.8s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   0.8s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   3.1s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   0.8s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.8s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   0.8s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.8s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   0.8s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.8s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   0.8s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.8s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   0.8s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.8s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   0.8s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   0.8s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   3.1s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.8s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=100, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   0.8s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   1.7s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=0.8; total time=   1.7s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   1.8s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=0.9; total time=   1.8s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   4.1s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=0.1, subsample=1.0; total time=   1.7s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   1.7s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=0.8; total time=   1.7s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   1.7s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=0.9; total time=   1.7s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   4.1s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=1.0, subsample=1.0; total time=   1.7s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   1.7s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=0.8; total time=   1.6s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   1.6s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=0.9; total time=   1.7s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   1.7s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=0, reg_lambda=10.0, subsample=1.0; total time=   3.9s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   1.7s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8; total time=   1.7s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   1.8s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=0.9; total time=   1.8s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   1.7s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=0.1, subsample=1.0; total time=   4.0s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   1.6s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=0.8; total time=   1.6s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   1.7s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=0.9; total time=   1.7s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   1.7s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=1.0, subsample=1.0; total time=   4.0s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   1.6s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=0.8; total time=   1.6s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   1.6s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=0.9; total time=   1.6s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   1.6s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=0.1, reg_lambda=10.0, subsample=1.0; total time=   4.0s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   1.6s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=0.8; total time=   1.6s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   1.6s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=0.9; total time=   1.6s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   1.6s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=0.1, subsample=1.0; total time=   1.6s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   3.9s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=0.8; total time=   1.6s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   1.7s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=0.9; total time=   1.6s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   1.7s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=1.0, subsample=1.0; total time=   1.6s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   3.8s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=0.8; total time=   1.5s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   1.6s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=0.9; total time=   1.6s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   1.6s\n",
            "[CV] END learning_rate=0.001, max_depth=7, n_estimators=200, reg_alpha=1.0, reg_lambda=10.0, subsample=1.0; total time=   1.6s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=2,\n",
              "             estimator=XGBRegressor(base_score=None, booster=None,\n",
              "                                    callbacks=None, colsample_bylevel=None,\n",
              "                                    colsample_bynode=None,\n",
              "                                    colsample_bytree=None,\n",
              "                                    early_stopping_rounds=None,\n",
              "                                    enable_categorical=False, eval_metric=None,\n",
              "                                    feature_types=None, gamma=None, gpu_id=None,\n",
              "                                    grow_policy=None, importance_type=None,\n",
              "                                    interaction_constraints=None,\n",
              "                                    learning_rate=None, m...\n",
              "                                    min_child_weight=None, missing=nan,\n",
              "                                    monotone_constraints=None, n_estimators=100,\n",
              "                                    n_jobs=None, num_parallel_tree=None,\n",
              "                                    predictor=None, random_state=40, ...),\n",
              "             param_grid={'learning_rate': [0.1, 0.01, 0.001],\n",
              "                         'max_depth': [3, 5, 7],\n",
              "                         'n_estimators': [30, 50, 100, 200],\n",
              "                         'reg_alpha': [0, 0.1, 1.0],\n",
              "                         'reg_lambda': [0.1, 1.0, 10.0],\n",
              "                         'subsample': [0.8, 0.9, 1.0]},\n",
              "             scoring='neg_mean_squared_error', verbose=2)"
            ],
            "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>GridSearchCV(cv=2,\n",
              "             estimator=XGBRegressor(base_score=None, booster=None,\n",
              "                                    callbacks=None, colsample_bylevel=None,\n",
              "                                    colsample_bynode=None,\n",
              "                                    colsample_bytree=None,\n",
              "                                    early_stopping_rounds=None,\n",
              "                                    enable_categorical=False, eval_metric=None,\n",
              "                                    feature_types=None, gamma=None, gpu_id=None,\n",
              "                                    grow_policy=None, importance_type=None,\n",
              "                                    interaction_constraints=None,\n",
              "                                    learning_rate=None, m...\n",
              "                                    min_child_weight=None, missing=nan,\n",
              "                                    monotone_constraints=None, n_estimators=100,\n",
              "                                    n_jobs=None, num_parallel_tree=None,\n",
              "                                    predictor=None, random_state=40, ...),\n",
              "             param_grid={&#x27;learning_rate&#x27;: [0.1, 0.01, 0.001],\n",
              "                         &#x27;max_depth&#x27;: [3, 5, 7],\n",
              "                         &#x27;n_estimators&#x27;: [30, 50, 100, 200],\n",
              "                         &#x27;reg_alpha&#x27;: [0, 0.1, 1.0],\n",
              "                         &#x27;reg_lambda&#x27;: [0.1, 1.0, 10.0],\n",
              "                         &#x27;subsample&#x27;: [0.8, 0.9, 1.0]},\n",
              "             scoring=&#x27;neg_mean_squared_error&#x27;, verbose=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=2,\n",
              "             estimator=XGBRegressor(base_score=None, booster=None,\n",
              "                                    callbacks=None, colsample_bylevel=None,\n",
              "                                    colsample_bynode=None,\n",
              "                                    colsample_bytree=None,\n",
              "                                    early_stopping_rounds=None,\n",
              "                                    enable_categorical=False, eval_metric=None,\n",
              "                                    feature_types=None, gamma=None, gpu_id=None,\n",
              "                                    grow_policy=None, importance_type=None,\n",
