Multimodal_experiements_Özdemir_Schwabl_share.ipynb

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  "metadata": {
    "colab": {
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Import Packages"
      ],
      "metadata": {
        "id": "pzjc_LqCfn8q"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v4NgAAo6fZvO",
        "outputId": "2930ec1a-f517-4395-fd13-9d9ed959e9ee"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Collecting git+https://github.com/facebookresearch/detectron2.git\n",
            "  Cloning https://github.com/facebookresearch/detectron2.git to /tmp/pip-req-build-d4m13zdw\n",
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          ]
        }
      ],
      "source": [
        "import torch\n",
        "torch.__version__\n",
        "\n",
        "assert torch.cuda.is_available()\n",
        "\n",
        "# See https://detectron2.readthedocs.io/tutorials/install.html for instructions\n",
        "\n",
        "# %%capture\n",
        "!pip install pyyaml==5.1\n",
        "!python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'\n",
        "!pip install tensorflow_addons\n",
        "!pip install transformers\n",
        "\n",
        "\n",
        "import torchvision\n",
        "import matplotlib.pyplot as plt\n",
        "import json\n",
        "import cv2\n",
        "import numpy as np\n",
        "from copy import deepcopy\n",
        "import gc\n",
        "import os\n",
        "from getpass import getpass\n",
        "import urllib\n",
        "import pandas as pd\n",
        "\n",
        "\n",
        "import tensorflow_addons as tfa\n",
        "import tensorflow as tf\n",
        "\n",
        "\n",
        "from transformers import BertTokenizer, VisualBertModel, VisualBertForPreTraining, VisualBertForQuestionAnswering, AutoTokenizer\n",
        "\n",
        "\n",
        "from keras.models import Sequential\n",
        "from keras import layers\n",
        "from keras import metrics\n",
        "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "\n",
        "from sklearn import metrics\n",
        "from sklearn import svm\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.utils import resample\n",
        "from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix, classification_report\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "\n",
        "from detectron2.modeling import build_model\n",
        "from detectron2.checkpoint import DetectionCheckpointer\n",
        "from detectron2.structures.image_list import ImageList\n",
        "from detectron2.data import transforms as T\n",
        "from detectron2.modeling.box_regression import Box2BoxTransform\n",
        "# from detectron2.modeling.roi_heads.fast_rcnn import FastRCNNOutputs\n",
        "from detectron2.structures.boxes import Boxes\n",
        "from detectron2.layers import nms\n",
        "from detectron2 import model_zoo\n",
        "from detectron2.config import get_cfg\n",
        "\n",
        "plt.rcParams['figure.dpi'] = 150"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load in Data"
      ],
      "metadata": {
        "id": "yBkW5kkFnPSP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "import re\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "-3oGh1KcV5G_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "255716b1-9391-4b7f-dbc9-f393f5f00e05"
      },
      "execution_count": null,
      "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_feather(\"/content/drive/MyDrive/multimodal/DL_data.feather\")"
      ],
      "metadata": {
        "id": "IJmY4Bg7Sq48"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "id": "PbVsDKthXtU1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "0a897013-7d8d-45a9-f51b-8bf596d7bc5a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "                                                  text  \\\n",
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              "898     http://pbs.twimg.com/media/D8ywUFnXUAA-1PI.jpg   en  \n",
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              "      <th>0</th>\n",
              "      <td>1194204999078875137</td>\n",
              "      <td>https://twitter.com/EU_Finance/status/11942049...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>EU_Finance</td>\n",
              "      <td>The exchange of ideas continues at today's #EU...</td>\n",
              "      <td>http://pbs.twimg.com/media/EJKrHGBWoAA1yd0.jpg</td>\n",
              "      <td>en</td>\n",
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              "      <th>1</th>\n",
              "      <td>1202605773957844992</td>\n",
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              "      <td>en</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1222801468807360513</td>\n",
              "      <td>https://twitter.com/BaldwinMatthew_/status/122...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>BaldwinMatthew_</td>\n",
              "      <td>Pls read/retweet this important report from @E...</td>\n",
              "      <td>http://pbs.twimg.com/media/EPhDdAvWoAEVdLv.jpg</td>\n",
              "      <td>en</td>\n",
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              "      <th>3</th>\n",
              "      <td>1280416638802767872</td>\n",
              "      <td>https://twitter.com/EU_Commission/status/12804...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>EU_Commission</td>\n",
              "      <td>The #coronavirus has claimed more than half a ...</td>\n",
              "      <td>http://pbs.twimg.com/media/EcTzgpVXgAAuXAz.jpg</td>\n",
              "      <td>en</td>\n",
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              "      <th>4</th>\n",
              "      <td>1227338517287751685</td>\n",
              "      <td>https://twitter.com/dreynders/status/122733851...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>dreynders</td>\n",
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              "      <th>895</th>\n",
              "      <td>1275414642731429889</td>\n",
              "      <td>https://twitter.com/ESMAComms/status/127541464...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>ESMAComms</td>\n",
              "      <td>.@VDombrovskis: When we come w/ our proposal o...</td>\n",
              "      <td>http://pbs.twimg.com/media/EbMqx7BXkAEt2D-.jpg</td>\n",
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              "      <th>897</th>\n",
              "      <td>1214881110963998720</td>\n",
              "      <td>https://twitter.com/vonderleyen/status/1214881...</td>\n",
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              "      <td>vonderleyen</td>\n",
              "      <td>But we are ready to design a new partnership w...</td>\n",
              "      <td>http://pbs.twimg.com/media/ENwf7iGWkAARWa1.jpg</td>\n",
              "      <td>en</td>\n",
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              "      <th>898</th>\n",
              "      <td>1202967402838974464</td>\n",
              "      <td>https://twitter.com/DSMeu/status/1202967402838...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>DSMeu</td>\n",
              "      <td>Hyvää itsenäisyyspäivää Suomi! #Finland is bes...</td>\n",
              "      <td>http://pbs.twimg.com/media/D8ywUFnXUAA-1PI.jpg</td>\n",
              "      <td>en</td>\n",
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              "      <td>http://pbs.twimg.com/media/EaAJD5KWkAIi1FM.png</td>\n",
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        "data = data.sort_values(by=['status_id'])"
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              "               status_id                                            V101_02  \\\n",
              "347  1183662782589427712  https://twitter.com/DSMeu/status/1183662782589...   \n",
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              "..                   ...                                                ...   \n",
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              "48   1288807236769382400  https://twitter.com/EURightsAgency/status/1288...   \n",
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              "     V301_02     screen_name  \\\n",
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              "..       ...             ...   \n",
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              "130      1.0   GabrielMariya   \n",
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              "                                                  text  \\\n",
              "347  And the winner of the European Cyber Security ...   \n",
              "764  Our latest Free Trade Agreement implementation...   \n",
              "296  Let's start looking at what should be our prio...   \n",
              "172  What matters most to you? What 𝙧𝙚𝙖𝙡𝙡𝙮 matters ...   \n",
              "472  “I come from an area with lots of electricity ...   \n",
              "..                                                 ...   \n",
              "263  #EUStateAid #coronavirus #StrongerTogether Com...   \n",
              "704  Friendship is a priceless gift, a feeling, abl...   \n",
              "164  Dreaming of a cleaner Baltic Sea? 🌊🌊🌊 So do we...   \n",
              "48   Want to know how controlling #COVID19 has affe...   \n",
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              "472     http://pbs.twimg.com/media/EG6LO_1WoAIo7if.jpg   en  \n",
              "..                                                 ...  ...  \n",
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              "      <th>764</th>\n",
              "      <td>1183741219274481667</td>\n",
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              "      <td>Trade_EU</td>\n",
              "      <td>Our latest Free Trade Agreement implementation...</td>\n",
              "      <td>http://pbs.twimg.com/amplify_video_thumb/11837...</td>\n",
              "      <td>en</td>\n",
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              "      <th>296</th>\n",
              "      <td>1183787259134386176</td>\n",
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              "      <th>172</th>\n",
              "      <td>1183993915675824128</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>EU_Commission</td>\n",
              "      <td>What matters most to you? What 𝙧𝙚𝙖𝙡𝙡𝙮 matters ...</td>\n",
              "      <td>http://pbs.twimg.com/media/EG5kMHYUUAEpIMT.jpg</td>\n",
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              "      <th>472</th>\n",
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              "      <td>1.0</td>\n",
              "      <td>eu_echo</td>\n",
              "      <td>“I come from an area with lots of electricity ...</td>\n",
              "      <td>http://pbs.twimg.com/media/EG6LO_1WoAIo7if.jpg</td>\n",
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              "      <th>263</th>\n",
              "      <td>1288782299090358277</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>EU_Competition</td>\n",
              "      <td>#EUStateAid #coronavirus #StrongerTogether Com...</td>\n",
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              "      <td>en</td>\n",
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              "      <th>704</th>\n",
              "      <td>1288782490644160517</td>\n",
              "      <td>https://twitter.com/GabrielMariya/status/12887...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>GabrielMariya</td>\n",
              "      <td>Friendship is a priceless gift, a feeling, abl...</td>\n",
              "      <td>http://pbs.twimg.com/media/EeKs18PX0AA-v_b.jpg</td>\n",
              "      <td>en</td>\n",
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              "    <tr>\n",
              "      <th>164</th>\n",
              "      <td>1288793253286993920</td>\n",
              "      <td>https://twitter.com/VSinkevicius/status/128879...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>VSinkevicius</td>\n",
              "      <td>Dreaming of a cleaner Baltic Sea? 🌊🌊🌊 So do we...</td>\n",
              "      <td>http://pbs.twimg.com/media/EeK2oMxWsAEX4t8.jpg</td>\n",
              "      <td>en</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>1288807236769382400</td>\n",
              "      <td>https://twitter.com/EURightsAgency/status/1288...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>EURightsAgency</td>\n",
              "      <td>Want to know how controlling #COVID19 has affe...</td>\n",
              "      <td>http://pbs.twimg.com/media/EeKpQGgXkAIy1Mv.jpg</td>\n",
              "      <td>en</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>1288851013965959169</td>\n",
              "      <td>https://twitter.com/GabrielMariya/status/12888...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>GabrielMariya</td>\n",
              "      <td>Monastery, prison, lighthouse, pirates &amp;amp; s...</td>\n",
              "      <td>http://pbs.twimg.com/media/EeLrKi6XgAANPP6.jpg</td>\n",
              "      <td>en</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>898 rows × 7 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0265b522-da58-4fcc-8ab3-aa73857bf384')\"\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-0265b522-da58-4fcc-8ab3-aa73857bf384');\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": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels = data[\"V301_02\"].to_numpy()\n",
        "labels = torch.Tensor(labels).int()"
      ],
      "metadata": {
        "id": "OjKzOY1L6NW4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tweets = data[\"text\"].to_list()"
      ],
      "metadata": {
        "id": "GgPzPBFg-WPK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sum(labels.numpy())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c9dvAg_FlUyN",
        "outputId": "290b62c0-7ced-4150-fca7-211317ffcc9d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "479"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(tweets)"
      ],
      "metadata": {
        "id": "uNeAuD2t-bQS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5de44b40-4906-4b58-ee8c-63c09f819f4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "898"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Define functions"
      ],
      "metadata": {
        "id": "9_xyNWDynqoS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Copyright (c) Facebook, Inc. and its affiliates.\n",
        "\n",
        "# they removed this part from the detectron2 codebase (we need the FastRCNNOutputs class)\n",
        "# however thanks to github nothing is ever gone\n",
        "# from here: https://github.com/facebookresearch/detectron2/commit/11528ce#\n",
        "import logging\n",
        "from typing import Dict, List, Tuple, Union\n",
        "import torch\n",
        "from fvcore.nn import giou_loss, smooth_l1_loss\n",
        "from torch import nn\n",
        "from torch.nn import functional as F\n",
        "from detectron2.config import configurable\n",
        "from detectron2.layers import ShapeSpec, batched_nms, cat, cross_entropy, nonzero_tuple\n",
        "from detectron2.modeling.box_regression import Box2BoxTransform\n",
        "from detectron2.structures import Boxes, Instances\n",
        "from detectron2.utils.events import get_event_storage\n",
        "__all__ = [\"fast_rcnn_inference\", \"FastRCNNOutputLayers\"]\n",
        "logger = logging.getLogger(__name__)\n",
        "\"\"\"\n",
        "Shape shorthand in this module:\n",
        "    N: number of images in the minibatch\n",
        "    R: number of ROIs, combined over all images, in the minibatch\n",
        "    Ri: number of ROIs in image i\n",
        "    K: number of foreground classes. E.g.,there are 80 foreground classes in COCO.\n",
        "Naming convention:\n",
        "    deltas: refers to the 4-d (dx, dy, dw, dh) deltas that parameterize the box2box\n",
        "    transform (see :class:`box_regression.Box2BoxTransform`).\n",
        "    pred_class_logits: predicted class scores in [-inf, +inf]; use\n",
        "        softmax(pred_class_logits) to estimate P(class).\n",
        "    gt_classes: ground-truth classification labels in [0, K], where [0, K) represent\n",
        "        foreground object classes and K represents the background class.\n",
        "    pred_proposal_deltas: predicted box2box transform deltas for transforming proposals\n",
        "        to detection box predictions.\n",
        "    gt_proposal_deltas: ground-truth box2box transform deltas\n",
        "\"\"\"\n",
        "def fast_rcnn_inference(\n",
        "    boxes: List[torch.Tensor],\n",
        "    scores: List[torch.Tensor],\n",
        "    image_shapes: List[Tuple[int, int]],\n",
        "    score_thresh: float,\n",
        "    nms_thresh: float,\n",
        "    topk_per_image: int,\n",
        "):\n",
        "    \"\"\"\n",
        "    Call `fast_rcnn_inference_single_image` for all images.\n",
        "    Args:\n",
        "        boxes (list[Tensor]): A list of Tensors of predicted class-specific or class-agnostic\n",
        "            boxes for each image. Element i has shape (Ri, K * 4) if doing\n",
        "            class-specific regression, or (Ri, 4) if doing class-agnostic\n",
        "            regression, where Ri is the number of predicted objects for image i.\n",
        "            This is compatible with the output of :meth:`FastRCNNOutputLayers.predict_boxes`.\n",
        "        scores (list[Tensor]): A list of Tensors of predicted class scores for each image.\n",
        "            Element i has shape (Ri, K + 1), where Ri is the number of predicted objects\n",
        "            for image i. Compatible with the output of :meth:`FastRCNNOutputLayers.predict_probs`.\n",
        "        image_shapes (list[tuple]): A list of (width, height) tuples for each image in the batch.\n",
        "        score_thresh (float): Only return detections with a confidence score exceeding this\n",
        "            threshold.\n",
        "        nms_thresh (float):  The threshold to use for box non-maximum suppression. Value in [0, 1].\n",
        "        topk_per_image (int): The number of top scoring detections to return. Set < 0 to return\n",
        "            all detections.\n",
        "    Returns:\n",
        "        instances: (list[Instances]): A list of N instances, one for each image in the batch,\n",