              "                                    interaction_constraints=None,\n",
              "                                    learning_rate=None, m...\n",
              "                                    min_child_weight=None, missing=nan,\n",
              "                                    monotone_constraints=None, n_estimators=100,\n",
              "                                    n_jobs=None, num_parallel_tree=None,\n",
              "                                    predictor=None, random_state=40, ...),\n",
              "             param_grid={&#x27;learning_rate&#x27;: [0.1, 0.01, 0.001],\n",
              "                         &#x27;max_depth&#x27;: [3, 5, 7],\n",
              "                         &#x27;n_estimators&#x27;: [30, 50, 100, 200],\n",
              "                         &#x27;reg_alpha&#x27;: [0, 0.1, 1.0],\n",
              "                         &#x27;reg_lambda&#x27;: [0.1, 1.0, 10.0],\n",
              "                         &#x27;subsample&#x27;: [0.8, 0.9, 1.0]},\n",
              "             scoring=&#x27;neg_mean_squared_error&#x27;, verbose=2)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "             predictor=None, random_state=40, ...)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "             predictor=None, random_state=40, ...)</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n",
        "\n",
        "gs_subset = X_train[X_train['year_normed'] == 0.75].sample(frac = 0.005, random_state = 5) # to restrict ourselves to more relevant data, we focus on 2022\n",
        "                                                                                           # and we use a small sample as the process takes a lot of time\n",
        "                                                                                           # We suspect that this is not giving optimal parameters (more on\n",
        "                                                                                           # this later) due to the small sample size\n",
        "\n",
        "y_subset = y_train.loc[gs_subset.index]\n",
        "\n",
        "\n",
        "space = {\n",
        "    'learning_rate': [0.1, 0.01, 0.001],\n",
        "    'max_depth': [3, 5, 7],\n",
        "    'subsample': [0.8, 0.9, 1.0],\n",
        "    'reg_lambda': [0.1, 1.0, 10.0],\n",
        "    'reg_alpha': [0, 0.1, 1.0],\n",
        "    'n_estimators': [30, 50, 100, 200]\n",
        "}\n",
        "\n",
        "xgb_model = XGBRegressor(objective='reg:squarederror', random_state = 40)\n",
        "grid_search = GridSearchCV(estimator=xgb_model, param_grid=space, scoring='mean_squared_error', cv=2, verbose=2)\n",
        "\n",
        "grid_search.fit(gs_subset, y_subset)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grid_search.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wA9DJo11ZJEd",
        "outputId": "02b7d0bd-c646-49d0-a6c4-b1e0b3a0c188"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'learning_rate': 0.1,\n",
              " 'max_depth': 3,\n",
              " 'n_estimators': 100,\n",
              " 'reg_alpha': 0.1,\n",
              " 'reg_lambda': 10.0,\n",
              " 'subsample': 0.8}"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SbtiaQn_6e3E"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "\n",
        "# Once we have found the best set of parameters, we save them\n",
        "\n",
        "parameter = open('/content/drive/MyDrive/CapstoneProject/models/xgboost/best_params.json', 'w+')\n",
        "parameter.write(json.dumps(grid_search.best_params_))\n",
        "parameter.close()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "41IPvSht6e3E",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8888589f-aaa5-4da9-fde6-8dd3bf6e0a3f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\tvalidation_0-rmse:0.28248\tvalidation_1-rmse:0.25879\tvalidation_2-rmse:0.25481\n",
            "[1]\tvalidation_0-rmse:0.25987\tvalidation_1-rmse:0.23870\tvalidation_2-rmse:0.23439\n",
            "[2]\tvalidation_0-rmse:0.23998\tvalidation_1-rmse:0.22111\tvalidation_2-rmse:0.21649\n",
            "[3]\tvalidation_0-rmse:0.22248\tvalidation_1-rmse:0.20577\tvalidation_2-rmse:0.20084\n",
            "[4]\tvalidation_0-rmse:0.20718\tvalidation_1-rmse:0.19242\tvalidation_2-rmse:0.18721\n",
            "[5]\tvalidation_0-rmse:0.19385\tvalidation_1-rmse:0.18094\tvalidation_2-rmse:0.17546\n",
            "[6]\tvalidation_0-rmse:0.18230\tvalidation_1-rmse:0.17108\tvalidation_2-rmse:0.16535\n",
            "[7]\tvalidation_0-rmse:0.17234\tvalidation_1-rmse:0.16264\tvalidation_2-rmse:0.15677\n",
            "[8]\tvalidation_0-rmse:0.16370\tvalidation_1-rmse:0.15539\tvalidation_2-rmse:0.14938\n",
            "[9]\tvalidation_0-rmse:0.15946\tvalidation_1-rmse:0.15223\tvalidation_2-rmse:0.14585\n",
            "[10]\tvalidation_0-rmse:0.15285\tvalidation_1-rmse:0.14676\tvalidation_2-rmse:0.14033\n",
            "[11]\tvalidation_0-rmse:0.14712\tvalidation_1-rmse:0.14202\tvalidation_2-rmse:0.13558\n",
            "[12]\tvalidation_0-rmse:0.14217\tvalidation_1-rmse:0.13796\tvalidation_2-rmse:0.13150\n",
            "[13]\tvalidation_0-rmse:0.13792\tvalidation_1-rmse:0.13447\tvalidation_2-rmse:0.12808\n",
            "[14]\tvalidation_0-rmse:0.13434\tvalidation_1-rmse:0.13159\tvalidation_2-rmse:0.12520\n",
            "[15]\tvalidation_0-rmse:0.13122\tvalidation_1-rmse:0.12899\tvalidation_2-rmse:0.12267\n",
            "[16]\tvalidation_0-rmse:0.12859\tvalidation_1-rmse:0.12676\tvalidation_2-rmse:0.12060\n",
            "[17]\tvalidation_0-rmse:0.12766\tvalidation_1-rmse:0.12610\tvalidation_2-rmse:0.11997\n",
            "[18]\tvalidation_0-rmse:0.12554\tvalidation_1-rmse:0.12434\tvalidation_2-rmse:0.11836\n",
            "[19]\tvalidation_0-rmse:0.12377\tvalidation_1-rmse:0.12286\tvalidation_2-rmse:0.11702\n",
            "[20]\tvalidation_0-rmse:0.12225\tvalidation_1-rmse:0.12164\tvalidation_2-rmse:0.11592\n",
            "[21]\tvalidation_0-rmse:0.12091\tvalidation_1-rmse:0.12051\tvalidation_2-rmse:0.11498\n",
            "[22]\tvalidation_0-rmse:0.11978\tvalidation_1-rmse:0.11953\tvalidation_2-rmse:0.11419\n",
            "[23]\tvalidation_0-rmse:0.11883\tvalidation_1-rmse:0.11882\tvalidation_2-rmse:0.11353\n",
            "[24]\tvalidation_0-rmse:0.11800\tvalidation_1-rmse:0.11814\tvalidation_2-rmse:0.11297\n",
            "[25]\tvalidation_0-rmse:0.11766\tvalidation_1-rmse:0.11796\tvalidation_2-rmse:0.11282\n",
            "[26]\tvalidation_0-rmse:0.11727\tvalidation_1-rmse:0.11773\tvalidation_2-rmse:0.11268\n",
            "[27]\tvalidation_0-rmse:0.11656\tvalidation_1-rmse:0.11712\tvalidation_2-rmse:0.11226\n",
            "[28]\tvalidation_0-rmse:0.11598\tvalidation_1-rmse:0.11664\tvalidation_2-rmse:0.11192\n",
            "[29]\tvalidation_0-rmse:0.11544\tvalidation_1-rmse:0.11617\tvalidation_2-rmse:0.11161\n",
            "[30]\tvalidation_0-rmse:0.11501\tvalidation_1-rmse:0.11582\tvalidation_2-rmse:0.11135\n",
            "[31]\tvalidation_0-rmse:0.11455\tvalidation_1-rmse:0.11541\tvalidation_2-rmse:0.11110\n",
            "[32]\tvalidation_0-rmse:0.11415\tvalidation_1-rmse:0.11504\tvalidation_2-rmse:0.11087\n",
            "[33]\tvalidation_0-rmse:0.11377\tvalidation_1-rmse:0.11472\tvalidation_2-rmse:0.11068\n",
            "[34]\tvalidation_0-rmse:0.11354\tvalidation_1-rmse:0.11461\tvalidation_2-rmse:0.11062\n",
            "[35]\tvalidation_0-rmse:0.11336\tvalidation_1-rmse:0.11451\tvalidation_2-rmse:0.11057\n",
            "[36]\tvalidation_0-rmse:0.11309\tvalidation_1-rmse:0.11427\tvalidation_2-rmse:0.11043\n",
            "[37]\tvalidation_0-rmse:0.11286\tvalidation_1-rmse:0.11410\tvalidation_2-rmse:0.11030\n",
            "[38]\tvalidation_0-rmse:0.11261\tvalidation_1-rmse:0.11394\tvalidation_2-rmse:0.11022\n",
            "[39]\tvalidation_0-rmse:0.11241\tvalidation_1-rmse:0.11374\tvalidation_2-rmse:0.11008\n",
            "[40]\tvalidation_0-rmse:0.11221\tvalidation_1-rmse:0.11360\tvalidation_2-rmse:0.11007\n",
            "[41]\tvalidation_0-rmse:0.11200\tvalidation_1-rmse:0.11343\tvalidation_2-rmse:0.10992\n",
            "[42]\tvalidation_0-rmse:0.11183\tvalidation_1-rmse:0.11331\tvalidation_2-rmse:0.10980\n",
            "[43]\tvalidation_0-rmse:0.11171\tvalidation_1-rmse:0.11325\tvalidation_2-rmse:0.10978\n",
            "[44]\tvalidation_0-rmse:0.11163\tvalidation_1-rmse:0.11319\tvalidation_2-rmse:0.10973\n",
            "[45]\tvalidation_0-rmse:0.11141\tvalidation_1-rmse:0.11300\tvalidation_2-rmse:0.10962\n",
            "[46]\tvalidation_0-rmse:0.11132\tvalidation_1-rmse:0.11295\tvalidation_2-rmse:0.10959\n",
            "[47]\tvalidation_0-rmse:0.11122\tvalidation_1-rmse:0.11283\tvalidation_2-rmse:0.10953\n",
            "[48]\tvalidation_0-rmse:0.11105\tvalidation_1-rmse:0.11268\tvalidation_2-rmse:0.10945\n",
            "[49]\tvalidation_0-rmse:0.11089\tvalidation_1-rmse:0.11255\tvalidation_2-rmse:0.10936\n",
            "[50]\tvalidation_0-rmse:0.11079\tvalidation_1-rmse:0.11252\tvalidation_2-rmse:0.10937\n",
            "[51]\tvalidation_0-rmse:0.11072\tvalidation_1-rmse:0.11245\tvalidation_2-rmse:0.10933\n",
            "[52]\tvalidation_0-rmse:0.11060\tvalidation_1-rmse:0.11236\tvalidation_2-rmse:0.10925\n",
            "[53]\tvalidation_0-rmse:0.11049\tvalidation_1-rmse:0.11228\tvalidation_2-rmse:0.10921\n",
            "[54]\tvalidation_0-rmse:0.11033\tvalidation_1-rmse:0.11213\tvalidation_2-rmse:0.10912\n",
            "[55]\tvalidation_0-rmse:0.11019\tvalidation_1-rmse:0.11201\tvalidation_2-rmse:0.10905\n",
            "[56]\tvalidation_0-rmse:0.11013\tvalidation_1-rmse:0.11195\tvalidation_2-rmse:0.10902\n",
            "[57]\tvalidation_0-rmse:0.11005\tvalidation_1-rmse:0.11187\tvalidation_2-rmse:0.10897\n",
            "[58]\tvalidation_0-rmse:0.10995\tvalidation_1-rmse:0.11182\tvalidation_2-rmse:0.10896\n",
            "[59]\tvalidation_0-rmse:0.10987\tvalidation_1-rmse:0.11177\tvalidation_2-rmse:0.10891\n",
            "[60]\tvalidation_0-rmse:0.10981\tvalidation_1-rmse:0.11173\tvalidation_2-rmse:0.10891\n",
            "[61]\tvalidation_0-rmse:0.10974\tvalidation_1-rmse:0.11168\tvalidation_2-rmse:0.10889\n",
            "[62]\tvalidation_0-rmse:0.10968\tvalidation_1-rmse:0.11164\tvalidation_2-rmse:0.10886\n",
            "[63]\tvalidation_0-rmse:0.10960\tvalidation_1-rmse:0.11156\tvalidation_2-rmse:0.10880\n",
            "[64]\tvalidation_0-rmse:0.10951\tvalidation_1-rmse:0.11148\tvalidation_2-rmse:0.10873\n",
            "[65]\tvalidation_0-rmse:0.10943\tvalidation_1-rmse:0.11141\tvalidation_2-rmse:0.10868\n",
            "[66]\tvalidation_0-rmse:0.10937\tvalidation_1-rmse:0.11135\tvalidation_2-rmse:0.10865\n",
            "[67]\tvalidation_0-rmse:0.10929\tvalidation_1-rmse:0.11128\tvalidation_2-rmse:0.10858\n",
            "[68]\tvalidation_0-rmse:0.10919\tvalidation_1-rmse:0.11122\tvalidation_2-rmse:0.10856\n",
            "[69]\tvalidation_0-rmse:0.10914\tvalidation_1-rmse:0.11117\tvalidation_2-rmse:0.10851\n",
            "[70]\tvalidation_0-rmse:0.10909\tvalidation_1-rmse:0.11114\tvalidation_2-rmse:0.10850\n",
            "[71]\tvalidation_0-rmse:0.10903\tvalidation_1-rmse:0.11108\tvalidation_2-rmse:0.10847\n",
            "[72]\tvalidation_0-rmse:0.10896\tvalidation_1-rmse:0.11103\tvalidation_2-rmse:0.10842\n",
            "[73]\tvalidation_0-rmse:0.10892\tvalidation_1-rmse:0.11100\tvalidation_2-rmse:0.10839\n",
            "[74]\tvalidation_0-rmse:0.10882\tvalidation_1-rmse:0.11094\tvalidation_2-rmse:0.10838\n",
            "[75]\tvalidation_0-rmse:0.10877\tvalidation_1-rmse:0.11087\tvalidation_2-rmse:0.10834\n",
            "[76]\tvalidation_0-rmse:0.10871\tvalidation_1-rmse:0.11082\tvalidation_2-rmse:0.10829\n",
            "[77]\tvalidation_0-rmse:0.10863\tvalidation_1-rmse:0.11074\tvalidation_2-rmse:0.10824\n",
            "[78]\tvalidation_0-rmse:0.10858\tvalidation_1-rmse:0.11071\tvalidation_2-rmse:0.10823\n",
            "[79]\tvalidation_0-rmse:0.10851\tvalidation_1-rmse:0.11066\tvalidation_2-rmse:0.10821\n",
            "[80]\tvalidation_0-rmse:0.10846\tvalidation_1-rmse:0.11061\tvalidation_2-rmse:0.10819\n",
            "[81]\tvalidation_0-rmse:0.10842\tvalidation_1-rmse:0.11058\tvalidation_2-rmse:0.10816\n",
            "[82]\tvalidation_0-rmse:0.10837\tvalidation_1-rmse:0.11053\tvalidation_2-rmse:0.10816\n",
            "[83]\tvalidation_0-rmse:0.10834\tvalidation_1-rmse:0.11053\tvalidation_2-rmse:0.10817\n",
            "[84]\tvalidation_0-rmse:0.10832\tvalidation_1-rmse:0.11053\tvalidation_2-rmse:0.10817\n",
            "[85]\tvalidation_0-rmse:0.10826\tvalidation_1-rmse:0.11046\tvalidation_2-rmse:0.10812\n",
            "[86]\tvalidation_0-rmse:0.10822\tvalidation_1-rmse:0.11042\tvalidation_2-rmse:0.10810\n",
            "[87]\tvalidation_0-rmse:0.10817\tvalidation_1-rmse:0.11038\tvalidation_2-rmse:0.10806\n",
            "[88]\tvalidation_0-rmse:0.10815\tvalidation_1-rmse:0.11036\tvalidation_2-rmse:0.10805\n",
            "[89]\tvalidation_0-rmse:0.10812\tvalidation_1-rmse:0.11035\tvalidation_2-rmse:0.10805\n",
            "[90]\tvalidation_0-rmse:0.10808\tvalidation_1-rmse:0.11034\tvalidation_2-rmse:0.10805\n",
            "[91]\tvalidation_0-rmse:0.10806\tvalidation_1-rmse:0.11032\tvalidation_2-rmse:0.10804\n",
            "[92]\tvalidation_0-rmse:0.10802\tvalidation_1-rmse:0.11027\tvalidation_2-rmse:0.10800\n",
            "[93]\tvalidation_0-rmse:0.10797\tvalidation_1-rmse:0.11023\tvalidation_2-rmse:0.10797\n",
            "[94]\tvalidation_0-rmse:0.10795\tvalidation_1-rmse:0.11023\tvalidation_2-rmse:0.10798\n",
            "[95]\tvalidation_0-rmse:0.10787\tvalidation_1-rmse:0.11014\tvalidation_2-rmse:0.10795\n",
            "[96]\tvalidation_0-rmse:0.10784\tvalidation_1-rmse:0.11011\tvalidation_2-rmse:0.10792\n",
            "[97]\tvalidation_0-rmse:0.10780\tvalidation_1-rmse:0.11007\tvalidation_2-rmse:0.10789\n",
            "[98]\tvalidation_0-rmse:0.10777\tvalidation_1-rmse:0.11006\tvalidation_2-rmse:0.10790\n",
            "[99]\tvalidation_0-rmse:0.10773\tvalidation_1-rmse:0.11002\tvalidation_2-rmse:0.10786\n",
            "rmse: 0.11001850344693934\n"
          ]
        }
      ],
      "source": [
        "# And now that we have the best parameters for our subset of data we train the model.\n",
        "# We also use the validation dataset to monitor the performance of the model\n",
        "# during training to look for overfitting curves\n",
        "\n",
        "xgb_model = XGBRegressor(objective='reg:squarederror', colsample_bytree=0.8, gamma=0.1, n_jobs=-1, random_state=123, eval_metric = 'rmse', early_stopping_rounds = 30)\n",
        "\n",
        "with open('/content/drive/MyDrive/CapstoneProject/models/xgboost/best_params.json', 'r') as f:\n",
        "  best_params = json.load(f)\n",
        "\n",
        "xgb_model.set_params(**best_params)\n",
        "\n",
        "evalset = [(X_train, y_train), (X_val, y_val), (X_val24, y_val24)]\n",
        "\n",
        "xgb_model.fit(X_train, y_train, eval_set = evalset)\n",
        "\n",
        "y_pred = xgb_model.predict(X_val)\n",
        "\n",
        "rmse = np.sqrt(mean_squared_error(y_true = y_val, y_pred = y_pred))\n",
        "\n",
        "print('rmse:', rmse)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Once we have trained the model, we are interested in knowing how the loss evolved during training, aswell as what variables were the most relevant to predict the percentage of available docks"
      ],
      "metadata": {
        "id": "cY9Pl_EuPX9W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost import plot_importance\n",
        "from matplotlib import pyplot\n",
        "\n",
        "results = xgb_model.evals_result()\n",
        "pyplot.plot(results['validation_0']['rmse'], label='train')\n",
        "pyplot.plot(results['validation_1']['rmse'], label='val')\n",
        "\n",
        "pyplot.plot(results['validation_2']['rmse'], label='val24')\n",
        "pyplot.xlabel('iteration')\n",
        "pyplot.ylabel('rmse')\n",
        "pyplot.yscale('log')\n",
        "# show the legend\n",
        "pyplot.legend()\n",
        "# show the plot\n",
        "pyplot.savefig('/content/drive/MyDrive/CapstoneProject/plots and figures/xgb_loss.png')\n",
        "pyplot.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "3QIAckmrPkU-",
        "outputId": "644f18bf-eff6-4600-a01c-39abe8991a27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_importance(xgb_model)\n",
        "pyplot.savefig('/content/drive/MyDrive/CapstoneProject/plots and figures/xgb_features.png')\n",
        "pyplot.plot()"
      ],
      "metadata": {
        "id": "n78gFBr7_LPy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "outputId": "c91b5df0-3dca-4eba-9606-68c253abfdc7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The feature important plot is really interesting. From it we can conclude that:\n",
        "- A expected, the previous hours percentage gives a lot of info about how many docks will be available in the following hours.\n",
        "- The location of the station are the next most relevant features. The usage of the service is not homogeneus across the city and this impacts te availability of docks\n",
        "- timeframe2, corresponding to peak hours in the morning has quite a bit of weight. The stations network probably has the most activity in these hours\n",
        "- the hour also affects quite a bit the final %, as expected --> Since we are only training with\n",
        "- The weather does not even appear in the graph. We expected way more weight from those variables, especially rain. We guess that since Barcelon is not a very rainy city, the model does not see many registers with rain and so it end up ignoring completely the variable"
      ],
      "metadata": {
        "id": "aaa-PhwKVgKN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "xgb_model.save_model('/content/drive/MyDrive/CapstoneProject/models/xgboost/xgb.json')"
      ],
      "metadata": {
        "id": "pvf6tn6VmZb5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_submission = xgb_model.predict(X_submission.drop('index', axis = 1))\n",
        "prediction = pd.DataFrame({'percentage_docks_available':y_submission.flatten()})\n",
        "prediction = prediction.reset_index()\n",
        "prediction.to_csv('/content/drive/MyDrive/CapstoneProject/models/xgboost/xgb_prediction.csv', index = False)"
      ],
      "metadata": {
        "id": "nLW1v014nDQN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Second XGBoost model\n",
        "In an earlier version of the code, we started with different parameters, using 1000 estimators and a max depth of 6, and ended up getting better results. Let's try to replicate that behaviour with a more complex model that uses more estimators, aswell as higher max_depth, even though the grid search gave us different results, to see if we can get better performance out of the model."