        "            that stores the topk most confidence detections.\n",
        "        kept_indices: (list[Tensor]): A list of 1D tensor of length of N, each element indicates\n",
        "            the corresponding boxes/scores index in [0, Ri) from the input, for image i.\n",
        "    \"\"\"\n",
        "    result_per_image = [\n",
        "        fast_rcnn_inference_single_image(\n",
        "            boxes_per_image, scores_per_image, image_shape, score_thresh, nms_thresh, topk_per_image\n",
        "        )\n",
        "        for scores_per_image, boxes_per_image, image_shape in zip(scores, boxes, image_shapes)\n",
        "    ]\n",
        "    return [x[0] for x in result_per_image], [x[1] for x in result_per_image]\n",
        "def _log_classification_stats(pred_logits, gt_classes, prefix=\"fast_rcnn\"):\n",
        "    \"\"\"\n",
        "    Log the classification metrics to EventStorage.\n",
        "    Args:\n",
        "        pred_logits: Rx(K+1) logits. The last column is for background class.\n",
        "        gt_classes: R labels\n",
        "    \"\"\"\n",
        "    num_instances = gt_classes.numel()\n",
        "    if num_instances == 0:\n",
        "        return\n",
        "    pred_classes = pred_logits.argmax(dim=1)\n",
        "    bg_class_ind = pred_logits.shape[1] - 1\n",
        "    fg_inds = (gt_classes >= 0) & (gt_classes < bg_class_ind)\n",
        "    num_fg = fg_inds.nonzero().numel()\n",
        "    fg_gt_classes = gt_classes[fg_inds]\n",
        "    fg_pred_classes = pred_classes[fg_inds]\n",
        "    num_false_negative = (fg_pred_classes == bg_class_ind).nonzero().numel()\n",
        "    num_accurate = (pred_classes == gt_classes).nonzero().numel()\n",
        "    fg_num_accurate = (fg_pred_classes == fg_gt_classes).nonzero().numel()\n",
        "    storage = get_event_storage()\n",
        "    storage.put_scalar(f\"{prefix}/cls_accuracy\", num_accurate / num_instances)\n",
        "    if num_fg > 0:\n",
        "        storage.put_scalar(f\"{prefix}/fg_cls_accuracy\", fg_num_accurate / num_fg)\n",
        "        storage.put_scalar(f\"{prefix}/false_negative\", num_false_negative / num_fg)\n",
        "def fast_rcnn_inference_single_image(\n",
        "    boxes,\n",
        "    scores,\n",
        "    image_shape: Tuple[int, int],\n",
        "    score_thresh: float,\n",
        "    nms_thresh: float,\n",
        "    topk_per_image: int,\n",
        "):\n",
        "    \"\"\"\n",
        "    Single-image inference. Return bounding-box detection results by thresholding\n",
        "    on scores and applying non-maximum suppression (NMS).\n",
        "    Args:\n",
        "        Same as `fast_rcnn_inference`, but with boxes, scores, and image shapes\n",
        "        per image.\n",
        "    Returns:\n",
        "        Same as `fast_rcnn_inference`, but for only one image.\n",
        "    \"\"\"\n",
        "    valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)\n",
        "    if not valid_mask.all():\n",
        "        boxes = boxes[valid_mask]\n",
        "        scores = scores[valid_mask]\n",
        "    scores = scores[:, :-1]\n",
        "    num_bbox_reg_classes = boxes.shape[1] // 4\n",
        "    # Convert to Boxes to use the `clip` function ...\n",
        "    boxes = Boxes(boxes.reshape(-1, 4))\n",
        "    boxes.clip(image_shape)\n",
        "    boxes = boxes.tensor.view(-1, num_bbox_reg_classes, 4)  # R x C x 4\n",
        "    # 1. Filter results based on detection scores. It can make NMS more efficient\n",
        "    #    by filtering out low-confidence detections.\n",
        "    filter_mask = scores > score_thresh  # R x K\n",
        "    # R' x 2. First column contains indices of the R predictions;\n",
        "    # Second column contains indices of classes.\n",
        "    filter_inds = filter_mask.nonzero()\n",
        "    if num_bbox_reg_classes == 1:\n",
        "        boxes = boxes[filter_inds[:, 0], 0]\n",
        "    else:\n",
        "        boxes = boxes[filter_mask]\n",
        "    scores = scores[filter_mask]\n",
        "    # 2. Apply NMS for each class independently.\n",
        "    keep = batched_nms(boxes, scores, filter_inds[:, 1], nms_thresh)\n",
        "    if topk_per_image >= 0:\n",
        "        keep = keep[:topk_per_image]\n",
        "    boxes, scores, filter_inds = boxes[keep], scores[keep], filter_inds[keep]\n",
        "    result = Instances(image_shape)\n",
        "    result.pred_boxes = Boxes(boxes)\n",
        "    result.scores = scores\n",
        "    result.pred_classes = filter_inds[:, 1]\n",
        "    return result, filter_inds[:, 0]\n",
        "\n",
        "\n",
        "class FastRCNNOutputs:\n",
        "    \"\"\"\n",
        "    An internal implementation that stores information about outputs of a Fast R-CNN head,\n",
        "    and provides methods that are used to decode the outputs of a Fast R-CNN head.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(\n",
        "        self,\n",
        "        box2box_transform,\n",
        "        pred_class_logits,\n",
        "        pred_proposal_deltas,\n",
        "        proposals,\n",
        "        smooth_l1_beta=0.0,\n",
        "        box_reg_loss_type=\"smooth_l1\",\n",
        "    ):\n",
        "        \"\"\"\n",
        "        Args:\n",
        "            box2box_transform (Box2BoxTransform/Box2BoxTransformRotated):\n",
        "                box2box transform instance for proposal-to-detection transformations.\n",
        "            pred_class_logits (Tensor): A tensor of shape (R, K + 1) storing the predicted class\n",
        "                logits for all R predicted object instances.\n",
        "                Each row corresponds to a predicted object instance.\n",
        "            pred_proposal_deltas (Tensor): A tensor of shape (R, K * B) or (R, B) for\n",
        "                class-specific or class-agnostic regression. It stores the predicted deltas that\n",
        "                transform proposals into final box detections.\n",
        "                B is the box dimension (4 or 5).\n",
        "                When B is 4, each row is [dx, dy, dw, dh (, ....)].\n",
        "                When B is 5, each row is [dx, dy, dw, dh, da (, ....)].\n",
        "            proposals (list[Instances]): A list of N Instances, where Instances i stores the\n",
        "                proposals for image i, in the field \"proposal_boxes\".\n",
        "                When training, each Instances must have ground-truth labels\n",
        "                stored in the field \"gt_classes\" and \"gt_boxes\".\n",
        "                The total number of all instances must be equal to R.\n",
        "            smooth_l1_beta (float): The transition point between L1 and L2 loss in\n",
        "                the smooth L1 loss function. When set to 0, the loss becomes L1. When\n",
        "                set to +inf, the loss becomes constant 0.\n",
        "            box_reg_loss_type (str): Box regression loss type. One of: \"smooth_l1\", \"giou\"\n",
        "        \"\"\"\n",
        "        self.box2box_transform = box2box_transform\n",
        "        self.num_preds_per_image = [len(p) for p in proposals]\n",
        "        self.pred_class_logits = pred_class_logits\n",
        "        self.pred_proposal_deltas = pred_proposal_deltas\n",
        "        self.smooth_l1_beta = smooth_l1_beta\n",
        "        self.box_reg_loss_type = box_reg_loss_type\n",
        "\n",
        "        self.image_shapes = [x.image_size for x in proposals]\n",
        "\n",
        "        if len(proposals):\n",
        "            box_type = type(proposals[0].proposal_boxes)\n",
        "            # cat(..., dim=0) concatenates over all images in the batch\n",
        "            self.proposals = box_type.cat([p.proposal_boxes for p in proposals])\n",
        "            assert (\n",
        "                not self.proposals.tensor.requires_grad\n",
        "            ), \"Proposals should not require gradients!\"\n",
        "\n",
        "            # \"gt_classes\" exists if and only if training. But other gt fields may\n",
        "            # not necessarily exist in training for images that have no groundtruth.\n",
        "            if proposals[0].has(\"gt_classes\"):\n",
        "                self.gt_classes = cat([p.gt_classes for p in proposals], dim=0)\n",
        "\n",
        "                # If \"gt_boxes\" does not exist, the proposals must be all negative and\n",
        "                # should not be included in regression loss computation.\n",
        "                # Here we just use proposal_boxes as an arbitrary placeholder because its\n",
        "                # value won't be used in self.box_reg_loss().\n",
        "                gt_boxes = [\n",
        "                    p.gt_boxes if p.has(\"gt_boxes\") else p.proposal_boxes for p in proposals\n",
        "                ]\n",
        "                self.gt_boxes = box_type.cat(gt_boxes)\n",
        "        else:\n",
        "            self.proposals = Boxes(torch.zeros(0, 4, device=self.pred_proposal_deltas.device))\n",
        "        self._no_instances = len(self.proposals) == 0  # no instances found\n",
        "\n",
        "    def softmax_cross_entropy_loss(self):\n",
        "        \"\"\"\n",
        "        Deprecated\n",
        "        \"\"\"\n",
        "        _log_classification_stats(self.pred_class_logits, self.gt_classes)\n",
        "        return cross_entropy(self.pred_class_logits, self.gt_classes, reduction=\"mean\")\n",
        "\n",
        "    def box_reg_loss(self):\n",
        "        \"\"\"\n",
        "        Deprecated\n",
        "        \"\"\"\n",
        "        if self._no_instances:\n",
        "            return 0.0 * self.pred_proposal_deltas.sum()\n",
        "\n",
        "        box_dim = self.proposals.tensor.size(1)  # 4 or 5\n",
        "        cls_agnostic_bbox_reg = self.pred_proposal_deltas.size(1) == box_dim\n",
        "        device = self.pred_proposal_deltas.device\n",
        "\n",
        "        bg_class_ind = self.pred_class_logits.shape[1] - 1\n",
        "        # Box delta loss is only computed between the prediction for the gt class k\n",
        "        # (if 0 <= k < bg_class_ind) and the target; there is no loss defined on predictions\n",
        "        # for non-gt classes and background.\n",
        "        # Empty fg_inds should produce a valid loss of zero because reduction=sum.\n",
        "        fg_inds = nonzero_tuple((self.gt_classes >= 0) & (self.gt_classes < bg_class_ind))[0]\n",
        "\n",
        "        if cls_agnostic_bbox_reg:\n",
        "            # pred_proposal_deltas only corresponds to foreground class for agnostic\n",
        "            gt_class_cols = torch.arange(box_dim, device=device)\n",
        "        else:\n",
        "            # pred_proposal_deltas for class k are located in columns [b * k : b * k + b],\n",
        "            # where b is the dimension of box representation (4 or 5)\n",
        "            # Note that compared to Detectron1,\n",
        "            # we do not perform bounding box regression for background classes.\n",
        "            gt_class_cols = box_dim * self.gt_classes[fg_inds, None] + torch.arange(\n",
        "                box_dim, device=device\n",
        "            )\n",
        "\n",
        "        if self.box_reg_loss_type == \"smooth_l1\":\n",
        "            gt_proposal_deltas = self.box2box_transform.get_deltas(\n",
        "                self.proposals.tensor, self.gt_boxes.tensor\n",
        "            )\n",
        "            loss_box_reg = smooth_l1_loss(\n",
        "                self.pred_proposal_deltas[fg_inds[:, None], gt_class_cols],\n",
        "                gt_proposal_deltas[fg_inds],\n",
        "                self.smooth_l1_beta,\n",
        "                reduction=\"sum\",\n",
        "            )\n",
        "        elif self.box_reg_loss_type == \"giou\":\n",
        "            fg_pred_boxes = self.box2box_transform.apply_deltas(\n",
        "                self.pred_proposal_deltas[fg_inds[:, None], gt_class_cols],\n",
        "                self.proposals.tensor[fg_inds],\n",
        "            )\n",
        "            loss_box_reg = giou_loss(\n",
        "                fg_pred_boxes,\n",
        "                self.gt_boxes.tensor[fg_inds],\n",
        "                reduction=\"sum\",\n",
        "            )\n",
        "        else:\n",
        "            raise ValueError(f\"Invalid bbox reg loss type '{self.box_reg_loss_type}'\")\n",
        "\n",
        "        loss_box_reg = loss_box_reg / self.gt_classes.numel()\n",
        "        return loss_box_reg\n",
        "\n",
        "    def losses(self):\n",
        "        \"\"\"\n",
        "        Deprecated\n",
        "        \"\"\"\n",
        "        return {\"loss_cls\": self.softmax_cross_entropy_loss(), \"loss_box_reg\": self.box_reg_loss()}\n",
        "\n",
        "    def predict_boxes(self):\n",
        "        \"\"\"\n",
        "        Deprecated\n",
        "        \"\"\"\n",
        "        pred = self.box2box_transform.apply_deltas(self.pred_proposal_deltas, self.proposals.tensor)\n",
        "        return pred.split(self.num_preds_per_image, dim=0)\n",
        "\n",
        "    def predict_probs(self):\n",
        "        \"\"\"\n",
        "        Deprecated\n",
        "        \"\"\"\n",
        "        probs = F.softmax(self.pred_class_logits, dim=-1)\n",
        "        return probs.split(self.num_preds_per_image, dim=0)\n",
        "\n",
        "\n",
        "class FastRCNNOutputLayers(nn.Module):\n",
        "    \"\"\"\n",
        "    Two linear layers for predicting Fast R-CNN outputs:\n",
        "    1. proposal-to-detection box regression deltas\n",
        "    2. classification scores\n",
        "    \"\"\"\n",
        "    @configurable\n",
        "    def __init__(\n",
        "        self,\n",
        "        input_shape: ShapeSpec,\n",
        "        *,\n",
        "        box2box_transform,\n",
        "        num_classes: int,\n",
        "        test_score_thresh: float = 0.0,\n",
        "        test_nms_thresh: float = 0.5,\n",
        "        test_topk_per_image: int = 100,\n",
        "        cls_agnostic_bbox_reg: bool = False,\n",
        "        smooth_l1_beta: float = 0.0,\n",
        "        box_reg_loss_type: str = \"smooth_l1\",\n",
        "        loss_weight: Union[float, Dict[str, float]] = 1.0,\n",
        "    ):\n",
        "        \"\"\"\n",
        "        NOTE: this interface is experimental.\n",
        "        Args:\n",
        "            input_shape (ShapeSpec): shape of the input feature to this module\n",
        "            box2box_transform (Box2BoxTransform or Box2BoxTransformRotated):\n",
        "            num_classes (int): number of foreground classes\n",
        "            test_score_thresh (float): threshold to filter predictions results.\n",
        "            test_nms_thresh (float): NMS threshold for prediction results.\n",
        "            test_topk_per_image (int): number of top predictions to produce per image.\n",
        "            cls_agnostic_bbox_reg (bool): whether to use class agnostic for bbox regression\n",
        "            smooth_l1_beta (float): transition point from L1 to L2 loss. Only used if\n",
        "                `box_reg_loss_type` is \"smooth_l1\"\n",
        "            box_reg_loss_type (str): Box regression loss type. One of: \"smooth_l1\", \"giou\"\n",
        "            loss_weight (float|dict): weights to use for losses. Can be single float for weighting\n",
        "                all losses, or a dict of individual weightings. Valid dict keys are:\n",
        "                    * \"loss_cls\": applied to classification loss\n",
        "                    * \"loss_box_reg\": applied to box regression loss\n",
        "        \"\"\"\n",
        "        super().__init__()\n",
        "        if isinstance(input_shape, int):  # some backward compatibility\n",
        "            input_shape = ShapeSpec(channels=input_shape)\n",
        "        self.num_classes = num_classes\n",
        "        input_size = input_shape.channels * (input_shape.width or 1) * (input_shape.height or 1)\n",
        "        # prediction layer for num_classes foreground classes and one background class (hence + 1)\n",
        "        self.cls_score = nn.Linear(input_size, num_classes + 1)\n",
        "        num_bbox_reg_classes = 1 if cls_agnostic_bbox_reg else num_classes\n",
        "        box_dim = len(box2box_transform.weights)\n",
        "        self.bbox_pred = nn.Linear(input_size, num_bbox_reg_classes * box_dim)\n",
        "        nn.init.normal_(self.cls_score.weight, std=0.01)\n",
        "        nn.init.normal_(self.bbox_pred.weight, std=0.001)\n",
        "        for l in [self.cls_score, self.bbox_pred]:\n",
        "            nn.init.constant_(l.bias, 0)\n",
        "        self.box2box_transform = box2box_transform\n",
        "        self.smooth_l1_beta = smooth_l1_beta\n",
        "        self.test_score_thresh = test_score_thresh\n",
        "        self.test_nms_thresh = test_nms_thresh\n",
        "        self.test_topk_per_image = test_topk_per_image\n",
        "        self.box_reg_loss_type = box_reg_loss_type\n",
        "        if isinstance(loss_weight, float):\n",
        "            loss_weight = {\"loss_cls\": loss_weight, \"loss_box_reg\": loss_weight}\n",
        "        self.loss_weight = loss_weight\n",
        "    @classmethod\n",
        "    def from_config(cls, cfg, input_shape):\n",
        "        return {\n",
        "            \"input_shape\": input_shape,\n",
        "            \"box2box_transform\": Box2BoxTransform(weights=cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS),\n",
        "            # fmt: off\n",
        "            \"num_classes\"           : cfg.MODEL.ROI_HEADS.NUM_CLASSES,\n",
        "            \"cls_agnostic_bbox_reg\" : cfg.MODEL.ROI_BOX_HEAD.CLS_AGNOSTIC_BBOX_REG,\n",
        "            \"smooth_l1_beta\"        : cfg.MODEL.ROI_BOX_HEAD.SMOOTH_L1_BETA,\n",
        "            \"test_score_thresh\"     : cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST,\n",
        "            \"test_nms_thresh\"       : cfg.MODEL.ROI_HEADS.NMS_THRESH_TEST,\n",
        "            \"test_topk_per_image\"   : cfg.TEST.DETECTIONS_PER_IMAGE,\n",