      ],
      "metadata": {
        "id": "HYRAouisXgNe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# In this case the parameters are selected \"through intuition\" (or in spanish, a ojo)\n",
        "\n",
        "\n",
        "xgb_model2 = XGBRegressor(objective='reg:squarederror',\n",
        "                          n_estimators=2000,\n",
        "                          max_depth=10,\n",
        "                          learning_rate=0.1,\n",
        "                          subsample=0.8,\n",
        "                          colsample_bytree=0.8,\n",
        "                          gamma=0.1,\n",
        "                          reg_alpha=0.1,\n",
        "                          reg_lambda=10.0,\n",
        "                          n_jobs=-1,\n",
        "                          random_state=128,\n",
        "                          early_stopping_rounds = 30)\n",
        "\n",
        "evalset = [(X_train, y_train), (X_val, y_val), (X_val24, y_val24)]\n",
        "\n",
        "xgb_model2.fit(X_train, y_train, eval_set = evalset)\n",
        "\n",
        "y_pred = xgb_model2.predict(X_val)\n",
        "\n",
        "rmse = np.sqrt(mean_squared_error(y_true = y_val, y_pred = y_pred))\n",
        "\n",
        "print('rmse:', rmse)"
      ],
      "metadata": {
        "id": "j8kE-P-bXfc4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "751fa933-5ffe-45e5-b6ce-9789f6249f83"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "[170]\tvalidation_0-rmse:0.09507\tvalidation_1-rmse:0.10209\tvalidation_2-rmse:0.10374\n",
            "[171]\tvalidation_0-rmse:0.09505\tvalidation_1-rmse:0.10208\tvalidation_2-rmse:0.10374\n",
            "[172]\tvalidation_0-rmse:0.09504\tvalidation_1-rmse:0.10208\tvalidation_2-rmse:0.10374\n",
            "[173]\tvalidation_0-rmse:0.09503\tvalidation_1-rmse:0.10207\tvalidation_2-rmse:0.10374\n",
            "[174]\tvalidation_0-rmse:0.09502\tvalidation_1-rmse:0.10207\tvalidation_2-rmse:0.10374\n",
            "[175]\tvalidation_0-rmse:0.09496\tvalidation_1-rmse:0.10202\tvalidation_2-rmse:0.10373\n",
            "[176]\tvalidation_0-rmse:0.09495\tvalidation_1-rmse:0.10202\tvalidation_2-rmse:0.10372\n",
            "[177]\tvalidation_0-rmse:0.09488\tvalidation_1-rmse:0.10195\tvalidation_2-rmse:0.10372\n",
            "[178]\tvalidation_0-rmse:0.09487\tvalidation_1-rmse:0.10195\tvalidation_2-rmse:0.10372\n",
            "[179]\tvalidation_0-rmse:0.09484\tvalidation_1-rmse:0.10194\tvalidation_2-rmse:0.10372\n",
            "[180]\tvalidation_0-rmse:0.09484\tvalidation_1-rmse:0.10194\tvalidation_2-rmse:0.10372\n",
            "[181]\tvalidation_0-rmse:0.09483\tvalidation_1-rmse:0.10194\tvalidation_2-rmse:0.10372\n",
            "[182]\tvalidation_0-rmse:0.09482\tvalidation_1-rmse:0.10194\tvalidation_2-rmse:0.10372\n",
            "[183]\tvalidation_0-rmse:0.09481\tvalidation_1-rmse:0.10193\tvalidation_2-rmse:0.10372\n",
            "[184]\tvalidation_0-rmse:0.09479\tvalidation_1-rmse:0.10192\tvalidation_2-rmse:0.10372\n",
            "[185]\tvalidation_0-rmse:0.09478\tvalidation_1-rmse:0.10192\tvalidation_2-rmse:0.10372\n",
            "[186]\tvalidation_0-rmse:0.09476\tvalidation_1-rmse:0.10191\tvalidation_2-rmse:0.10372\n",
            "[187]\tvalidation_0-rmse:0.09476\tvalidation_1-rmse:0.10191\tvalidation_2-rmse:0.10372\n",
            "[188]\tvalidation_0-rmse:0.09473\tvalidation_1-rmse:0.10188\tvalidation_2-rmse:0.10372\n",
            "[189]\tvalidation_0-rmse:0.09472\tvalidation_1-rmse:0.10188\tvalidation_2-rmse:0.10372\n",
            "[190]\tvalidation_0-rmse:0.09470\tvalidation_1-rmse:0.10187\tvalidation_2-rmse:0.10373\n",
            "[191]\tvalidation_0-rmse:0.09467\tvalidation_1-rmse:0.10185\tvalidation_2-rmse:0.10373\n",
            "[192]\tvalidation_0-rmse:0.09466\tvalidation_1-rmse:0.10185\tvalidation_2-rmse:0.10372\n",
            "[193]\tvalidation_0-rmse:0.09461\tvalidation_1-rmse:0.10183\tvalidation_2-rmse:0.10372\n",
            "[194]\tvalidation_0-rmse:0.09459\tvalidation_1-rmse:0.10182\tvalidation_2-rmse:0.10373\n",
            "[195]\tvalidation_0-rmse:0.09458\tvalidation_1-rmse:0.10182\tvalidation_2-rmse:0.10373\n",
            "[196]\tvalidation_0-rmse:0.09452\tvalidation_1-rmse:0.10181\tvalidation_2-rmse:0.10372\n",
            "[197]\tvalidation_0-rmse:0.09451\tvalidation_1-rmse:0.10181\tvalidation_2-rmse:0.10372\n",
            "[198]\tvalidation_0-rmse:0.09450\tvalidation_1-rmse:0.10181\tvalidation_2-rmse:0.10372\n",
            "[199]\tvalidation_0-rmse:0.09449\tvalidation_1-rmse:0.10181\tvalidation_2-rmse:0.10372\n",
            "[200]\tvalidation_0-rmse:0.09448\tvalidation_1-rmse:0.10181\tvalidation_2-rmse:0.10373\n",
            "[201]\tvalidation_0-rmse:0.09447\tvalidation_1-rmse:0.10181\tvalidation_2-rmse:0.10373\n",
            "[202]\tvalidation_0-rmse:0.09445\tvalidation_1-rmse:0.10180\tvalidation_2-rmse:0.10372\n",
            "[203]\tvalidation_0-rmse:0.09442\tvalidation_1-rmse:0.10181\tvalidation_2-rmse:0.10372\n",
            "[204]\tvalidation_0-rmse:0.09440\tvalidation_1-rmse:0.10180\tvalidation_2-rmse:0.10372\n",
            "[205]\tvalidation_0-rmse:0.09439\tvalidation_1-rmse:0.10180\tvalidation_2-rmse:0.10372\n",
            "[206]\tvalidation_0-rmse:0.09438\tvalidation_1-rmse:0.10179\tvalidation_2-rmse:0.10372\n",
            "[207]\tvalidation_0-rmse:0.09435\tvalidation_1-rmse:0.10178\tvalidation_2-rmse:0.10372\n",
            "[208]\tvalidation_0-rmse:0.09434\tvalidation_1-rmse:0.10178\tvalidation_2-rmse:0.10372\n",
            "[209]\tvalidation_0-rmse:0.09433\tvalidation_1-rmse:0.10178\tvalidation_2-rmse:0.10372\n",
            "[210]\tvalidation_0-rmse:0.09432\tvalidation_1-rmse:0.10178\tvalidation_2-rmse:0.10372\n",
            "[211]\tvalidation_0-rmse:0.09431\tvalidation_1-rmse:0.10178\tvalidation_2-rmse:0.10372\n",
            "[212]\tvalidation_0-rmse:0.09431\tvalidation_1-rmse:0.10178\tvalidation_2-rmse:0.10372\n",
            "[213]\tvalidation_0-rmse:0.09428\tvalidation_1-rmse:0.10177\tvalidation_2-rmse:0.10373\n",
            "[214]\tvalidation_0-rmse:0.09426\tvalidation_1-rmse:0.10176\tvalidation_2-rmse:0.10373\n",
            "rmse: 0.10192343605538062\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results2 = xgb_model2.evals_result()\n",
        "pyplot.plot(results2['validation_0']['rmse'], label='train')\n",
        "pyplot.plot(results2['validation_1']['rmse'], label='val')\n",
        "\n",
        "pyplot.plot(results2['validation_2']['rmse'], label='val24')\n",
        "pyplot.xlabel('iteration')\n",
        "pyplot.ylabel('rmse')\n",
        "pyplot.yscale('log')\n",
        "# show the legend\n",
        "pyplot.legend()\n",
        "# show the plot\n",
        "pyplot.savefig('/content/drive/MyDrive/CapstoneProject/plots and figures/xgb_loss2.png')\n",
        "\n",
        "pyplot.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "m4UfvrKXT-1_",
        "outputId": "2daff2fb-b735-4368-8ebe-39fd058dc303"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_importance(xgb_model2)\n",
        "pyplot.savefig('/content/drive/MyDrive/CapstoneProject/plots and figures/xgb2_features.png')\n",
        "pyplot.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "ndYmJ6kNUebk",
        "outputId": "e1344190-4726-4c8c-c8ae-d2a4801de245"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "And in this new model, while it does get slightly better results, the higher complexity seems to lead to some overfitting. It is also notable that in this case, the weight of the location of the station has increased quite a lot, and now the model is considering the temperature as valuable information to proedict the % of free docks available"
      ],
      "metadata": {
        "id": "ZWiYunwtkSqf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "xgb_model2.save_model('/content/drive/MyDrive/CapstoneProject/models/xgboost/xgb2.json')\n"
      ],
      "metadata": {
        "id": "JnrUkt7IThfC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "y_submission2 = xgb_model2.predict(X_submission.drop('index', axis = 1))\n",
        "prediction2 = pd.DataFrame({'percentage_docks_available':y_submission2.flatten()})\n",
        "prediction2 = prediction2.reset_index()\n",
        "prediction2.to_csv('/content/drive/MyDrive/CapstoneProject/models/xgboost/xgb2_prediction.csv', index = False)"
      ],
      "metadata": {
        "id": "awNcepvNTrxc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OWHeU1co6e3F"
      },
      "source": [
        "## LSTM\n",
        " Apart from our xgbregressor model, and since our data contains historical data, we have also tried to study the prediction problem as a time-series analysis by working with long short-term memory layers on a neural network to try and exploit the temporal ordering of the information. Since not all the variables are historical, we have built a time series with just the one that are historical context, and we have treated the rest with a fully connected network. By uing this approach our intention is to capture not only the static context of the data, but also the past that leads to each station's state.\n",
        " The data corresponding to the two categories is first split into two grups.\n",
        " The static data is left as it is and the temporal is reshaped into a tensor that contains a third dimension with the information about previous timesteps.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9PLcHCoq6e3F"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "\n",
        "X_submission = pd.read_csv('/content/drive/MyDrive/CapstoneProject/prepped_data/X_submission.csv')\n",
        "X_train = pd.read_csv('/content/drive/MyDrive/CapstoneProject/prepped_data/X_train.csv', delimiter = ',').drop('index', axis = 1)\n",
        "y_train = pd.read_csv('/content/drive/MyDrive/CapstoneProject/prepped_data/y_train.csv', delimiter = ',')\n",
        "\n",
        "X_val = pd.read_csv('/content/drive/MyDrive/CapstoneProject/prepped_data/X_val.csv', delimiter = ',').drop('index', axis = 1)\n",
        "y_val = pd.read_csv('/content/drive/MyDrive/CapstoneProject/prepped_data/y_val.csv', delimiter = ',')\n",
        "\n",
        "X_val24 = pd.read_csv('/content/drive/MyDrive/CapstoneProject/prepped_data/X_val24.csv', delimiter = ',').drop('index', axis = 1)\n",
        "y_val24 = pd.read_csv('/content/drive/MyDrive/CapstoneProject/prepped_data/y_val24.csv', delimiter = ',')\n",
        "\n",
        "#################################################################################\n",
        "\n",
        "\n",