        "            \"box_reg_loss_type\"     : cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_LOSS_TYPE,\n",
        "            \"loss_weight\"           : {\"loss_box_reg\": cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_LOSS_WEIGHT},\n",
        "            # fmt: on\n",
        "        }\n",
        "    def forward(self, x):\n",
        "        \"\"\"\n",
        "        Args:\n",
        "            x: per-region features of shape (N, ...) for N bounding boxes to predict.\n",
        "        Returns:\n",
        "            (Tensor, Tensor):\n",
        "            First tensor: shape (N,K+1), scores for each of the N box. Each row contains the\n",
        "            scores for K object categories and 1 background class.\n",
        "            Second tensor: bounding box regression deltas for each box. Shape is shape (N,Kx4),\n",
        "            or (N,4) for class-agnostic regression.\n",
        "        \"\"\"\n",
        "        if x.dim() > 2:\n",
        "            x = torch.flatten(x, start_dim=1)\n",
        "        scores = self.cls_score(x)\n",
        "        proposal_deltas = self.bbox_pred(x)\n",
        "        return scores, proposal_deltas\n",
        "    def losses(self, predictions, proposals):\n",
        "        \"\"\"\n",
        "        Args:\n",
        "            predictions: return values of :meth:`forward()`.\n",
        "            proposals (list[Instances]): proposals that match the features that were used\n",
        "                to compute predictions. The fields ``proposal_boxes``, ``gt_boxes``,\n",
        "                ``gt_classes`` are expected.\n",
        "        Returns:\n",
        "            Dict[str, Tensor]: dict of losses\n",
        "        \"\"\"\n",
        "        scores, proposal_deltas = predictions\n",
        "        # parse classification outputs\n",
        "        gt_classes = (\n",
        "            cat([p.gt_classes for p in proposals], dim=0) if len(proposals) else torch.empty(0)\n",
        "        )\n",
        "        _log_classification_stats(scores, gt_classes)\n",
        "        # parse box regression outputs\n",
        "        if len(proposals):\n",
        "            proposal_boxes = cat([p.proposal_boxes.tensor for p in proposals], dim=0)  # Nx4\n",
        "            assert not proposal_boxes.requires_grad, \"Proposals should not require gradients!\"\n",
        "            # If \"gt_boxes\" does not exist, the proposals must be all negative and\n",
        "            # should not be included in regression loss computation.\n",
        "            # Here we just use proposal_boxes as an arbitrary placeholder because its\n",
        "            # value won't be used in self.box_reg_loss().\n",
        "            gt_boxes = cat(\n",
        "                [(p.gt_boxes if p.has(\"gt_boxes\") else p.proposal_boxes).tensor for p in proposals],\n",
        "                dim=0,\n",
        "            )\n",
        "        else:\n",
        "            proposal_boxes = gt_boxes = torch.empty((0, 4), device=proposal_deltas.device)\n",
        "        losses = {\n",
        "            \"loss_cls\": cross_entropy(scores, gt_classes, reduction=\"mean\"),\n",
        "            \"loss_box_reg\": self.box_reg_loss(\n",
        "                proposal_boxes, gt_boxes, proposal_deltas, gt_classes\n",
        "            ),\n",
        "        }\n",
        "        return {k: v * self.loss_weight.get(k, 1.0) for k, v in losses.items()}\n",
        "    def box_reg_loss(self, proposal_boxes, gt_boxes, pred_deltas, gt_classes):\n",
        "        \"\"\"\n",
        "        Args:\n",
        "            All boxes are tensors with the same shape Rx(4 or 5).\n",
        "            gt_classes is a long tensor of shape R, the gt class label of each proposal.\n",
        "            R shall be the number of proposals.\n",
        "        \"\"\"\n",
        "        box_dim = proposal_boxes.shape[1]  # 4 or 5\n",
        "        # Regression loss is only computed for foreground proposals (those matched to a GT)\n",
        "        fg_inds = nonzero_tuple((gt_classes >= 0) & (gt_classes < self.num_classes))[0]\n",
        "        if pred_deltas.shape[1] == box_dim:  # cls-agnostic regression\n",
        "            fg_pred_deltas = pred_deltas[fg_inds]\n",
        "        else:\n",
        "            fg_pred_deltas = pred_deltas.view(-1, self.num_classes, box_dim)[\n",
        "                fg_inds, gt_classes[fg_inds]\n",
        "            ]\n",
        "        if self.box_reg_loss_type == \"smooth_l1\":\n",
        "            gt_pred_deltas = self.box2box_transform.get_deltas(\n",
        "                proposal_boxes[fg_inds],\n",
        "                gt_boxes[fg_inds],\n",
        "            )\n",
        "            loss_box_reg = smooth_l1_loss(\n",
        "                fg_pred_deltas, gt_pred_deltas, self.smooth_l1_beta, reduction=\"sum\"\n",
        "            )\n",
        "        elif self.box_reg_loss_type == \"giou\":\n",
        "            fg_pred_boxes = self.box2box_transform.apply_deltas(\n",
        "                fg_pred_deltas, proposal_boxes[fg_inds]\n",
        "            )\n",
        "            loss_box_reg = giou_loss(fg_pred_boxes, gt_boxes[fg_inds], reduction=\"sum\")\n",
        "        else:\n",
        "            raise ValueError(f\"Invalid bbox reg loss type '{self.box_reg_loss_type}'\")\n",
        "        # The reg loss is normalized using the total number of regions (R), not the number\n",
        "        # of foreground regions even though the box regression loss is only defined on\n",
        "        # foreground regions. Why? Because doing so gives equal training influence to\n",
        "        # each foreground example. To see how, consider two different minibatches:\n",
        "        #  (1) Contains a single foreground region\n",
        "        #  (2) Contains 100 foreground regions\n",
        "        # If we normalize by the number of foreground regions, the single example in\n",
        "        # minibatch (1) will be given 100 times as much influence as each foreground\n",
        "        # example in minibatch (2). Normalizing by the total number of regions, R,\n",
        "        # means that the single example in minibatch (1) and each of the 100 examples\n",
        "        # in minibatch (2) are given equal influence.\n",
        "        return loss_box_reg / max(gt_classes.numel(), 1.0)  # return 0 if empty\n",
        "    def inference(self, predictions: Tuple[torch.Tensor, torch.Tensor], proposals: List[Instances]):\n",
        "        \"\"\"\n",
        "        Args:\n",
        "            predictions: return values of :meth:`forward()`.\n",
        "            proposals (list[Instances]): proposals that match the features that were\n",
        "                used to compute predictions. The ``proposal_boxes`` field is expected.\n",
        "        Returns:\n",
        "            list[Instances]: same as `fast_rcnn_inference`.\n",
        "            list[Tensor]: same as `fast_rcnn_inference`.\n",
        "        \"\"\"\n",
        "        boxes = self.predict_boxes(predictions, proposals)\n",
        "        scores = self.predict_probs(predictions, proposals)\n",
        "        image_shapes = [x.image_size for x in proposals]\n",
        "        return fast_rcnn_inference(\n",
        "            boxes,\n",
        "            scores,\n",
        "            image_shapes,\n",
        "            self.test_score_thresh,\n",
        "            self.test_nms_thresh,\n",
        "            self.test_topk_per_image,\n",
        "        )\n",
        "    def predict_boxes_for_gt_classes(self, predictions, proposals):\n",
        "        \"\"\"\n",
        "        Args:\n",
        "            predictions: return values of :meth:`forward()`.\n",
        "            proposals (list[Instances]): proposals that match the features that were used\n",
        "                to compute predictions. The fields ``proposal_boxes``, ``gt_classes`` are expected.\n",
        "        Returns:\n",
        "            list[Tensor]:\n",
        "                A list of Tensors of predicted boxes for GT classes in case of\n",
        "                class-specific box head. Element i of the list has shape (Ri, B), where Ri is\n",
        "                the number of proposals for image i and B is the box dimension (4 or 5)\n",
        "        \"\"\"\n",
        "        if not len(proposals):\n",
        "            return []\n",
        "        scores, proposal_deltas = predictions\n",
        "        proposal_boxes = cat([p.proposal_boxes.tensor for p in proposals], dim=0)\n",
        "        N, B = proposal_boxes.shape\n",
        "        predict_boxes = self.box2box_transform.apply_deltas(\n",
        "            proposal_deltas, proposal_boxes\n",
        "        )  # Nx(KxB)\n",
        "        K = predict_boxes.shape[1] // B\n",
        "        if K > 1:\n",
        "            gt_classes = torch.cat([p.gt_classes for p in proposals], dim=0)\n",
        "            # Some proposals are ignored or have a background class. Their gt_classes\n",
        "            # cannot be used as index.\n",
        "            gt_classes = gt_classes.clamp_(0, K - 1)\n",
        "            predict_boxes = predict_boxes.view(N, K, B)[\n",
        "                torch.arange(N, dtype=torch.long, device=predict_boxes.device), gt_classes\n",
        "            ]\n",
        "        num_prop_per_image = [len(p) for p in proposals]\n",
        "        return predict_boxes.split(num_prop_per_image)\n",
        "    def predict_boxes(\n",
        "        self, predictions: Tuple[torch.Tensor, torch.Tensor], proposals: List[Instances]\n",
        "    ):\n",
        "        \"\"\"\n",
        "        Args:\n",
        "            predictions: return values of :meth:`forward()`.\n",
        "            proposals (list[Instances]): proposals that match the features that were\n",
        "                used to compute predictions. The ``proposal_boxes`` field is expected.\n",
        "        Returns:\n",
        "            list[Tensor]:\n",
        "                A list of Tensors of predicted class-specific or class-agnostic boxes\n",
        "                for each image. Element i has shape (Ri, K * B) or (Ri, B), where Ri is\n",
        "                the number of proposals for image i and B is the box dimension (4 or 5)\n",
        "        \"\"\"\n",
        "        if not len(proposals):\n",
        "            return []\n",
        "        _, proposal_deltas = predictions\n",
        "        num_prop_per_image = [len(p) for p in proposals]\n",
        "        proposal_boxes = cat([p.proposal_boxes.tensor for p in proposals], dim=0)\n",
        "        predict_boxes = self.box2box_transform.apply_deltas(\n",
        "            proposal_deltas,\n",
        "            proposal_boxes,\n",
        "        )  # Nx(KxB)\n",
        "        return predict_boxes.split(num_prop_per_image)\n",
        "    def predict_probs(\n",
        "        self, predictions: Tuple[torch.Tensor, torch.Tensor], proposals: List[Instances]\n",
        "    ):\n",
        "        \"\"\"\n",
        "        Args:\n",
        "            predictions: return values of :meth:`forward()`.\n",
        "            proposals (list[Instances]): proposals that match the features that were\n",
        "                used to compute predictions.\n",
        "        Returns:\n",
        "            list[Tensor]:\n",
        "                A list of Tensors of predicted class probabilities for each image.\n",
        "                Element i has shape (Ri, K + 1), where Ri is the number of proposals for image i.\n",
        "        \"\"\"\n",
        "        scores, _ = predictions\n",
        "        num_inst_per_image = [len(p) for p in proposals]\n",
        "        probs = F.softmax(scores, dim=-1)\n",
        "        return probs.split(num_inst_per_image, dim=0)\n",
        "\n"
      ],
      "metadata": {
        "id": "9bo2TaLHq0fQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def load_config_and_model_weights(cfg_path, cuda = torch.cuda.is_available()):\n",
        "    cfg = get_cfg()\n",
        "    cfg.merge_from_file(model_zoo.get_config_file(cfg_path))\n",
        "\n",
        "    # ROI HEADS SCORE THRESHOLD\n",
        "    cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5\n",
        "\n",
        "    # Comment the next line if you're using 'cuda'\n",
        "    if cuda:\n",
        "      pass\n",
        "    else:\n",
        "      cfg['MODEL']['DEVICE']='cpu'\n",
        "\n",
        "    cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(cfg_path)\n",
        "\n",
        "    return cfg"
      ],
      "metadata": {
        "id": "61XaOuV1jnYW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_model(cfg):\n",
        "    # build model\n",
        "    model = build_model(cfg)\n",
        "\n",
        "    # load weights\n",
        "    checkpointer = DetectionCheckpointer(model)\n",
        "    checkpointer.load(cfg.MODEL.WEIGHTS)\n",
        "\n",
        "    # eval mode\n",
        "    model.eval()\n",
        "    return model"
      ],
      "metadata": {
        "id": "5Cch_TpJjodh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def prepare_image_inputs(cfg, model, img_list, cuda = torch.cuda.is_available()):\n",
        "    # Resizing the image according to the configuration\n",
        "    transform_gen = T.ResizeShortestEdge(\n",
        "                [cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST], cfg.INPUT.MAX_SIZE_TEST\n",
        "            )\n",
        "    img_list = [transform_gen.get_transform(img).apply_image(img) for img in img_list]\n",
        "\n",
        "    # Convert to C,H,W format\n",
        "    convert_to_tensor = lambda x: torch.Tensor(x.astype(\"float32\").transpose(2, 0, 1))\n",
        "    if cuda:\n",
        "      convert_to_tensor = lambda x: torch.Tensor(x.astype(\"float32\").transpose(2, 0, 1)).cuda()\n",
        "\n",
        "    batched_inputs = [{\"image\":convert_to_tensor(img), \"height\": img.shape[0], \"width\": img.shape[1]} for img in img_list]\n",
        "\n",
        "    # Normalizing the image\n",
        "    num_channels = len(cfg.MODEL.PIXEL_MEAN)\n",
        "    pixel_mean = torch.Tensor(cfg.MODEL.PIXEL_MEAN).view(num_channels, 1, 1)\n",
        "    pixel_std = torch.Tensor(cfg.MODEL.PIXEL_STD).view(num_channels, 1, 1)\n",
        "    if cuda:\n",
        "      pixel_mean = pixel_mean.cuda()\n",
        "      pixel_std = pixel_std.cuda()\n",
        "    normalizer = lambda x: (x - pixel_mean) / pixel_std\n",
        "    images = [normalizer(x[\"image\"]) for x in batched_inputs]\n",
        "\n",
        "    # Convert to ImageList\n",
        "    images =  ImageList.from_tensors(images,model.backbone.size_divisibility)\n",
        "    \n",
        "    return images, batched_inputs"
      ],
      "metadata": {
        "id": "BiTahb-jjraI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_features(model, images):\n",
        "    features = model.backbone(images.tensor)\n",
        "    return features"
      ],
      "metadata": {
        "id": "gjANFALRjtwr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_proposals(model, images, features):\n",
        "    proposals, _ = model.proposal_generator(images, features)\n",
        "    return proposals"
      ],
      "metadata": {
        "id": "t5RQ-EFZl1w_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_box_features(model, features, proposals):\n",
        "    features_list = [features[f] for f in ['p2', 'p3', 'p4', 'p5']]\n",
        "    box_features = model.roi_heads.box_pooler(features_list, [x.proposal_boxes for x in proposals])\n",
        "    box_features = model.roi_heads.box_head.flatten(box_features)\n",
        "    box_features = model.roi_heads.box_head.fc1(box_features)\n",
        "    box_features = model.roi_heads.box_head.fc_relu1(box_features)\n",
        "    box_features = model.roi_heads.box_head.fc2(box_features)\n",
        "\n",
        "\n",
        "    #box_features = box_features.reshape(int(box_features.nelement()/1024000), 1000, 1024) # depends on your config and batch size\n",
        "    box_features = box_features.reshape(1, 1000, 1024) # depends on your config and batch size\n",
        "    #box_features = box_features.reshape(2, 1000, 1024) # depends on your config and batch size\n",
        "    return box_features, features_list\n",
        "\n"
      ],
      "metadata": {
        "id": "hhG4gSn_l-0I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_prediction_logits(model, features_list, proposals):\n",
        "    cls_features = model.roi_heads.box_pooler(features_list, [x.proposal_boxes for x in proposals])\n",
        "    cls_features = model.roi_heads.box_head(cls_features)\n",
        "    pred_class_logits, pred_proposal_deltas = model.roi_heads.box_predictor(cls_features)\n",
        "    return pred_class_logits, pred_proposal_deltas\n",
        "\n"
      ],
      "metadata": {
        "id": "75hvrFwCmBFY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_box_scores(cfg, proposals, pred_class_logits, pred_proposal_deltas):\n",
        "    box2box_transform = Box2BoxTransform(weights=cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS)\n",
        "    smooth_l1_beta = cfg.MODEL.ROI_BOX_HEAD.SMOOTH_L1_BETA\n",
        "\n",
        "    outputs = FastRCNNOutputs(\n",
        "        box2box_transform,\n",
        "        pred_class_logits,\n",
        "        pred_proposal_deltas,\n",
        "        proposals,\n",
        "        smooth_l1_beta,\n",
        "    )\n",
        "\n",
        "    boxes = outputs.predict_boxes()\n",
        "    scores = outputs.predict_probs()\n",
        "    image_shapes = outputs.image_shapes\n",
        "\n",
        "    return boxes, scores, image_shapes\n",
        "\n"
      ],
      "metadata": {
        "id": "mNzpLICqmBtE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_output_boxes(boxes, batched_inputs, image_size):\n",
        "    proposal_boxes = boxes.reshape(-1, 4).clone()\n",
        "    scale_x, scale_y = (batched_inputs[\"width\"] / image_size[1], batched_inputs[\"height\"] / image_size[0])\n",
        "    output_boxes = Boxes(proposal_boxes)\n",
        "\n",
        "    output_boxes.scale(scale_x, scale_y)\n",