        "static_columns = ['is_weekend', 'timeframe1',\n",
        "       'timeframe2', 'timeframe3', 'timeframe4', 'timeframe5', 'cos_month',\n",
        "       'sin_month', 'cos_day', 'sin_day', 'cos_hour', 'sin_hour',\n",
        "       'year_normed', 'lat', 'lon', 'temperature', 'mm_precip']\n",
        "\n",
        "\n",
        "X_train_static = X_train[static_columns].values\n",
        "\n",
        "X_train_ts = np.stack((X_train[['temperature-1', 'mm_precip-1', 'ctx-1']].values,\n",
        "                       X_train[['temperature-2', 'mm_precip-2', 'ctx-2']].values,\n",
        "                       X_train[['temperature-3', 'mm_precip-3', 'ctx-3']].values,\n",
        "                       X_train[['temperature-4', 'mm_precip-4', 'ctx-4']].values),\n",
        "                       axis = 1)\n",
        "train_target = y_train[['percentage']].values\n",
        "\n",
        "#################################################################################\n",
        "\n",
        "X_val_static = X_val[static_columns].values\n",
        "\n",
        "X_val_ts = np.stack((X_val[['temperature-1', 'mm_precip-1', 'ctx-1']].values,\n",
        "                     X_val[['temperature-2', 'mm_precip-2', 'ctx-2']].values,\n",
        "                     X_val[['temperature-3', 'mm_precip-3', 'ctx-3']].values,\n",
        "                     X_val[['temperature-4', 'mm_precip-4', 'ctx-4']].values),\n",
        "                     axis = 1)\n",
        "val_target = y_val[['percentage']].values\n",
        "\n",
        "#################################################################################\n",
        "\n",
        "X_val24_static = X_val24[static_columns].values\n",
        "\n",
        "X_val24_ts = np.stack((X_val24[['temperature-1', 'mm_precip-1', 'ctx-1']].values,\n",
        "                       X_val24[['temperature-2', 'mm_precip-2', 'ctx-2']].values,\n",
        "                       X_val24[['temperature-3', 'mm_precip-3', 'ctx-3']].values,\n",
        "                       X_val24[['temperature-4', 'mm_precip-4', 'ctx-4']].values),\n",
        "                     axis = 1)\n",
        "val24_target = y_val24[['percentage']].values\n",
        "\n",
        "################################################################################\n",
        "\n",
        "\n",
        "X_sub_static = X_submission[static_columns].values\n",
        "\n",
        "X_sub_ts = np.stack((X_submission[['temperature-1', 'mm_precip-1', 'ctx-1']].values,\n",
        "                     X_submission[['temperature-2', 'mm_precip-2', 'ctx-2']].values,\n",
        "                     X_submission[['temperature-3', 'mm_precip-3', 'ctx-3']].values,\n",
        "                     X_submission[['temperature-4', 'mm_precip-4', 'ctx-4']].values),\n",
        "                     axis = 1)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Network construction\n",
        "\n",
        "To operate with the two groups of data, the nework starts off with 2 LSTM layers and a dense network that work in parallel and that process the temporal data and static data, respectively. After this, the outputs are concatenated and passed thorugh 3 final dense layers that lead to a single neuron with a sigmoid activation function that returns a value between 0 and 1.\n",
        "We have also added droupout layers to prevent overfitting (we will see that that is a problem with this model)"
      ],
      "metadata": {
        "id": "hMEf6XStVDsE"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pb4ZoKNN6e3G",
        "outputId": "fbdde482-a78c-458f-9b7d-a844d9513cea"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " static_input (InputLayer)      [(None, 17)]         0           []                               \n",
            "                                                                                                  \n",
            " dense (Dense)                  (None, 32)           576         ['static_input[0][0]']           \n",
            "                                                                                                  \n",
            " dropout_2 (Dropout)            (None, 32)           0           ['dense[0][0]']                  \n",
            "                                                                                                  \n",
            " dense_1 (Dense)                (None, 64)           2112        ['dropout_2[0][0]']              \n",
            "                                                                                                  \n",
            " ts_input (InputLayer)          [(None, 4, 3)]       0           []                               \n",
            "                                                                                                  \n",
            " dropout_3 (Dropout)            (None, 64)           0           ['dense_1[0][0]']                \n",
            "                                                                                                  \n",
            " lstm (LSTM)                    (None, 4, 64)        17408       ['ts_input[0][0]']               \n",
            "                                                                                                  \n",
            " dense_2 (Dense)                (None, 48)           3120        ['dropout_3[0][0]']              \n",
            "                                                                                                  \n",
            " dropout (Dropout)              (None, 4, 64)        0           ['lstm[0][0]']                   \n",
            "                                                                                                  \n",
            " dropout_4 (Dropout)            (None, 48)           0           ['dense_2[0][0]']                \n",
            "                                                                                                  \n",
            " lstm_1 (LSTM)                  (None, 64)           33024       ['dropout[0][0]']                \n",
            "                                                                                                  \n",
            " dense_3 (Dense)                (None, 32)           1568        ['dropout_4[0][0]']              \n",
            "                                                                                                  \n",
            " dropout_1 (Dropout)            (None, 64)           0           ['lstm_1[0][0]']                 \n",
            "                                                                                                  \n",
            " dropout_5 (Dropout)            (None, 32)           0           ['dense_3[0][0]']                \n",
            "                                                                                                  \n",
            " concatenate (Concatenate)      (None, 96)           0           ['dropout_1[0][0]',              \n",
            "                                                                  'dropout_5[0][0]']              \n",
            "                                                                                                  \n",
            " dense_4 (Dense)                (None, 32)           3104        ['concatenate[0][0]']            \n",
            "                                                                                                  \n",
            " dropout_6 (Dropout)            (None, 32)           0           ['dense_4[0][0]']                \n",
            "                                                                                                  \n",
            " dense_5 (Dense)                (None, 24)           792         ['dropout_6[0][0]']              \n",
            "                                                                                                  \n",
            " dropout_7 (Dropout)            (None, 24)           0           ['dense_5[0][0]']                \n",
            "                                                                                                  \n",
            " dense_6 (Dense)                (None, 24)           600         ['dropout_7[0][0]']              \n",
            "                                                                                                  \n",
            " dropout_8 (Dropout)            (None, 24)           0           ['dense_6[0][0]']                \n",
            "                                                                                                  \n",
            " dense_7 (Dense)                (None, 1)            25          ['dropout_8[0][0]']              \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 62,329\n",
            "Trainable params: 62,329\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "import keras\n",
        "from keras.layers import LSTM, Dense, Input, concatenate, Dropout, Bidirectional\n",
        "from keras.regularizers import l1, l2\n",
        "from keras.utils import plot_model\n",
        "\n",
        "\n",
        "# Construct eh neural network by separating it into two parts, one for the time series data and one for the static data\n",
        "# The time series data is fed into an LSTM layer, the static data is fed into a dense layer\n",
        "\n",
        "ts_input = tf.keras.Input(shape=(4, 3), name='ts_input')\n",
        "static_input = tf.keras.Input(shape = (17,), name='static_input')\n",
        "\n",
        "####################################################################################################\n",
        "\n",
        "LSTM1 = LSTM(64, activation='tanh', return_sequences=True)(ts_input)\n",
        "dropout_lstm = Dropout(0.1)(LSTM1)\n",
        "LSTM2 = LSTM(64, activation = 'tanh', return_sequences = False)(dropout_lstm)\n",
        "lstm_out = Dropout(0.1)(LSTM2)\n",
        "\n",
        "####################################################################################################\n",
        "\n",
        "static1 = Dense(32, activation='relu')(static_input)\n",
        "dropout_static = Dropout(0.1)(static1)\n",
        "static2 = Dense(64, activation = 'relu')(dropout_static)\n",
        "dropout_static2 = Dropout(0.1)(static2)\n",
        "static3 = Dense(48, activation = 'relu')(dropout_static2)\n",
        "dropout_static3 = Dropout(0.1)(static3)\n",
        "static4 = Dense(32, activation = 'relu')(dropout_static3)\n",