        "    output_boxes.clip(image_size)\n",
        "\n",
        "    return output_boxes\n",
        "\n"
      ],
      "metadata": {
        "id": "UgYJKt_EmEaY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def select_boxes(cfg, output_boxes, scores):\n",
        "    test_score_thresh = cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST\n",
        "    test_nms_thresh = cfg.MODEL.ROI_HEADS.NMS_THRESH_TEST\n",
        "    cls_prob = scores.detach().cpu()\n",
        "    cls_boxes = output_boxes.tensor.detach().reshape(1000,80,4).cpu()\n",
        "    max_conf = torch.zeros((cls_boxes.shape[0]))\n",
        "    for cls_ind in range(0, cls_prob.shape[1]-1):\n",
        "        cls_scores = cls_prob[:, cls_ind+1]\n",
        "        det_boxes = cls_boxes[:,cls_ind,:]\n",
        "        keep = np.array(nms(det_boxes, cls_scores, test_nms_thresh))\n",
        "        max_conf[keep] = torch.where(cls_scores[keep] > max_conf[keep], cls_scores[keep], max_conf[keep])\n",
        "    keep_boxes = torch.where(max_conf >= test_score_thresh)[0]\n",
        "    return keep_boxes, max_conf"
      ],
      "metadata": {
        "id": "_CNlKtE-mGDW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def filter_boxes(keep_boxes, max_conf, min_boxes, max_boxes):\n",
        "    if len(keep_boxes) < min_boxes:\n",
        "        keep_boxes = np.argsort(max_conf).numpy()[::-1][:min_boxes]\n",
        "    elif len(keep_boxes) > max_boxes:\n",
        "        keep_boxes = np.argsort(max_conf).numpy()[::-1][:max_boxes]\n",
        "    return keep_boxes"
      ],
      "metadata": {
        "id": "RYhhksoNmJRQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_visual_embeds(box_features, keep_boxes):\n",
        "    return box_features[keep_boxes.copy()]"
      ],
      "metadata": {
        "id": "jdtuslfUmKgM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def clean_gpu_cache():\n",
        "  gc.collect()\n",
        "  torch.cuda.empty_cache()"
      ],
      "metadata": {
        "id": "oAtej40JK0L8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def visual_embeds_from_imglist(img_list, cfg_path):\n",
        "\n",
        "  # load in the model config\n",
        "  cfg = load_config_and_model_weights(cfg_path)\n",
        "\n",
        "  # generate model\n",
        "  model = get_model(cfg)\n",
        "\n",
        "  # prepare image inputs to RNN\n",
        "  images, batched_inputs = prepare_image_inputs(cfg, model, img_list)\n",
        "\n",
        "  # \n",
        "  features = get_features(model, images)\n",
        "\n",
        "  # get box proposals\n",
        "  proposals = get_proposals(model, images, features)\n",
        "\n",
        "  box_features, features_list = get_box_features(model, features, proposals)\n",
        "\n",
        "  pred_class_logits, pred_proposal_deltas = get_prediction_logits(model, features_list, proposals)\n",
        "\n",
        "  boxes, scores, image_shapes = get_box_scores(cfg, proposals, pred_class_logits, pred_proposal_deltas)\n",
        "\n",
        "  output_boxes = [get_output_boxes(boxes[i], batched_inputs[i], proposals[i].image_size) for i in range(len(proposals))]\n",
        "\n",
        "  temp = [select_boxes(cfg, output_boxes[i], scores[i]) for i in range(len(scores))]\n",
        "  keep_boxes, max_conf = [],[]\n",
        "  for keep_box, mx_conf in temp:\n",
        "      keep_boxes.append(keep_box)\n",
        "      max_conf.append(mx_conf)\n",
        "\n",
        "  keep_boxes = [filter_boxes(keep_box, mx_conf, MIN_BOXES, MAX_BOXES) for keep_box, mx_conf in zip(keep_boxes, max_conf)]\n",
        "\n",
        "  visual_embeds = [get_visual_embeds(box_feature, keep_box) for box_feature, keep_box in zip(box_features, keep_boxes)]\n",
        "\n",
        "  return visual_embeds"
      ],
      "metadata": {
        "id": "HAO7H_qGmMc1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import glob\n",
        "import os\n",
        "import pickle\n",
        "\n",
        "def batch_vis_embed(img_directory, embed_directory, cfg_path, batch_size = 1):\n",
        "\n",
        "\n",
        "  # before running anything, clean the GPU cache.\n",
        "  clean_gpu_cache()\n",
        "\n",
        "  total_img_list = sorted(glob.glob(os.path.join(img_directory,'*.png')))\n",
        "  #total_img_list = total_img_list[0:100]\n",
        "  print(\"Found\", len(total_img_list), \"images.\")\n",
        "  # initialise empty torch tensor to store results or read in:\n",
        "  end_tensor_file = os.path.join(embed_directory, '00visual_embeddings.pt')\n",
        "\n",
        "  # list of images that have been processes\n",
        "  processed_img_names = []\n",
        "\n",
        "  if os.path.isfile(end_tensor_file):\n",
        "    end_tensor = torch.load(end_tensor_file)\n",
        "    print(f'Loaded in tensor')\n",
        "  else:\n",
        "    end_tensor = torch.zeros(size=(len(total_img_list), 100, 1024),requires_grad=False)\n",
        "\n",
        "  with torch.no_grad():\n",
        "\n",
        "    for i in range(0, len(total_img_list), batch_size):\n",
        "        current_img_list_names = total_img_list[i:i + batch_size]\n",
        "        print(f'Process {i} to {i+batch_size} : {current_img_list_names}')\n",
        "        img_file_path = current_img_list_names[0]\n",
        "        img_file_name = os.path.basename(img_file_path)\n",
        "        img_file_name = int(os.path.splitext(os.path.basename(img_file_name))[0])\n",
        "        processed_img_names.append(img_file_name)\n",
        "\n",
        "        current_img_list = [cv2.imread(x) for x in current_img_list_names]\n",
        "\n",
        "        try:\n",
        "          clean_gpu_cache()\n",
        "          if end_tensor[i:i+batch_size].sum():\n",
        "            print(f'Already processed. Continuing...')\n",
        "            continue\n",
        "\n",
        "          # if sorted alpahbetically, image number 674 makes some problems: ecb-1202992784652808192.png, inea_eu-1235481777197604865.png\n",
        "          # can not figure out now why that is the case; and no time until deadline; I moved those images into another directory in google drive\n",
        "          # and will delete the respective rows down below\n",
        "          # TO DO: MAKE THIS WORK DYNAMICALLY FOR ANY IMAGE, OR AT LEAST try-catch THE ERRORS\n",
        "          #if i not in [674, 839]:\n",
        "          res = visual_embeds_from_imglist(current_img_list, cfg_path)\n",
        "          #else:\n",
        "          #  continue\n",
        "          \n",
        "          \n",
        "          for index, tensor in enumerate(res):\n",
        "            # print(tensor.shape)\n",
        "            end_tensor[i+index] = tensor\n",
        "          del(res)\n",
        "        except RuntimeError:\n",
        "          clean_gpu_cache()\n",
        "          for j, val in enumerate(current_img_list):\n",
        "            if end_tensor[i+j].sum() > 0:\n",
        "              print(f'Already processed. Continuing...')\n",
        "              continue\n",
        "            res = visual_embeds_from_imglist([val], cfg_path)\n",
        "            # print(res[0].shape)\n",
        "            end_tensor[i+j] = res[0]\n",
        "            del(res)\n",
        "        torch.save(end_tensor, end_tensor_file)\n",
        "        # print(f'processed {i}')\n",
        "\n",
        "  return end_tensor, processed_img_names, total_img_list"
      ],
      "metadata": {
        "id": "3SFHMziTJWGU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras import backend as K\n",
        "\n",
        "def recall_m(y_true, y_pred):\n",
        "    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
        "    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n",
        "    recall = true_positives / (possible_positives + K.epsilon())\n",
        "    return recall\n",
        "\n",
        "def precision_m(y_true, y_pred):\n",
        "    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
        "    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n",
        "    precision = true_positives / (predicted_positives + K.epsilon())\n",
        "    return precision\n",
        "\n",
        "def f1_m(y_true, y_pred):\n",
        "    precision = precision_m(y_true, y_pred)\n",
        "    recall = recall_m(y_true, y_pred)\n",
        "    return 2*((precision*recall)/(precision+recall+K.epsilon()))"
      ],
      "metadata": {
        "id": "ipMGOglEWlwN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def batch_final_embeddings(tensor_directory='/content/drive/MyDrive/multimodal/final_embeddings/',\n",
        "                           visualbert_model='uclanlp/visualbert-nlvr2-coco-pre',\n",
        "                           batch_size = 1):\n",
        "\n",
        "\n",
        "  # before running anything, clean the GPU cache.\n",
        "  clean_gpu_cache()\n",
        "  model = VisualBertModel.from_pretrained(visualbert_model) # this checkpoint has 1024 dimensional visual embeddings projection\n",
        "\n",
        "  #total_img_list = sorted(glob.glob(os.path.join(img_directory,'*.png')))\n",
        "\n",
        "  # initialise empty torch tensor to store results or read in:\n",
        "  end_tensor_file = os.path.join(tensor_directory, '00final_embeddings.pt')\n",
        "\n",
        "  if os.path.isfile(end_tensor_file):\n",
        "    end_tensor = torch.load(end_tensor_file)\n",
        "    print(f'Loaded tensor of shape ', end_tensor.shape)\n",
        "  else:\n",
        "    end_tensor = torch.zeros(size=(len(tweets), 612, 768),requires_grad=False)\n",
        "\n",
        "  with torch.no_grad():\n",
        "    for i in range(0, len(tweets), batch_size):\n",
        "    #current_img_list_names = total_img_list[i:i + batch_size]\n",
        "      print(f'Processing {i} to {i+batch_size}')\n",
        "      #current_img_list = [cv2.imread(x) for x in current_img_list_names]\n",
        "\n",
        "      try:\n",
        "        clean_gpu_cache()\n",
        "        if end_tensor[i:i+batch_size].sum():\n",
        "          print(f'Already processed. Continuing...')\n",
        "          continue\n",
        "        outputs = model(input_ids=input_ids[i:i+batch_size], \n",
        "                attention_mask=attention_mask[i:i+batch_size], \n",
        "                token_type_ids=token_type_ids[i:i+batch_size], \n",
        "                visual_embeds=visual_embeds[i:i+batch_size], \n",
        "                visual_attention_mask=visual_attention_mask[i:i+batch_size], \n",
        "                visual_token_type_ids=visual_token_type_ids[i:i+batch_size])\n",
        "\n",
        "        res = outputs.last_hidden_state\n",
        "        #print(res)\n",
        "        for index, tensor in enumerate(res):\n",
        "          # print(tensor.shape)\n",
        "          end_tensor[i+index] = tensor\n",
        "        del(res)\n",
        "        if i % 10 == 0:\n",
        "          print(\"Saving tensor to drive...\")\n",
        "          torch.save(end_tensor, end_tensor_file)\n",
        "      except RuntimeError:\n",
        "          clean_gpu_cache()\n",
        "\n",
        "  print(\"Saving tensor to drive...\")\n",
        "  torch.save(end_tensor, end_tensor_file)\n",
        "  return end_tensor\n",
        "\n",
        "\n",
        "  "
      ],
      "metadata": {
        "id": "ZtI7GshZ9-nh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Produce visual embeddings\n"
      ],
      "metadata": {
        "id": "OPUxkMqNnvVg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define parameters.\n",
        "\n",
        "# For the box approach: determine the minimum and maximum boxes allowed.\n",
        "#MIN_BOXES=10\n",
        "#MAX_BOXES=100\n",
        "\n",
        "# Config path: model to load in.\n",
        "#cfg_path = \"COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml\" # the pre-trained visual model"
      ],
      "metadata": {
        "id": "GWheJ7TeobbI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#dir =('/content/drive/MyDrive/multimodal/image_data')\n",
        "#for i in os.listdir(dir):\n",
        "#    files = os.path.join(dir,i)\n",
        "#    split = os.path.splitext(files)\n",
        "    #if split[1]=='.txt':\n",
        "    #new_name = re.sub(\"[^0-9]\", \"\", split[0])\n",
        "#    os.rename(files,split[0]+'.png')"
      ],
      "metadata": {
        "id": "wt0G9MoOVAab"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#vis_embed_tensor, processed_img_names, total_img_list = batch_vis_embed('/content/drive/MyDrive/multimodal/image_data', '/content/drive/MyDrive/multimodal/visual_embeddings', cfg_path)"
      ],
      "metadata": {
        "id": "LhzqX6h6gSzn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#vis_embed_tensor, processed_img_names, total_img_list = batch_vis_embed('/content/drive/MyDrive/multimodal/image_data', '/content/drive/MyDrive/multimodal/visual_embeddings', cfg_path)\n",
        "#vis_embed_tensor = torch.load(\"/content/drive/MyDrive/multimodal/visual_embeddings/00visual_embeddings.pt\")"
      ],
      "metadata": {
        "id": "0ApIhZ6LIiH7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#vis_embed_tensor.shape"
      ],
      "metadata": {
        "id": "R0d3oUmgdl_r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Produce combined embeddings"
      ],
      "metadata": {
        "id": "uEp7dVGaKj1f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#tokenizer = BertTokenizer.from_pretrained('bert-base-cased')"
      ],
      "metadata": {
        "id": "D5H-RDzvXuVp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#tokens = tokenizer(tweets, padding='max_length', max_length=512, truncation=True)"
      ],
      "metadata": {
        "id": "ciPTrD9qXuYF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#input_ids = torch.tensor(tokens[\"input_ids\"])\n",
        "#attention_mask = torch.tensor(tokens[\"attention_mask\"])\n",
        "#token_type_ids = torch.tensor(tokens[\"token_type_ids\"])"
      ],
      "metadata": {
        "id": "VEENj8WyXuaW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#visual_embeds = vis_embed_tensor"
      ],
      "metadata": {
        "id": "r4eIXPneXucc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#visual_embeds.shape"
      ],
      "metadata": {
        "id": "oTRdHdlKD80m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.long)\n",
        "#visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long)"
      ],
      "metadata": {
        "id": "oq4SHwYhXuhR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#model = VisualBertModel.from_pretrained('uclanlp/visualbert-nlvr2-coco-pre') # this checkpoint has 1024 dimensional visual embeddings projection"
      ],
      "metadata": {
        "id": "hA_J3OSOYomI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#final_embeddings = batch_final_embeddings()"
      ],
      "metadata": {
        "id": "0ZgfVH959_uZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load combined embeddings"
      ],
      "metadata": {
        "id": "_EnWTXeNLNlE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "final_embeddings = torch.load('/content/drive/MyDrive/multimodal/final_embeddings/00final_embeddings.pt')"
      ],
      "metadata": {
        "id": "IZiiLDUOLQqG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_embeddings.shape"
      ],
      "metadata": {
        "id": "t-M5xqsik6QR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "54d5c000-2399-4766-c67b-72a4a6b47508"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([898, 612, 768])"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(labels)"
      ],
      "metadata": {
        "id": "9gJU7p_JmNlH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "12f77516-3aa8-41bd-c036-1038866a9427"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "898"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Build classifiers"
      ],
      "metadata": {
        "id": "QySZIKewLU-i"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Prepare data"
      ],
      "metadata": {
        "id": "8s0TCWaZOjEb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train_raw, X_test, y_train_raw, y_test = train_test_split(final_embeddings,\n",
        "                                                    labels,\n",
        "                                                    test_size=0.2,\n",
        "                                                    random_state=42)"
      ],
      "metadata": {
        "id": "UklKc27q8Fyx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_raw.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GAhy-SnnAhbP",
        "outputId": "3064a3a8-b9e5-4125-8915-837ac5634457"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([718, 612, 768])"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from sklearn.decomposition import PCA\n",
        "X_train = np.array(X_train_raw)\n",
        "y_train = np.array(y_train_raw)\n",
        "\n",
        "X_test = np.array(X_test)\n",
        "y_test = np.array(y_test)\n",
        "\n",
        "\n",
        "nsamples, nx, ny = X_train.shape\n",
        "X_train = X_train.reshape((nsamples,nx*ny))\n",
        "\n",
        "\n",
        "nsamples, nx, ny = X_test.shape\n",
        "X_test = X_test.reshape((nsamples,nx*ny))\n",
        "\n",
        "\n",
        "X_train_2d = PCA(n_components=2, random_state = 42).fit_transform(X_train)\n",
        "X_test_2d = PCA(n_components=2, random_state = 42).fit_transform(X_test)"
      ],
      "metadata": {
        "id": "8TlNtwt5ZOEk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train_2d.shape)\n",
        "print(X_test_2d.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0-4nsAtj_9Sf",
        "outputId": "c469bba4-4300-4cea-cb56-bd782ab5e61a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(718, 2)\n",
            "(180, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Support Vector Machine"
      ],
      "metadata": {
        "id": "0LMUrUiYLrzQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "parameters = {'kernel':('linear', 'rbf', 'poly'), 'C':[1, 10, 100, 300, 500]}\n",
        "svc = svm.SVC(verbose=1)\n",
        "clf = GridSearchCV(svc, parameters, verbose=10)\n",
        "clf.fit(X_train_2d, y_train)\n",
        "\n",
        "\n",
        "sorted(clf.cv_results_.keys())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CPtSTLNGMfBd",