        "static_out = Dropout(0.1)(static4)\n",
        "\n",
        "####################################################################################################\n",
        "\n",
        "merged_out = concatenate([lstm_out, static_out]) # join the outputs of the LSTM and dense layers and pass them into a final neuron\n",
        "\n",
        "layer_out1 = Dense(32, activation = 'relu')(merged_out)\n",
        "dropout_out1 = Dropout(0.1)(layer_out1)\n",
        "layer_out2 = Dense(24, activation = 'relu')(dropout_out1)\n",
        "dropout_out2 = Dropout(0.1)(layer_out2)\n",
        "layer_out3 = Dense(24, activation = 'relu')(dropout_out2)\n",
        "dropout_out3 = Dropout(0.1)(layer_out3)\n",
        "out_result = Dense(1, activation='sigmoid')(dropout_out3)\n",
        "\n",
        "model = tf.keras.Model(inputs=[ts_input, static_input], outputs=out_result)\n",
        "\n",
        "model.compile(optimizer='adam', loss='mse', metrics=['mse'])\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_model(model, show_shapes = True, rankdir='TB')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ndPHZ3SAWSae",
        "outputId": "4d7191bd-1d78-4c23-99ad-1b4ac188f6c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G8VMZWbnAi1u",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e6b1475c-8597-4f02-eaca-e51e272d28c5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "36547/36547 [==============================] - 399s 11ms/step - loss: 0.0121 - mse: 0.0121 - val_loss: 0.0120 - val_mse: 0.0120\n",
            "Epoch 2/20\n",
            "36547/36547 [==============================] - 394s 11ms/step - loss: 0.0113 - mse: 0.0113 - val_loss: 0.0119 - val_mse: 0.0119\n",
            "Epoch 3/20\n",
            "36547/36547 [==============================] - 364s 10ms/step - loss: 0.0112 - mse: 0.0112 - val_loss: 0.0118 - val_mse: 0.0118\n",
            "Epoch 4/20\n",
            "36547/36547 [==============================] - 349s 10ms/step - loss: 0.0111 - mse: 0.0111 - val_loss: 0.0120 - val_mse: 0.0120\n",
            "Epoch 5/20\n",
            "36547/36547 [==============================] - 356s 10ms/step - loss: 0.0110 - mse: 0.0110 - val_loss: 0.0117 - val_mse: 0.0117\n",
            "Epoch 6/20\n",
            "36547/36547 [==============================] - 372s 10ms/step - loss: 0.0110 - mse: 0.0110 - val_loss: 0.0117 - val_mse: 0.0117\n",
            "Epoch 7/20\n",
            "36547/36547 [==============================] - 427s 12ms/step - loss: 0.0109 - mse: 0.0109 - val_loss: 0.0117 - val_mse: 0.0117\n",
            "Epoch 8/20\n",
            "36547/36547 [==============================] - 418s 11ms/step - loss: 0.0109 - mse: 0.0109 - val_loss: 0.0118 - val_mse: 0.0118\n",
            "Epoch 9/20\n",
            "36547/36547 [==============================] - 374s 10ms/step - loss: 0.0109 - mse: 0.0109 - val_loss: 0.0118 - val_mse: 0.0118\n",
            "Epoch 10/20\n",
            "36547/36547 [==============================] - 377s 10ms/step - loss: 0.0108 - mse: 0.0108 - val_loss: 0.0122 - val_mse: 0.0122\n",
            "Epoch 11/20\n",
            "36547/36547 [==============================] - 351s 10ms/step - loss: 0.0108 - mse: 0.0108 - val_loss: 0.0113 - val_mse: 0.0113\n",
            "Epoch 12/20\n",
            "36547/36547 [==============================] - 353s 10ms/step - loss: 0.0108 - mse: 0.0108 - val_loss: 0.0119 - val_mse: 0.0119\n",
            "Epoch 13/20\n",
            "36547/36547 [==============================] - 349s 10ms/step - loss: 0.0108 - mse: 0.0108 - val_loss: 0.0114 - val_mse: 0.0114\n",
            "Epoch 14/20\n",
            "36547/36547 [==============================] - 349s 10ms/step - loss: 0.0108 - mse: 0.0108 - val_loss: 0.0119 - val_mse: 0.0119\n",
            "Epoch 15/20\n",
            "36547/36547 [==============================] - 355s 10ms/step - loss: 0.0108 - mse: 0.0108 - val_loss: 0.0115 - val_mse: 0.0115\n",
            "Epoch 16/20\n",
            "36546/36547 [============================>.] - ETA: 0s - loss: 0.0108 - mse: 0.0108Restoring model weights from the end of the best epoch: 11.\n",
            "36547/36547 [==============================] - 345s 9ms/step - loss: 0.0108 - mse: 0.0108 - val_loss: 0.0118 - val_mse: 0.0118\n",
            "Epoch 16: early stopping\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f12842bb0d0>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "from keras.callbacks import EarlyStopping\n",
        "earlyStopping = EarlyStopping(monitor='val_loss', patience=5, verbose=1, mode='auto', restore_best_weights = True)\n",
        "\n",
        "# Now fit the model to the data, obtaining pretty good results, save the model to a file and then predict on the X_submission data\n",
        "\n",
        "model.fit(x = [X_train_ts, X_train_static],\n",
        "          y = train_target,\n",
        "          batch_size = 64,\n",
        "          epochs = 20,\n",
        "          validation_data=([X_val24_ts, X_val24_static], val24_target), # keras only supports one set of validation data, so we use the one that\n",
        "                                                                        # contains all 24h, taht resembles the most the submission set\n",
        "          callbacks=[earlyStopping],\n",
        "          shuffle=True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost import plot_importance\n",
        "from matplotlib import pyplot\n",
        "\n",
        "pyplot.plot(model.history.history['loss'])\n",
        "pyplot.plot(model.history.history['val_loss'])\n",
        "pyplot.title('model loss')\n",
        "pyplot.ylabel('loss')\n",
        "pyplot.xlabel('epoch')\n",
        "pyplot.legend(['train', 'val'], loc='upper left')\n",
        "pyplot.savefig('/content/drive/MyDrive/CapstoneProject/plots and figures/lstm_loss.png')\n",
        "pyplot.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Ge2SIPopXgaK",
        "outputId": "af0efb17-de5f-44a0-bf99-d55307f115a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "static_columns = ['is_weekend', 'timeframe1',\n",
        "       'timeframe2', 'timeframe3', 'timeframe4', 'timeframe5', 'cos_month',\n",
        "       'sin_month', 'cos_day', 'sin_day', 'cos_hour', 'sin_hour',\n",
        "       'year_normed', 'lat', 'lon', 'temperature', 'mm_precip']\n",
        "\n",
        "weights = model.get_weights()\n",
        "\n",
        "# Calculate feature importance based on the weights\n",
        "feature_importance = np.sort(np.mean(np.abs(weights[0]), axis=1))\n",
        "\n",
        "# Print the importance of each feature\n",
        "for i, importance in enumerate(feature_importance):\n",
        "    print(f\"{static_columns[i]}: {importance}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-hYCoZm_Yzrv",
        "outputId": "1b6f8f42-8a70-4678-8f72-6ac9612156ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "is_weekend: 0.04162748157978058\n",
            "timeframe1: 0.04198416694998741\n",
            "timeframe2: 0.13254521787166595\n",
            "timeframe3: 0.14563903212547302\n",
            "timeframe4: 0.15736523270606995\n",
            "timeframe5: 0.28035128116607666\n",
            "cos_month: 0.2895676791667938\n",
            "sin_month: 0.3056214451789856\n",
            "cos_day: 0.3103480041027069\n",
            "sin_day: 0.3388426899909973\n",
            "cos_hour: 0.38680586218833923\n",
            "sin_hour: 0.4838072955608368\n",
            "year_normed: 0.5267024040222168\n",
            "lat: 0.6065153479576111\n",
            "lon: 0.69236820936203\n",
            "temperature: 1.2320904731750488\n",
            "mm_precip: 1.4689390659332275\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Again, as with the more complex xgboost model, we see quite a bit of overfitting. On the other hand though, the model seems to prioritize weather quite a bit, which is something that we expected would be important but that the other two models didn't pay much attention to it."
      ],
      "metadata": {
        "id": "OTse9r6A6D3T"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rh5u2O1QOwi1"
      },
      "outputs": [],
      "source": [
        "model.save('/content/drive/MyDrive/CapstoneProject/models/keras/lstm.h5')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.models.load_model('/content/drive/MyDrive/CapstoneProject/models/keras/lstm.h5')"
      ],
      "metadata": {
        "id": "RTe0TqePaTf8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dPHhvgcyP1MY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c1514af3-7943-40c6-adfc-1953a709dfc8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1719/1719 [==============================] - 6s 3ms/step\n"
          ]
        }
      ],
      "source": [
        "y_pred = model.predict([X_sub_ts, X_sub_static])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-oPIvKSrRSeZ"
      },
      "outputs": [],
      "source": [
        "prediction = pd.DataFrame({'percentage_docks_available':y_pred.flatten()})\n",
        "prediction = prediction.reset_index()\n",
        "prediction.to_csv('/content/drive/MyDrive/CapstoneProject/models/keras/lstm_pred.csv', index = False)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We get reall similar results to that of the xgb model --> Further improvements should come from the hand of a better preproccesing probably, working with outliers and incorrect data in a more detailed way\n"
      ],
      "metadata": {
        "id": "QlC5Cs6Lm0_r"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Heatmap and buffer analysis**\n",
        "### This section consists in two parts:\n",
        "\n",
        "\n",
        "*   Creation of a dinamic heat map of dock availability that can be changed based on the month and year parameter. This will allow us to understand the pattern of bicycle usage across the city.  \n",
        "*   Performance of a buffer analysis. Here we are going to search for areas that are near the bike lanes but do not have a nearby bike station to detect potential zones where new bike stations could be installed. The buffer has been set in 500 meters based on the size of the city and the number of stations.\n",
        "\n"
      ],
      "metadata": {
        "id": "qgvkZ1MEyIEd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Heatmap ##\n",
        "\n",
        "The following code is designed to generate and display the dinamic heatmap that represents the average availability of bike docks across the city, given a specific month and year."