        "outputId": "6782863a-546d-4fe0-e146-fb86c3a793c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['mean_fit_time',\n",
              " 'mean_score_time',\n",
              " 'mean_test_score',\n",
              " 'param_C',\n",
              " 'param_kernel',\n",
              " 'params',\n",
              " 'rank_test_score',\n",
              " 'split0_test_score',\n",
              " 'split1_test_score',\n",
              " 'split2_test_score',\n",
              " 'split3_test_score',\n",
              " 'split4_test_score',\n",
              " 'std_fit_time',\n",
              " 'std_score_time',\n",
              " 'std_test_score']"
            ]
          },
          "metadata": {},
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf.cv_results_[\"mean_test_score\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SPaTQ-w4RT4n",
        "outputId": "bcd505f0-447d-45c7-ac3a-50261eed67a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.57804973, 0.58494561, 0.56267483, 0.57804973, 0.56960956,\n",
              "       0.56267483, 0.5780303 , 0.57244561, 0.56267483, 0.57524281,\n",
              "       0.56825952, 0.56267483, 0.57386364, 0.56961927, 0.56267483])"
            ]
          },
          "metadata": {},
          "execution_count": 183
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction_svm = clf.predict(X_test_2d)"
      ],
      "metadata": {
        "id": "M7M_sF_KOYdb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, prediction_svm))\n",
        "\n",
        "cm = confusion_matrix(y_test, prediction_svm)\n",
        "\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
        "\n",
        "disp.plot(cmap=plt.cm.Blues)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 440
        },
        "id": "IEJh1whGOId0",
        "outputId": "80b83d87-b2f6-4d3d-9c4a-c91955778a7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.54      0.48      0.51        92\n",
            "           1       0.52      0.58      0.55        88\n",
            "\n",
            "    accuracy                           0.53       180\n",
            "   macro avg       0.53      0.53      0.53       180\n",
            "weighted avg       0.53      0.53      0.53       180\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Random Forest"
      ],
      "metadata": {
        "id": "Nuo8sFkHPsD7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Number of trees in random forest\n",
        "n_estimators = [200, 400, 2000]\n",
        "# Number of features to consider at every split\n",
        "max_features = ['auto', 'sqrt']\n",
        "# Maximum number of levels in tree\n",
        "max_depth = [40, 110]\n",
        "max_depth.append(None)\n",
        "# Minimum number of samples required to split a node\n",
        "#min_samples_split = [2, 5, 10]\n",
        "# Minimum number of samples required at each leaf node\n",
        "#min_samples_leaf = [1, 2, 4]\n",
        "# Method of selecting samples for training each tree\n",
        "bootstrap = [True]\n",
        "\n",
        "\n",
        "# Create the random grid\n",
        "parameters = {'n_estimators': n_estimators,\n",
        "               'max_features': max_features,\n",
        "               'max_depth': max_depth,\n",
        "               'min_samples_split': min_samples_split,\n",
        "               'min_samples_leaf': min_samples_leaf,\n",
        "               'bootstrap': bootstrap}\n",
        "\n",
        "print(parameters)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Na99CZt4PhdA",
        "outputId": "03089540-b922-4424-f98d-020072b93298"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'n_estimators': [200, 400, 2000], 'max_features': ['auto', 'sqrt'], 'max_depth': [40, 110, None], 'min_samples_split': [2, 5, 10], 'min_samples_leaf': [1, 2, 4], 'bootstrap': [True]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rfc = RandomForestClassifier()\n",
        "clf = GridSearchCV(rfc, parameters, verbose=10)\n",
        "clf.fit(X_train_2d, y_train)\n",
        "\n",
        "\n",
        "sorted(clf.cv_results_.keys())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sLD96CzLPhlW",
        "outputId": "93d7fa0f-ca1c-4025-916d-f98fe480c889"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 162 candidates, totalling 810 fits\n",
            "[CV 1/5; 1/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
            "[CV 1/5; 1/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.521 total time=   0.3s\n",
            "[CV 2/5; 1/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
            "[CV 2/5; 1/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.562 total time=   0.3s\n",
            "[CV 3/5; 1/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
            "[CV 3/5; 1/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.618 total time=   0.3s\n",
            "[CV 4/5; 1/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
            "[CV 4/5; 1/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.601 total time=   0.3s\n",
            "[CV 5/5; 1/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
            "[CV 5/5; 1/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.587 total time=   0.3s\n",
            "[CV 1/5; 2/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=400\n",
            "[CV 1/5; 2/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.521 total time=   0.7s\n",
            "[CV 2/5; 2/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=400\n",
            "[CV 2/5; 2/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.562 total time=   0.7s\n",
            "[CV 3/5; 2/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=400\n",
            "[CV 3/5; 2/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.604 total time=   0.7s\n",
            "[CV 4/5; 2/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=400\n",
            "[CV 4/5; 2/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.601 total time=   0.7s\n",
            "[CV 5/5; 2/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=400\n",
            "[CV 5/5; 2/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.573 total time=   0.7s\n",
            "[CV 1/5; 3/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=2000\n",
            "[CV 1/5; 3/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=2000;, score=0.514 total time=   3.3s\n",
            "[CV 2/5; 3/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=2000\n",
            "[CV 2/5; 3/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=2000;, score=0.556 total time=   3.2s\n",
            "[CV 3/5; 3/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=2000\n",
            "[CV 3/5; 3/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=2000;, score=0.604 total time=   3.3s\n",
            "[CV 4/5; 3/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=2000\n",
            "[CV 4/5; 3/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=2000;, score=0.601 total time=   3.3s\n",
            "[CV 5/5; 3/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=2000\n",
            "[CV 5/5; 3/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=2000;, score=0.587 total time=   3.3s\n",
            "[CV 1/5; 4/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
            "[CV 1/5; 4/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.521 total time=   0.3s\n",
            "[CV 2/5; 4/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
            "[CV 2/5; 4/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.535 total time=   0.3s\n",
            "[CV 3/5; 4/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
            "[CV 3/5; 4/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.597 total time=   0.3s\n",
            "[CV 4/5; 4/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
            "[CV 4/5; 4/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.580 total time=   0.3s\n",
            "[CV 5/5; 4/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
            "[CV 5/5; 4/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.573 total time=   0.3s\n",
            "[CV 1/5; 5/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=400\n",
            "[CV 1/5; 5/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.521 total time=   0.6s\n",
            "[CV 2/5; 5/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=400\n",
            "[CV 2/5; 5/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.514 total time=   0.7s\n",
            "[CV 3/5; 5/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=400\n",
            "[CV 3/5; 5/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.590 total time=   0.6s\n",
            "[CV 4/5; 5/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=400\n",
            "[CV 4/5; 5/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.608 total time=   0.6s\n",
            "[CV 5/5; 5/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=400\n",
            "[CV 5/5; 5/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.566 total time=   0.6s\n",
            "[CV 1/5; 6/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=2000\n",
            "[CV 1/5; 6/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=2000;, score=0.528 total time=   3.1s\n",
            "[CV 2/5; 6/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=2000\n",
            "[CV 2/5; 6/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=2000;, score=0.535 total time=   3.3s\n",
            "[CV 3/5; 6/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=2000\n",
            "[CV 3/5; 6/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=2000;, score=0.597 total time=   3.2s\n",
            "[CV 4/5; 6/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=2000\n",
            "[CV 4/5; 6/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=2000;, score=0.587 total time=   3.1s\n",
            "[CV 5/5; 6/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=2000\n",
            "[CV 5/5; 6/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=2000;, score=0.601 total time=   3.2s\n",
            "[CV 1/5; 7/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
            "[CV 1/5; 7/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.535 total time=   0.3s\n",
            "[CV 2/5; 7/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
            "[CV 2/5; 7/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.583 total time=   0.3s\n",
            "[CV 3/5; 7/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
            "[CV 3/5; 7/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.583 total time=   0.3s\n",
            "[CV 4/5; 7/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
            "[CV 4/5; 7/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.566 total time=   0.3s\n",
            "[CV 5/5; 7/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
            "[CV 5/5; 7/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.559 total time=   0.3s\n",
            "[CV 1/5; 8/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=400\n",
            "[CV 1/5; 8/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.549 total time=   0.6s\n",
            "[CV 2/5; 8/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=400\n",
            "[CV 2/5; 8/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.562 total time=   0.6s\n",
            "[CV 3/5; 8/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=400\n",
            "[CV 3/5; 8/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.597 total time=   0.6s\n",
            "[CV 4/5; 8/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=400\n",
            "[CV 4/5; 8/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.573 total time=   0.6s\n",
            "[CV 5/5; 8/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=400\n",
            "[CV 5/5; 8/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.594 total time=   0.6s\n",
            "[CV 1/5; 9/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=2000\n",
            "[CV 1/5; 9/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=2000;, score=0.549 total time=   3.1s\n",
            "[CV 2/5; 9/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=2000\n",
            "[CV 2/5; 9/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=2000;, score=0.542 total time=   3.2s\n",
            "[CV 3/5; 9/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=2000\n",
            "[CV 3/5; 9/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=2000;, score=0.604 total time=   3.2s\n",
            "[CV 4/5; 9/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=2000\n",
            "[CV 4/5; 9/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=2000;, score=0.552 total time=   3.1s\n",
            "[CV 5/5; 9/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=2000\n",
            "[CV 5/5; 9/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=2000;, score=0.573 total time=   3.1s\n",
            "[CV 1/5; 10/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
            "[CV 1/5; 10/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.549 total time=   0.3s\n",
            "[CV 2/5; 10/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
            "[CV 2/5; 10/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.535 total time=   0.3s\n",
            "[CV 3/5; 10/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
            "[CV 3/5; 10/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.611 total time=   0.3s\n",
            "[CV 4/5; 10/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
            "[CV 4/5; 10/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.587 total time=   0.4s\n",
            "[CV 5/5; 10/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
            "[CV 5/5; 10/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.573 total time=   0.3s\n",
            "[CV 1/5; 11/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=400\n",
            "[CV 1/5; 11/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.549 total time=   0.6s\n",
            "[CV 2/5; 11/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=400\n",
            "[CV 2/5; 11/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.528 total time=   0.6s\n",
            "[CV 3/5; 11/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=400\n",
            "[CV 3/5; 11/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.597 total time=   0.7s\n",
            "[CV 4/5; 11/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=400\n",
            "[CV 4/5; 11/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.566 total time=   0.6s\n",
            "[CV 5/5; 11/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=400\n",
            "[CV 5/5; 11/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.573 total time=   0.7s\n",
            "[CV 1/5; 12/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=2000\n",
            "[CV 1/5; 12/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=2000;, score=0.549 total time=   3.2s\n",
            "[CV 2/5; 12/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=2000\n",
            "[CV 2/5; 12/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=2000;, score=0.535 total time=   3.1s\n",
            "[CV 3/5; 12/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=2000\n",
            "[CV 3/5; 12/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=2000;, score=0.611 total time=   3.1s\n",
            "[CV 4/5; 12/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=2000\n",
            "[CV 4/5; 12/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=2000;, score=0.580 total time=   3.1s\n",
            "[CV 5/5; 12/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=2000\n",
            "[CV 5/5; 12/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=2000;, score=0.566 total time=   3.3s\n",
            "[CV 1/5; 13/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
            "[CV 1/5; 13/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.535 total time=   0.3s\n",
            "[CV 2/5; 13/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
            "[CV 2/5; 13/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.556 total time=   0.3s\n",
            "[CV 3/5; 13/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
            "[CV 3/5; 13/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.590 total time=   0.3s\n",
            "[CV 4/5; 13/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
            "[CV 4/5; 13/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.587 total time=   0.3s\n",
            "[CV 5/5; 13/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
            "[CV 5/5; 13/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.538 total time=   0.3s\n",
            "[CV 1/5; 14/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=400\n",
            "[CV 1/5; 14/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.535 total time=   0.6s\n",
            "[CV 2/5; 14/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=400\n",
            "[CV 2/5; 14/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.535 total time=   0.6s\n",
            "[CV 3/5; 14/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=400\n",
            "[CV 3/5; 14/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.583 total time=   0.6s\n",
            "[CV 4/5; 14/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=400\n",
            "[CV 4/5; 14/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.552 total time=   0.7s\n",
            "[CV 5/5; 14/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=400\n",
            "[CV 5/5; 14/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.573 total time=   0.6s\n",
            "[CV 1/5; 15/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=2000\n",
            "[CV 1/5; 15/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=2000;, score=0.562 total time=   3.1s\n",
            "[CV 2/5; 15/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=2000\n",
            "[CV 2/5; 15/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=2000;, score=0.542 total time=   3.2s\n",
            "[CV 3/5; 15/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=2000\n",
            "[CV 3/5; 15/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=2000;, score=0.597 total time=   3.1s\n",
            "[CV 4/5; 15/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=2000\n",
            "[CV 4/5; 15/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=2000;, score=0.587 total time=   3.1s\n",