      ],
      "metadata": {
        "id": "cokFBcSeoQR8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install dash dash_core_components dash_html_components jupyter_dash\n",
        "!pip install geopandas shapely"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qPhFmW8FytS0",
        "outputId": "f17fbd09-dbcb-433a-89c2-f091142210ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting dash\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import folium\n",
        "from folium.plugins import HeatMap\n",
        "import pandas as pd\n",
        "import json\n",
        "import numpy as np\n",
        "import dask.dataframe as dd\n",
        "import dash\n",
        "from dash.dependencies import Input, Output\n",
        "from dash import dcc as dcc\n",
        "from dash import html as html\n",
        "import base64\n",
        "import os\n",
        "from flask import Flask\n",
        "from io import BytesIO\n",
        "from jupyter_dash import JupyterDash\n",
        "import geopandas as gpd\n",
        "from shapely.geometry import LineString\n",
        "from shapely.geometry import Point, Polygon\n",
        "import geopy.distance\n",
        "import math"
      ],
      "metadata": {
        "id": "K96Lgy0Gz5sm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "############################################################\n",
        "# Create a joined dataframe of the historical data used for train and test that will be used for the heatmap analysis\n",
        "df_info = pd.read_csv('/content/drive/MyDrive/CapstoneProject/df_info.csv')"
      ],
      "metadata": {
        "id": "pSIpaDq9x5RB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_info.head()"
      ],
      "metadata": {
        "id": "8YopdCu70wy5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "d9442604-4655-4850-de33-7216179b0b3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   index  station_id        lat       lon    year  month   day  hour  \\\n",
              "0      0       444.0  41.371730  2.155405  2022.0   10.0   7.0  14.0   \n",
              "1      1        14.0  41.384844  2.185085  2022.0    2.0  16.0  11.0   \n",
              "2      2        23.0  41.392466  2.171740  2022.0    7.0  29.0  12.0   \n",
              "3      3        58.0  41.382680  2.167120  2022.0    3.0  17.0  23.0   \n",
              "4      4       480.0  41.433326  2.162926  2021.0   12.0   4.0  13.0   \n",
              "\n",
              "      ctx-4     ctx-3     ctx-2     ctx-1  percentage  average_percentage  \n",
              "0  0.262346  0.050505  0.059829  0.010101    0.079772            0.092511  \n",
              "1  0.669753  0.589226  0.512821  0.629630    0.629630            0.606212  \n",
              "2  0.515432  0.484568  0.299383  0.290123    0.475309            0.412963  \n",
              "3  0.457364  0.515856  0.588551  0.403101    0.401163            0.473207  \n",
              "4  0.428571  0.450549  0.497024  0.500000    0.500000            0.475229  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>station_id</th>\n",
              "      <th>lat</th>\n",
              "      <th>lon</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>hour</th>\n",
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              "      <th>ctx-3</th>\n",
              "      <th>ctx-2</th>\n",
              "      <th>ctx-1</th>\n",
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              "      <th>average_percentage</th>\n",
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              "      <td>0.079772</td>\n",
              "      <td>0.092511</td>\n",
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              "      <td>2.185085</td>\n",
              "      <td>2022.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.669753</td>\n",
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              "      <td>0.629630</td>\n",
              "      <td>0.606212</td>\n",
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              "      <th>2</th>\n",
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              "      <td>23.0</td>\n",
              "      <td>41.392466</td>\n",
              "      <td>2.171740</td>\n",
              "      <td>2022.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0.515432</td>\n",
              "      <td>0.484568</td>\n",
              "      <td>0.299383</td>\n",
              "      <td>0.290123</td>\n",
              "      <td>0.475309</td>\n",
              "      <td>0.412963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>58.0</td>\n",
              "      <td>41.382680</td>\n",
              "      <td>2.167120</td>\n",
              "      <td>2022.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0.457364</td>\n",
              "      <td>0.515856</td>\n",
              "      <td>0.588551</td>\n",
              "      <td>0.403101</td>\n",
              "      <td>0.401163</td>\n",
              "      <td>0.473207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>480.0</td>\n",
              "      <td>41.433326</td>\n",
              "      <td>2.162926</td>\n",
              "      <td>2021.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.428571</td>\n",
              "      <td>0.450549</td>\n",
              "      <td>0.497024</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.475229</td>\n",
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              "  </tbody>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d57a7037-7240-4c4e-92c5-fecc1f540333')\"\n",
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              "    .colab-df-convert {\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d57a7037-7240-4c4e-92c5-fecc1f540333');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_info.shape"
      ],
      "metadata": {
        "id": "aGllOH_aNhA3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b2e49c47-c079-4c2a-8920-63f0695816e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11892390, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "years = [i for i in range(2019, 2024)]\n",
        "months = [i for i in range(1, 13)]"
      ],
      "metadata": {
        "id": "dMAT1wj2A9Cu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "First we are going to calculate the average percentage of bike availability at each station using the historical data. then we will generate a heatmap based on this data, with coordinates of each station and a color intensity indicating the level of bike availability. This heatmap is overlaid onto the city map created using Folium, providing a clear visualization of bike availability in different areas of the city.\n",
        "\n",
        "The resulting map is saved as an HTML file in a temporary location and then the interface powered by Dash includes dropdown menus for selecting the month and year of interest. The map updates dynamically based on these selections, providing a tool for exploring dock availability across different times."
      ],
      "metadata": {
        "id": "krauCkWdrlwU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dash definition\n",
        "app = JupyterDash(__name__)\n",
        "\n",
        "# Function to generate the heatmap\n",
        "def generate_heatmap(year, month):\n",
        "    selected_info = df_info[(df_info['year'] == float(year)) & (df_info['month'] == float(month))]\n",
        "    print(\"Selected info:\")\n",
        "    print(selected_info.head())\n",
        "\n",
        "    selected_info['average_percentage'] = selected_info[['percentage', 'ctx-4', 'ctx-3', 'ctx-2', 'ctx-1']].mean(axis=1)\n",
        "    average_availability = selected_info.groupby('station_id').mean().reset_index()\n",
        "    print(average_availability.head())# average percentage of bike availability at each station\n",
        "\n",
        "    # Create data for the heatmap (latitude, longitude, availability)\n",
        "    heatmap_data = [(row['lat'], row['lon'], 1 - row['average_percentage']) for idx, row in average_availability.iterrows()]\n",
        "    print(heatmap_data[:5])\n",
        "\n",
        "    # Create a folium map centered around the city\n",
        "    folium_map = folium.Map(location=[41.3870154, 2.1700471], zoom_start=13)\n",
        "\n",
        "\n",
        "# Load the bike lane data from a GeoJSON file and add it to the map\n",
        "    with open('/content/drive/MyDrive/CapstoneProject/maps/CARRIL_BICI.geojson') as f:\n",
        "        bikelane_data = json.load(f)\n",
        "    folium.GeoJson(bikelane_data).add_to(folium_map)\n",
        "\n",
        "    HeatMap(heatmap_data).add_to(folium_map)\n",
        "\n",
        "    # Map saved as html\n",
        "    folium_map.save('/tmp/temp_map.html')\n",
        "\n",
        "    # Copy in Google drive\n",
        "    !cp /tmp/temp_map.html /content/drive/MyDrive/CapstoneProject/maps/\n",
        "\n",
        "# Initial map for the first year and month\n",
        "generate_heatmap(years[0], months[0])\n",
        "\n",
        "# Dash Layout and dropdowns\n",
        "app.layout = html.Div([\n",
        "    html.H1(\"Disponibilidad de estaciones de Bicing\"),\n",
        "    html.H2(id='map-title', children=\"Disponibilidad: Mes/Año\"),\n",
        "    dcc.Dropdown(\n",
        "        id='year-dropdown',\n",
        "        options=[{'label': str(year), 'value': year} for year in years],\n",
        "        value=years[0]\n",
        "    ),\n",
        "    dcc.Dropdown(\n",
        "        id='month-dropdown',\n",
        "        options=[{'label': str(month), 'value': month} for month in months],\n",
        "        value=months[0]\n",
        "    ),\n",
        "    html.Iframe(id = 'map', srcDoc = open('/content/drive/MyDrive/CapstoneProject/maps/temp_map.html', 'r').read(), width = '100%', height = '600')\n",
        "])\n",
        "\n",
        "# Callback to update the map and the subtitle based on the selected year and month\n",
        "@app.callback(\n",
        "    [Output('map', 'srcDoc'),\n",
        "    Output('map-title', 'children')],\n",
        "    [Input('year-dropdown', 'value'),\n",
        "    Input('month-dropdown', 'value')])\n",
        "def update_map(year, month):\n",
        "    generate_heatmap(year, month)\n",
        "    return open('/content/drive/MyDrive/CapstoneProject/maps/temp_map.html', 'r').read(), f'Disponibilidad: {month}/{year}'\n",
        "\n",
        "if __name__ == '__main__':\n",
        "\n",
        "   app.run_server(debug=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 845
        },
        "id": "UcnY7ZtATaA-",
        "outputId": "22073a69-6971-4e72-d80d-060992174748"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selected info:\n",
            "Empty DataFrame\n",
            "Columns: [index, station_id, lat, lon, year, month, day, hour, ctx-4, ctx-3, ctx-2, ctx-1, percentage, average_percentage]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [station_id, index, lat, lon, year, month, day, hour, ctx-4, ctx-3, ctx-2, ctx-1, percentage, average_percentage]\n",
            "Index: []\n",
            "[]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "(async (port, path, width, height, cache, element) => {\n",
              "    if (!google.colab.kernel.accessAllowed && !cache) {\n",
              "      return;\n",
              "    }\n",
              "    element.appendChild(document.createTextNode(''));\n",
              "    const url = await google.colab.kernel.proxyPort(port, {cache});\n",
              "    const iframe = document.createElement('iframe');\n",
              "    iframe.src = new URL(path, url).toString();\n",
              "    iframe.height = height;\n",
              "    iframe.width = width;\n",
              "    iframe.style.border = 0;\n",
              "    iframe.allow = [\n",
              "        'accelerometer',\n",
              "        'autoplay',\n",
              "        'camera',\n",
              "        'clipboard-read',\n",
              "        'clipboard-write',\n",
              "        'gyroscope',\n",
              "        'magnetometer',\n",
              "        'microphone',\n",
              "        'serial',\n",
              "        'usb',\n",
              "        'xr-spatial-tracking',\n",
              "    ].join('; ');\n",
              "    element.appendChild(iframe);\n",
              "  })(8050, \"/\", \"100%\", 650, false, window.element)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dash app running on:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "(async (port, path, text, element) => {\n",
              "    if (!google.colab.kernel.accessAllowed) {\n",
              "      return;\n",
              "    }\n",
              "    element.appendChild(document.createTextNode(''));\n",
              "    const url = await google.colab.kernel.proxyPort(port);\n",
              "    const anchor = document.createElement('a');\n",
              "    anchor.href = new URL(path, url).toString();\n",
              "    anchor.target = '_blank';\n",
              "    anchor.setAttribute('data-href', url + path);\n",
              "    anchor.textContent = text;\n",
              "    element.appendChild(anchor);\n",
              "  })(8050, \"/\", \"http://127.0.0.1:8050/\", window.element)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Remarks**: If we compare the different periods, we can see that in general, the downtown is the area where there is a major use of bicycle and therefore less availability."