            "[CV 5/5; 15/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=2000\n",
            "[CV 5/5; 15/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=2000;, score=0.566 total time=   3.1s\n",
            "[CV 1/5; 16/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
            "[CV 1/5; 16/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.542 total time=   0.3s\n",
            "[CV 2/5; 16/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
            "[CV 2/5; 16/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.521 total time=   0.3s\n",
            "[CV 3/5; 16/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
            "[CV 3/5; 16/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.583 total time=   0.3s\n",
            "[CV 4/5; 16/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
            "[CV 4/5; 16/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.559 total time=   0.3s\n",
            "[CV 5/5; 16/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
            "[CV 5/5; 16/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.559 total time=   0.3s\n",
            "[CV 1/5; 17/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=400\n",
            "[CV 1/5; 17/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.556 total time=   0.6s\n",
            "[CV 2/5; 17/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=400\n",
            "[CV 2/5; 17/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.542 total time=   0.6s\n",
            "[CV 3/5; 17/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=400\n",
            "[CV 3/5; 17/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.597 total time=   0.6s\n",
            "[CV 4/5; 17/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=400\n",
            "[CV 4/5; 17/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.573 total time=   0.6s\n",
            "[CV 5/5; 17/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=400\n",
            "[CV 5/5; 17/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.573 total time=   0.6s\n",
            "[CV 1/5; 18/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=2000\n",
            "[CV 1/5; 18/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=2000;, score=0.549 total time=   3.0s\n",
            "[CV 2/5; 18/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=2000\n",
            "[CV 2/5; 18/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=2000;, score=0.528 total time=   4.1s\n",
            "[CV 3/5; 18/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=2000\n",
            "[CV 3/5; 18/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=2000;, score=0.604 total time=   3.1s\n",
            "[CV 4/5; 18/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=2000\n",
            "[CV 4/5; 18/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=2000;, score=0.566 total time=   3.1s\n",
            "[CV 5/5; 18/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=2000\n",
            "[CV 5/5; 18/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=2000;, score=0.573 total time=   3.1s\n",
            "[CV 1/5; 19/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
            "[CV 1/5; 19/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.542 total time=   0.3s\n",
            "[CV 2/5; 19/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
            "[CV 2/5; 19/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.576 total time=   0.3s\n",
            "[CV 3/5; 19/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
            "[CV 3/5; 19/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.611 total time=   0.3s\n",
            "[CV 4/5; 19/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
            "[CV 4/5; 19/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.587 total time=   0.3s\n",
            "[CV 5/5; 19/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
            "[CV 5/5; 19/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.531 total time=   0.3s\n",
            "[CV 1/5; 20/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=400\n",
            "[CV 1/5; 20/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.549 total time=   0.6s\n",
            "[CV 2/5; 20/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=400\n",
            "[CV 2/5; 20/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.549 total time=   0.6s\n",
            "[CV 3/5; 20/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=400\n",
            "[CV 3/5; 20/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.604 total time=   0.7s\n",
            "[CV 4/5; 20/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=400\n",
            "[CV 4/5; 20/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.587 total time=   0.6s\n",
            "[CV 5/5; 20/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=400\n",
            "[CV 5/5; 20/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.580 total time=   0.6s\n",
            "[CV 1/5; 21/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=2000\n",
            "[CV 1/5; 21/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=2000;, score=0.562 total time=   3.0s\n",
            "[CV 2/5; 21/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=2000\n",
            "[CV 2/5; 21/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=2000;, score=0.549 total time=   3.0s\n",
            "[CV 3/5; 21/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=2000\n",
            "[CV 3/5; 21/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=2000;, score=0.618 total time=   3.1s\n",
            "[CV 4/5; 21/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=2000\n",
            "[CV 4/5; 21/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=2000;, score=0.580 total time=   3.0s\n",
            "[CV 5/5; 21/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=2000\n",
            "[CV 5/5; 21/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=2000;, score=0.566 total time=   3.1s\n",
            "[CV 1/5; 22/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
            "[CV 1/5; 22/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.535 total time=   0.3s\n",
            "[CV 2/5; 22/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
            "[CV 2/5; 22/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.542 total time=   0.3s\n",
            "[CV 3/5; 22/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
            "[CV 3/5; 22/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.611 total time=   0.3s\n",
            "[CV 4/5; 22/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
            "[CV 4/5; 22/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.566 total time=   0.3s\n",
            "[CV 5/5; 22/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
            "[CV 5/5; 22/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.559 total time=   0.3s\n",
            "[CV 1/5; 23/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=400\n",
            "[CV 1/5; 23/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.562 total time=   0.6s\n",
            "[CV 2/5; 23/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=400\n",
            "[CV 2/5; 23/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.556 total time=   0.8s\n",
            "[CV 3/5; 23/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=400\n",
            "[CV 3/5; 23/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.604 total time=   0.6s\n",
            "[CV 4/5; 23/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=400\n",
            "[CV 4/5; 23/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.580 total time=   0.6s\n",
            "[CV 5/5; 23/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=400\n",
            "[CV 5/5; 23/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.559 total time=   0.6s\n",
            "[CV 1/5; 24/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=2000\n",
            "[CV 1/5; 24/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=2000;, score=0.556 total time=   3.0s\n",
            "[CV 2/5; 24/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=2000\n",
            "[CV 2/5; 24/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=2000;, score=0.542 total time=   3.1s\n",
            "[CV 3/5; 24/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=2000\n",
            "[CV 3/5; 24/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=2000;, score=0.604 total time=   3.1s\n",
            "[CV 4/5; 24/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=2000\n",
            "[CV 4/5; 24/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=2000;, score=0.594 total time=   3.0s\n",
            "[CV 5/5; 24/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=2000\n",
            "[CV 5/5; 24/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=2000;, score=0.566 total time=   3.0s\n",
            "[CV 1/5; 25/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
            "[CV 1/5; 25/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.549 total time=   0.3s\n",
            "[CV 2/5; 25/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
            "[CV 2/5; 25/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.556 total time=   0.3s\n",
            "[CV 3/5; 25/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
            "[CV 3/5; 25/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.611 total time=   0.3s\n",
            "[CV 4/5; 25/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
            "[CV 4/5; 25/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.580 total time=   0.3s\n",
            "[CV 5/5; 25/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
            "[CV 5/5; 25/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.573 total time=   0.3s\n",
            "[CV 1/5; 26/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=400\n",
            "[CV 1/5; 26/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.556 total time=   0.6s\n",
            "[CV 2/5; 26/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=400\n",
            "[CV 2/5; 26/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.556 total time=   0.6s\n",
            "[CV 3/5; 26/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=400\n",
            "[CV 3/5; 26/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.625 total time=   0.6s\n",
            "[CV 4/5; 26/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=400\n",
            "[CV 4/5; 26/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.601 total time=   0.6s\n",
            "[CV 5/5; 26/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=400\n",
            "[CV 5/5; 26/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.545 total time=   0.6s\n",
            "[CV 1/5; 27/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=2000\n",
            "[CV 1/5; 27/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=2000;, score=0.556 total time=   3.1s\n",
            "[CV 2/5; 27/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=2000\n",
            "[CV 2/5; 27/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=2000;, score=0.535 total time=   3.0s\n",
            "[CV 3/5; 27/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=2000\n",
            "[CV 3/5; 27/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=2000;, score=0.611 total time=   3.0s\n",
            "[CV 4/5; 27/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=2000\n",
            "[CV 4/5; 27/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=2000;, score=0.587 total time=   3.0s\n",
            "[CV 5/5; 27/162] START bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=2000\n",
            "[CV 5/5; 27/162] END bootstrap=True, max_depth=40, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=2000;, score=0.552 total time=   3.0s\n",
            "[CV 1/5; 28/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
            "[CV 1/5; 28/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.542 total time=   0.3s\n",
            "[CV 2/5; 28/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
            "[CV 2/5; 28/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.569 total time=   0.4s\n",
            "[CV 3/5; 28/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
            "[CV 3/5; 28/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.597 total time=   0.3s\n",
            "[CV 4/5; 28/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
            "[CV 4/5; 28/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.601 total time=   0.4s\n",
            "[CV 5/5; 28/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
            "[CV 5/5; 28/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.587 total time=   0.3s\n",
            "[CV 1/5; 29/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400\n",
            "[CV 1/5; 29/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.542 total time=   0.6s\n",
            "[CV 2/5; 29/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400\n",
            "[CV 2/5; 29/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.562 total time=   0.6s\n",
            "[CV 3/5; 29/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400\n",
            "[CV 3/5; 29/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.597 total time=   0.7s\n",
            "[CV 4/5; 29/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400\n",
            "[CV 4/5; 29/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.629 total time=   0.7s\n",
            "[CV 5/5; 29/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400\n",
            "[CV 5/5; 29/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.573 total time=   0.6s\n",
            "[CV 1/5; 30/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=2000\n",
            "[CV 1/5; 30/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=2000;, score=0.521 total time=   3.3s\n",
            "[CV 2/5; 30/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=2000\n",
            "[CV 2/5; 30/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=2000;, score=0.556 total time=   3.4s\n",
            "[CV 3/5; 30/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=2000\n",
            "[CV 3/5; 30/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=2000;, score=0.604 total time=   3.3s\n",
            "[CV 4/5; 30/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=2000\n",
            "[CV 4/5; 30/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=2000;, score=0.587 total time=   3.3s\n",
            "[CV 5/5; 30/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=2000\n",
            "[CV 5/5; 30/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=2000;, score=0.573 total time=   3.4s\n",
            "[CV 1/5; 31/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
            "[CV 1/5; 31/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.549 total time=   0.4s\n",
            "[CV 2/5; 31/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
            "[CV 2/5; 31/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.521 total time=   0.3s\n",
            "[CV 3/5; 31/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
            "[CV 3/5; 31/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.597 total time=   0.3s\n",
            "[CV 4/5; 31/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
            "[CV 4/5; 31/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.615 total time=   0.3s\n",
            "[CV 5/5; 31/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
            "[CV 5/5; 31/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.559 total time=   0.3s\n",
            "[CV 1/5; 32/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400\n",
            "[CV 1/5; 32/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.535 total time=   0.7s\n",
            "[CV 2/5; 32/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400\n",
            "[CV 2/5; 32/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.542 total time=   0.7s\n",
            "[CV 3/5; 32/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400\n",
            "[CV 3/5; 32/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.597 total time=   0.6s\n",
            "[CV 4/5; 32/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400\n",
            "[CV 4/5; 32/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.601 total time=   0.7s\n",
            "[CV 5/5; 32/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400\n",
            "[CV 5/5; 32/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.559 total time=   0.6s\n",
            "[CV 1/5; 33/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=2000\n",
            "[CV 1/5; 33/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=2000;, score=0.521 total time=   3.1s\n",
            "[CV 2/5; 33/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=2000\n",
            "[CV 2/5; 33/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=2000;, score=0.528 total time=   3.2s\n",
            "[CV 3/5; 33/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=2000\n",
            "[CV 3/5; 33/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=2000;, score=0.590 total time=   3.2s\n",
            "[CV 4/5; 33/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=2000\n",
            "[CV 4/5; 33/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=2000;, score=0.594 total time=   3.2s\n",
            "[CV 5/5; 33/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=2000\n",
            "[CV 5/5; 33/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=2000;, score=0.545 total time=   3.2s\n",
            "[CV 1/5; 34/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
            "[CV 1/5; 34/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.528 total time=   0.3s\n",
            "[CV 2/5; 34/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
            "[CV 2/5; 34/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.542 total time=   0.3s\n",
            "[CV 3/5; 34/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
            "[CV 3/5; 34/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.604 total time=   0.3s\n",
            "[CV 4/5; 34/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
            "[CV 4/5; 34/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.573 total time=   0.3s\n",
            "[CV 5/5; 34/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
            "[CV 5/5; 34/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.587 total time=   0.3s\n",
            "[CV 1/5; 35/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400\n",
            "[CV 1/5; 35/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.549 total time=   0.6s\n",
            "[CV 2/5; 35/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400\n",
            "[CV 2/5; 35/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.549 total time=   0.6s\n",
            "[CV 3/5; 35/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400\n",
            "[CV 3/5; 35/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.590 total time=   0.6s\n",
            "[CV 4/5; 35/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400\n",
            "[CV 4/5; 35/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.552 total time=   0.6s\n",
            "[CV 5/5; 35/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400\n",
            "[CV 5/5; 35/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.559 total time=   0.7s\n",
            "[CV 1/5; 36/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=2000\n",
            "[CV 1/5; 36/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=2000;, score=0.549 total time=   3.2s\n",
            "[CV 2/5; 36/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=2000\n",
            "[CV 2/5; 36/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=2000;, score=0.549 total time=   3.2s\n",
            "[CV 3/5; 36/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=2000\n",
            "[CV 3/5; 36/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=2000;, score=0.604 total time=   3.2s\n",
            "[CV 4/5; 36/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=2000\n",
            "[CV 4/5; 36/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=2000;, score=0.580 total time=   3.3s\n",
            "[CV 5/5; 36/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=2000\n",
            "[CV 5/5; 36/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=2000;, score=0.580 total time=   3.2s\n",
            "[CV 1/5; 37/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
            "[CV 1/5; 37/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.542 total time=   0.3s\n",
            "[CV 2/5; 37/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
            "[CV 2/5; 37/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.542 total time=   0.4s\n",
            "[CV 3/5; 37/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
            "[CV 3/5; 37/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.604 total time=   0.4s\n",
            "[CV 4/5; 37/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
            "[CV 4/5; 37/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.573 total time=   0.3s\n",
            "[CV 5/5; 37/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
            "[CV 5/5; 37/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.566 total time=   0.3s\n",
            "[CV 1/5; 38/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400\n",
            "[CV 1/5; 38/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.549 total time=   0.7s\n",
            "[CV 2/5; 38/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400\n",
            "[CV 2/5; 38/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.528 total time=   0.6s\n",
            "[CV 3/5; 38/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400\n",
            "[CV 3/5; 38/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.611 total time=   0.6s\n",
            "[CV 4/5; 38/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400\n",
            "[CV 4/5; 38/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.587 total time=   0.6s\n",
            "[CV 5/5; 38/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400\n",
            "[CV 5/5; 38/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.566 total time=   0.7s\n",
            "[CV 1/5; 39/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=2000\n",
            "[CV 1/5; 39/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=2000;, score=0.562 total time=   3.2s\n",
            "[CV 2/5; 39/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=2000\n",
            "[CV 2/5; 39/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=2000;, score=0.542 total time=   3.1s\n",
            "[CV 3/5; 39/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=2000\n",
            "[CV 3/5; 39/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=2000;, score=0.604 total time=   3.1s\n",
            "[CV 4/5; 39/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=2000\n",
            "[CV 4/5; 39/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=2000;, score=0.601 total time=   3.3s\n",
            "[CV 5/5; 39/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=2000\n",
            "[CV 5/5; 39/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=2000;, score=0.552 total time=   3.1s\n",
            "[CV 1/5; 40/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
            "[CV 1/5; 40/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.549 total time=   0.3s\n",
            "[CV 2/5; 40/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
            "[CV 2/5; 40/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.556 total time=   0.3s\n",
            "[CV 3/5; 40/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
            "[CV 3/5; 40/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.618 total time=   0.3s\n",
            "[CV 4/5; 40/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
            "[CV 4/5; 40/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.601 total time=   0.3s\n",
            "[CV 5/5; 40/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
            "[CV 5/5; 40/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.531 total time=   0.3s\n",
            "[CV 1/5; 41/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400\n",
            "[CV 1/5; 41/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.528 total time=   0.6s\n",
            "[CV 2/5; 41/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400\n",
            "[CV 2/5; 41/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.556 total time=   0.6s\n",
            "[CV 3/5; 41/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400\n",
            "[CV 3/5; 41/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.597 total time=   0.6s\n",
            "[CV 4/5; 41/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400\n",
            "[CV 4/5; 41/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.587 total time=   0.6s\n",
            "[CV 5/5; 41/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400\n",
            "[CV 5/5; 41/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.566 total time=   0.6s\n",
            "[CV 1/5; 42/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=2000\n",
            "[CV 1/5; 42/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=2000;, score=0.562 total time=   3.1s\n",
            "[CV 2/5; 42/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=2000\n",
            "[CV 2/5; 42/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=2000;, score=0.549 total time=   3.2s\n",
            "[CV 3/5; 42/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=2000\n",
            "[CV 3/5; 42/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=2000;, score=0.604 total time=   3.2s\n",
            "[CV 4/5; 42/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=2000\n",
            "[CV 4/5; 42/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=2000;, score=0.573 total time=   3.2s\n",
            "[CV 5/5; 42/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=2000\n",
            "[CV 5/5; 42/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=2000;, score=0.552 total time=   3.2s\n",
            "[CV 1/5; 43/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
            "[CV 1/5; 43/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.556 total time=   0.3s\n",
            "[CV 2/5; 43/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
            "[CV 2/5; 43/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.542 total time=   0.3s\n",
            "[CV 3/5; 43/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
            "[CV 3/5; 43/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.590 total time=   0.3s\n",
            "[CV 4/5; 43/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
            "[CV 4/5; 43/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.573 total time=   0.3s\n",
            "[CV 5/5; 43/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
            "[CV 5/5; 43/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.552 total time=   0.3s\n",
            "[CV 1/5; 44/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400\n",
            "[CV 1/5; 44/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.562 total time=   0.6s\n",
            "[CV 2/5; 44/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400\n",
            "[CV 2/5; 44/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.521 total time=   0.6s\n",
            "[CV 3/5; 44/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400\n",
            "[CV 3/5; 44/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.597 total time=   0.6s\n",
            "[CV 4/5; 44/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400\n",
            "[CV 4/5; 44/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.580 total time=   0.6s\n",
            "[CV 5/5; 44/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400\n",
            "[CV 5/5; 44/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.552 total time=   0.6s\n",
            "[CV 1/5; 45/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=2000\n",
            "[CV 1/5; 45/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=2000;, score=0.549 total time=   3.1s\n",
            "[CV 2/5; 45/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=2000\n",
            "[CV 2/5; 45/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=2000;, score=0.542 total time=   3.1s\n",
            "[CV 3/5; 45/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=2000\n",
            "[CV 3/5; 45/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=2000;, score=0.604 total time=   3.1s\n",
            "[CV 4/5; 45/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=2000\n",
            "[CV 4/5; 45/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=2000;, score=0.573 total time=   3.1s\n",
            "[CV 5/5; 45/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=2000\n",
            "[CV 5/5; 45/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=2000;, score=0.566 total time=   3.1s\n",
            "[CV 1/5; 46/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
            "[CV 1/5; 46/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.542 total time=   0.3s\n",
            "[CV 2/5; 46/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
            "[CV 2/5; 46/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.562 total time=   0.3s\n",
            "[CV 3/5; 46/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
            "[CV 3/5; 46/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.618 total time=   0.3s\n",
            "[CV 4/5; 46/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
            "[CV 4/5; 46/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.545 total time=   0.3s\n",
            "[CV 5/5; 46/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
            "[CV 5/5; 46/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.545 total time=   0.3s\n",
            "[CV 1/5; 47/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400\n",
            "[CV 1/5; 47/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.535 total time=   0.6s\n",
            "[CV 2/5; 47/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400\n",
            "[CV 2/5; 47/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.556 total time=   0.6s\n",
            "[CV 3/5; 47/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400\n",
            "[CV 3/5; 47/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.597 total time=   0.6s\n",
            "[CV 4/5; 47/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400\n",
            "[CV 4/5; 47/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.594 total time=   0.6s\n",
            "[CV 5/5; 47/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400\n",
            "[CV 5/5; 47/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.587 total time=   0.6s\n",
            "[CV 1/5; 48/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=2000\n",
            "[CV 1/5; 48/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=2000;, score=0.556 total time=   2.9s\n",
            "[CV 2/5; 48/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=2000\n",
            "[CV 2/5; 48/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=2000;, score=0.556 total time=   3.0s\n",
            "[CV 3/5; 48/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=2000\n",
            "[CV 3/5; 48/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=2000;, score=0.604 total time=   3.2s\n",
            "[CV 4/5; 48/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=2000\n",
            "[CV 4/5; 48/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=2000;, score=0.580 total time=   3.0s\n",
            "[CV 5/5; 48/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=2000\n",
            "[CV 5/5; 48/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=2000;, score=0.566 total time=   3.0s\n",
            "[CV 1/5; 49/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
            "[CV 1/5; 49/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.542 total time=   0.3s\n",
            "[CV 2/5; 49/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
            "[CV 2/5; 49/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.521 total time=   0.3s\n",
            "[CV 3/5; 49/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
            "[CV 3/5; 49/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.597 total time=   0.3s\n",
            "[CV 4/5; 49/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
            "[CV 4/5; 49/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.559 total time=   0.3s\n",
            "[CV 5/5; 49/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
            "[CV 5/5; 49/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.552 total time=   0.3s\n",
            "[CV 1/5; 50/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400\n",
            "[CV 1/5; 50/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.556 total time=   0.6s\n",
            "[CV 2/5; 50/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400\n",
            "[CV 2/5; 50/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.528 total time=   0.6s\n",
            "[CV 3/5; 50/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400\n",
            "[CV 3/5; 50/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.597 total time=   0.6s\n",
            "[CV 4/5; 50/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400\n",
            "[CV 4/5; 50/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.573 total time=   0.6s\n",
            "[CV 5/5; 50/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400\n",
            "[CV 5/5; 50/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.573 total time=   0.6s\n",
            "[CV 1/5; 51/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=2000\n",
            "[CV 1/5; 51/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=2000;, score=0.556 total time=   3.0s\n",
            "[CV 2/5; 51/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=2000\n",
            "[CV 2/5; 51/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=2000;, score=0.556 total time=   3.0s\n",
            "[CV 3/5; 51/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=2000\n",
            "[CV 3/5; 51/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=2000;, score=0.597 total time=   3.0s\n",
            "[CV 4/5; 51/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=2000\n",
            "[CV 4/5; 51/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=2000;, score=0.573 total time=   3.0s\n",
            "[CV 5/5; 51/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=2000\n",
            "[CV 5/5; 51/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=2000;, score=0.587 total time=   3.0s\n",
            "[CV 1/5; 52/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
            "[CV 1/5; 52/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.549 total time=   0.3s\n",
            "[CV 2/5; 52/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
            "[CV 2/5; 52/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.549 total time=   0.3s\n",
            "[CV 3/5; 52/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
            "[CV 3/5; 52/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.604 total time=   0.3s\n",
            "[CV 4/5; 52/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
            "[CV 4/5; 52/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.587 total time=   0.3s\n",
            "[CV 5/5; 52/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
            "[CV 5/5; 52/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.566 total time=   0.3s\n",
            "[CV 1/5; 53/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400\n",
            "[CV 1/5; 53/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.542 total time=   0.6s\n",
            "[CV 2/5; 53/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400\n",
            "[CV 2/5; 53/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.535 total time=   0.6s\n",
            "[CV 3/5; 53/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400\n",
            "[CV 3/5; 53/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.611 total time=   0.6s\n",
            "[CV 4/5; 53/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400\n",
            "[CV 4/5; 53/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.608 total time=   0.6s\n",
            "[CV 5/5; 53/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400\n",
            "[CV 5/5; 53/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.545 total time=   0.6s\n",
            "[CV 1/5; 54/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=2000\n",
            "[CV 1/5; 54/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=2000;, score=0.562 total time=   3.0s\n",