      ],
      "metadata": {
        "id": "KMimzryp2fBq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Buffer Analysis ##\n",
        "\n",
        "The following code produces a heatmap representing the average availability of bikes at different stations in march 2023 including the buffer analysis around bike lanes."
      ],
      "metadata": {
        "id": "V7U7yF7yu3Xb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_info_mar23 = df_info.loc[df_info['year'] == 2023]"
      ],
      "metadata": {
        "id": "Pbz4Zd29oOyj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_info_mar23.head()"
      ],
      "metadata": {
        "id": "wEBC5naIpQH4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "b85a3d24-283c-42f0-98c2-5fd18943b2ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    index  station_id        lat       lon    year  month  day  hour  \\\n",
              "0   60701        43.0  41.406386  2.187440  2023.0    1.0  1.0   0.0   \n",
              "1  115459       441.0  41.375443  2.139841  2023.0    1.0  1.0   0.0   \n",
              "2  158776       284.0  41.389462  2.131495  2023.0    1.0  1.0   0.0   \n",
              "0    3970       379.0  41.382933  2.142070  2023.0    1.0  1.0   0.0   \n",
              "1   23567       189.0  41.396984  2.166185  2023.0    1.0  1.0   0.0   \n",
              "\n",
              "      ctx-4     ctx-3     ctx-2     ctx-1  percentage  \n",
              "0  0.802469  0.802469  0.827160  0.793210    0.814815  \n",
              "1  0.726852  0.629630  0.504630  0.500000    0.537037  \n",
              "2  0.873457  0.885802  0.962963  0.962963    0.962963  \n",
              "0  0.797619  0.761905  0.730159  0.714286    0.761905  \n",
              "1  0.562500  0.604167  0.595238  0.625000    0.738095  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b80a9b8b-46a5-43b8-9f55-007050b81b36\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>station_id</th>\n",
              "      <th>lat</th>\n",
              "      <th>lon</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>hour</th>\n",
              "      <th>ctx-4</th>\n",
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              "      <th>0</th>\n",
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              "      <td>43.0</td>\n",
              "      <td>41.406386</td>\n",
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              "      <td>2023.0</td>\n",
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              "      <td>0.793210</td>\n",
              "      <td>0.814815</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>115459</td>\n",
              "      <td>441.0</td>\n",
              "      <td>41.375443</td>\n",
              "      <td>2.139841</td>\n",
              "      <td>2023.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.726852</td>\n",
              "      <td>0.629630</td>\n",
              "      <td>0.504630</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.537037</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>158776</td>\n",
              "      <td>284.0</td>\n",
              "      <td>41.389462</td>\n",
              "      <td>2.131495</td>\n",
              "      <td>2023.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.873457</td>\n",
              "      <td>0.885802</td>\n",
              "      <td>0.962963</td>\n",
              "      <td>0.962963</td>\n",
              "      <td>0.962963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3970</td>\n",
              "      <td>379.0</td>\n",
              "      <td>41.382933</td>\n",
              "      <td>2.142070</td>\n",
              "      <td>2023.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.797619</td>\n",
              "      <td>0.761905</td>\n",
              "      <td>0.730159</td>\n",
              "      <td>0.714286</td>\n",
              "      <td>0.761905</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>23567</td>\n",
              "      <td>189.0</td>\n",
              "      <td>41.396984</td>\n",
              "      <td>2.166185</td>\n",
              "      <td>2023.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.562500</td>\n",
              "      <td>0.604167</td>\n",
              "      <td>0.595238</td>\n",
              "      <td>0.625000</td>\n",
              "      <td>0.738095</td>\n",
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            ]
          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "First, we define a function function to calculate the geographical distance between two points on Earth given their latitude and longitude. This function will be later used in the buffer analysis"
      ],
      "metadata": {
        "id": "la9uD-GL2qgd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from math import radians, cos, sin, asin, sqrt\n",
        "\n",
        "def calculate_distance(lat1, lon1, lat2, lon2):\n",
        "\n",
        "    # Convert coordinates from degrees to radians\n",
        "    lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])\n",
        "\n",
        "    # haversine\n",
        "    dlon = lon2 - lon1\n",
        "    dlat = lat2 - lat1\n",
        "    a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2\n",
        "    c = 2 * asin(sqrt(a))\n",
        "    r = 6371  # Earth radius in km\n",
        "\n",
        "    return c * r * 1000  # Convert to meters"
      ],
      "metadata": {
        "id": "9vftyG19vSvz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, we compute the average availability of bikes at each station for march 2023. This data is used to create a list of tuples where each tuple contains the latitude, longitude, and availability of a bike station. The bike availability is subtracted from 1 to display lack of availability in the heatmap.\n",
        "\n",
        "A base map of the city is created using Folium and the bike lanes data is loaded from a GeoJSON file and added to the map.The heatmap is added to the map using the previously computed bike station data.\n",
        "\n",
        "The script then performs the buffer analysis. It iterates through each bike lane segment and checks if it's more than 500 meters away from any bike station. If it is, a marker is added to that point on the map. This analysis helps identify underserved areas where additional bike stations might be needed. Then is saved as an html."
      ],
      "metadata": {
        "id": "8AruZPos2ux9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Average Pct column\n",
        "df_info['average_percentage'] = df_info[['percentage', 'ctx-4', 'ctx-3', 'ctx-2', 'ctx-1']].mean(axis=1)\n",
        "\n",
        "# Select year and month info\n",
        "selected_info = df_info[(df_info['year'] == 2023) & (df_info['month'] == 3)]\n",
        "\n",
        "# average availability of each station\n",
        "average_availability = selected_info.groupby('station_id').mean().reset_index()\n",
        "\n",
        "# heatmap data\n",
        "heatmap_data = [(row['lat'], row['lon'], 1 - row['average_percentage']) for idx, row in average_availability.iterrows()]\n",
        "\n",
        "# Map creation\n",
        "folium_map = folium.Map(location=[41.3870154, 2.1700471], zoom_start=13)\n",
        "\n",
        "# Bikelanes\n",
        "with open(\"/content/drive/MyDrive/CapstoneProject/maps/CARRIL_BICI.geojson\") as f:\n",
        "    bikelane_data = json.load(f)\n",
        "folium.GeoJson(bikelane_data).add_to(folium_map)\n",
        "\n",
        "\n",
        "HeatMap(heatmap_data).add_to(folium_map)\n",
        "\n",
        "# Buffer analysis\n",
        "for feature in bikelane_data['features']:\n",
        "    for i in range(len(feature['geometry']['coordinates']) - 1):\n",
        "        start = feature['geometry']['coordinates'][i]\n",
        "        end = feature['geometry']['coordinates'][i + 1]\n",
        "\n",
        "        num_points = round(calculate_distance(start[1], start[0], end[1], end[0]) / 100)\n",
        "        lats = np.linspace(start[1], end[1], num_points)\n",
        "        lons = np.linspace(start[0], end[0], num_points)\n",
        "\n",
        "        for lat, lon in zip(lats, lons):\n",
        "            min_distance = min(calculate_distance(lat, lon, station_lat, station_lon) for station_lat, station_lon in zip(average_availability['lat'], average_availability['lon']))\n",
        "            if min_distance > 500:  # 500 meters buffer\n",
        "                folium.Marker([lat, lon]).add_to(folium_map)\n",
        "\n",
        "\n",
        "folium_map.save('/content/drive/MyDrive/CapstoneProject/maps/temp_map_no_mark23.html')"
      ],
      "metadata": {
        "id": "DKIe_0ZYumZE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Remarks: With this buffer analysis we can see that there are several areas in the city that might need a station, such as Zona franca, the port of Barcelona or near Sant Adria de Besos."
      ],
      "metadata": {
        "id": "2LaJQWYW9Kmb"
      }
    }
  ],
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