            "[CV 2/5; 54/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=2000\n",
            "[CV 2/5; 54/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=2000;, score=0.549 total time=   3.0s\n",
            "[CV 3/5; 54/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=2000\n",
            "[CV 3/5; 54/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=2000;, score=0.611 total time=   3.0s\n",
            "[CV 4/5; 54/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=2000\n",
            "[CV 4/5; 54/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=2000;, score=0.587 total time=   3.0s\n",
            "[CV 5/5; 54/162] START bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=2000\n",
            "[CV 5/5; 54/162] END bootstrap=True, max_depth=40, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=2000;, score=0.566 total time=   3.0s\n",
            "[CV 1/5; 55/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
            "[CV 1/5; 55/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.514 total time=   0.3s\n",
            "[CV 2/5; 55/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
            "[CV 2/5; 55/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.549 total time=   0.3s\n",
            "[CV 3/5; 55/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
            "[CV 3/5; 55/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.590 total time=   0.3s\n",
            "[CV 4/5; 55/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
            "[CV 4/5; 55/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.587 total time=   0.4s\n",
            "[CV 5/5; 55/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
            "[CV 5/5; 55/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.594 total time=   0.4s\n",
            "[CV 1/5; 56/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=400\n",
            "[CV 1/5; 56/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.528 total time=   0.7s\n",
            "[CV 2/5; 56/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=400\n",
            "[CV 2/5; 56/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.549 total time=   0.6s\n",
            "[CV 3/5; 56/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=400\n",
            "[CV 3/5; 56/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.604 total time=   0.6s\n",
            "[CV 4/5; 56/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=400\n",
            "[CV 4/5; 56/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.608 total time=   0.6s\n",
            "[CV 5/5; 56/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=400\n",
            "[CV 5/5; 56/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.566 total time=   0.6s\n",
            "[CV 1/5; 57/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=2000\n",
            "[CV 1/5; 57/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=2000;, score=0.521 total time=   3.2s\n",
            "[CV 2/5; 57/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=2000\n",
            "[CV 2/5; 57/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=2000;, score=0.556 total time=   3.2s\n",
            "[CV 3/5; 57/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=2000\n",
            "[CV 3/5; 57/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=2000;, score=0.611 total time=   3.4s\n",
            "[CV 4/5; 57/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=2000\n",
            "[CV 4/5; 57/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=2000;, score=0.601 total time=   3.2s\n",
            "[CV 5/5; 57/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=2000\n",
            "[CV 5/5; 57/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=2000;, score=0.587 total time=   3.2s\n",
            "[CV 1/5; 58/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
            "[CV 1/5; 58/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.556 total time=   0.3s\n",
            "[CV 2/5; 58/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
            "[CV 2/5; 58/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.528 total time=   0.4s\n",
            "[CV 3/5; 58/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
            "[CV 3/5; 58/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.576 total time=   0.3s\n",
            "[CV 4/5; 58/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
            "[CV 4/5; 58/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.594 total time=   0.3s\n",
            "[CV 5/5; 58/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
            "[CV 5/5; 58/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.573 total time=   0.3s\n",
            "[CV 1/5; 59/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=400\n",
            "[CV 1/5; 59/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.535 total time=   0.6s\n",
            "[CV 2/5; 59/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=400\n",
            "[CV 2/5; 59/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.528 total time=   0.7s\n",
            "[CV 3/5; 59/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=400\n",
            "[CV 3/5; 59/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.583 total time=   0.7s\n",
            "[CV 4/5; 59/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=400\n",
            "[CV 4/5; 59/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.587 total time=   0.6s\n",
            "[CV 5/5; 59/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=400\n",
            "[CV 5/5; 59/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.580 total time=   0.7s\n",
            "[CV 1/5; 60/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=2000\n",
            "[CV 1/5; 60/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=2000;, score=0.535 total time=   3.2s\n",
            "[CV 2/5; 60/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=2000\n",
            "[CV 2/5; 60/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=2000;, score=0.521 total time=   3.2s\n",
            "[CV 3/5; 60/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=2000\n",
            "[CV 3/5; 60/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=2000;, score=0.590 total time=   3.2s\n",
            "[CV 4/5; 60/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=2000\n",
            "[CV 4/5; 60/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=2000;, score=0.608 total time=   3.2s\n",
            "[CV 5/5; 60/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=2000\n",
            "[CV 5/5; 60/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=2000;, score=0.559 total time=   3.2s\n",
            "[CV 1/5; 61/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
            "[CV 1/5; 61/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.556 total time=   0.3s\n",
            "[CV 2/5; 61/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
            "[CV 2/5; 61/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.535 total time=   0.3s\n",
            "[CV 3/5; 61/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
            "[CV 3/5; 61/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.590 total time=   0.3s\n",
            "[CV 4/5; 61/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
            "[CV 4/5; 61/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.566 total time=   0.3s\n",
            "[CV 5/5; 61/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
            "[CV 5/5; 61/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.573 total time=   0.3s\n",
            "[CV 1/5; 62/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=400\n",
            "[CV 1/5; 62/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.549 total time=   0.6s\n",
            "[CV 2/5; 62/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=400\n",
            "[CV 2/5; 62/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.535 total time=   0.6s\n",
            "[CV 3/5; 62/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=400\n",
            "[CV 3/5; 62/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.597 total time=   0.6s\n",
            "[CV 4/5; 62/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=400\n",
            "[CV 4/5; 62/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.580 total time=   0.6s\n",
            "[CV 5/5; 62/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=400\n",
            "[CV 5/5; 62/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.580 total time=   0.6s\n",
            "[CV 1/5; 63/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=2000\n",
            "[CV 1/5; 63/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=2000;, score=0.549 total time=   3.3s\n",
            "[CV 2/5; 63/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=2000\n",
            "[CV 2/5; 63/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=2000;, score=0.535 total time=   3.8s\n",
            "[CV 3/5; 63/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=2000\n",
            "[CV 3/5; 63/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=2000;, score=0.597 total time=   3.1s\n",
            "[CV 4/5; 63/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=2000\n",
            "[CV 4/5; 63/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=2000;, score=0.559 total time=   3.1s\n",
            "[CV 5/5; 63/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=2000\n",
            "[CV 5/5; 63/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=2000;, score=0.566 total time=   3.2s\n",
            "[CV 1/5; 64/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
            "[CV 1/5; 64/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.514 total time=   0.3s\n",
            "[CV 2/5; 64/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
            "[CV 2/5; 64/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.521 total time=   0.3s\n",
            "[CV 3/5; 64/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
            "[CV 3/5; 64/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.604 total time=   0.3s\n",
            "[CV 4/5; 64/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
            "[CV 4/5; 64/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.573 total time=   0.3s\n",
            "[CV 5/5; 64/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
            "[CV 5/5; 64/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.559 total time=   0.3s\n",
            "[CV 1/5; 65/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=400\n",
            "[CV 1/5; 65/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.542 total time=   0.6s\n",
            "[CV 2/5; 65/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=400\n",
            "[CV 2/5; 65/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.556 total time=   0.6s\n",
            "[CV 3/5; 65/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=400\n",
            "[CV 3/5; 65/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.625 total time=   0.6s\n",
            "[CV 4/5; 65/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=400\n",
            "[CV 4/5; 65/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.580 total time=   0.6s\n",
            "[CV 5/5; 65/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=400\n",
            "[CV 5/5; 65/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.552 total time=   0.7s\n",
            "[CV 1/5; 66/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=2000\n",
            "[CV 1/5; 66/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=2000;, score=0.556 total time=   3.2s\n",
            "[CV 2/5; 66/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=2000\n",
            "[CV 2/5; 66/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=2000;, score=0.549 total time=   3.1s\n",
            "[CV 3/5; 66/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=2000\n",
            "[CV 3/5; 66/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=2000;, score=0.597 total time=   3.2s\n",
            "[CV 4/5; 66/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=2000\n",
            "[CV 4/5; 66/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=2000;, score=0.587 total time=   3.1s\n",
            "[CV 5/5; 66/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=2000\n",
            "[CV 5/5; 66/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=2000;, score=0.552 total time=   3.2s\n",
            "[CV 1/5; 67/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
            "[CV 1/5; 67/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.528 total time=   0.3s\n",
            "[CV 2/5; 67/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
            "[CV 2/5; 67/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.521 total time=   0.3s\n",
            "[CV 3/5; 67/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
            "[CV 3/5; 67/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.611 total time=   0.3s\n",
            "[CV 4/5; 67/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
            "[CV 4/5; 67/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.587 total time=   0.3s\n",
            "[CV 5/5; 67/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
            "[CV 5/5; 67/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.552 total time=   0.3s\n",
            "[CV 1/5; 68/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=400\n",
            "[CV 1/5; 68/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.535 total time=   0.6s\n",
            "[CV 2/5; 68/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=400\n",
            "[CV 2/5; 68/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.535 total time=   0.6s\n",
            "[CV 3/5; 68/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=400\n",
            "[CV 3/5; 68/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.597 total time=   0.6s\n",
            "[CV 4/5; 68/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=400\n",
            "[CV 4/5; 68/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.573 total time=   0.6s\n",
            "[CV 5/5; 68/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=400\n",
            "[CV 5/5; 68/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.559 total time=   0.6s\n",
            "[CV 1/5; 69/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=2000\n",
            "[CV 1/5; 69/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=2000;, score=0.556 total time=   3.1s\n",
            "[CV 2/5; 69/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=2000\n",
            "[CV 2/5; 69/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=2000;, score=0.542 total time=   3.1s\n",
            "[CV 3/5; 69/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=2000\n",
            "[CV 3/5; 69/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=2000;, score=0.590 total time=   3.2s\n",
            "[CV 4/5; 69/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=2000\n",
            "[CV 4/5; 69/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=2000;, score=0.587 total time=   3.2s\n",
            "[CV 5/5; 69/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=2000\n",
            "[CV 5/5; 69/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=2000;, score=0.566 total time=   3.1s\n",
            "[CV 1/5; 70/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
            "[CV 1/5; 70/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.542 total time=   0.3s\n",
            "[CV 2/5; 70/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
            "[CV 2/5; 70/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.528 total time=   0.3s\n",
            "[CV 3/5; 70/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
            "[CV 3/5; 70/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.597 total time=   0.3s\n",
            "[CV 4/5; 70/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
            "[CV 4/5; 70/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.552 total time=   0.4s\n",
            "[CV 5/5; 70/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
            "[CV 5/5; 70/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.559 total time=   0.3s\n",
            "[CV 1/5; 71/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=400\n",
            "[CV 1/5; 71/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.549 total time=   0.6s\n",
            "[CV 2/5; 71/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=400\n",
            "[CV 2/5; 71/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.535 total time=   0.6s\n",
            "[CV 3/5; 71/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=400\n",
            "[CV 3/5; 71/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.604 total time=   0.6s\n",
            "[CV 4/5; 71/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=400\n",
            "[CV 4/5; 71/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.573 total time=   0.6s\n",
            "[CV 5/5; 71/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=400\n",
            "[CV 5/5; 71/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.573 total time=   0.6s\n",
            "[CV 1/5; 72/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=2000\n",
            "[CV 1/5; 72/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=2000;, score=0.549 total time=   3.1s\n",
            "[CV 2/5; 72/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=2000\n",
            "[CV 2/5; 72/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=2000;, score=0.549 total time=   3.1s\n",
            "[CV 3/5; 72/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=2000\n",
            "[CV 3/5; 72/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=2000;, score=0.604 total time=   3.1s\n",
            "[CV 4/5; 72/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=2000\n",
            "[CV 4/5; 72/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=2000;, score=0.573 total time=   3.2s\n",
            "[CV 5/5; 72/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=2000\n",
            "[CV 5/5; 72/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=2000;, score=0.573 total time=   3.1s\n",
            "[CV 1/5; 73/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
            "[CV 1/5; 73/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.556 total time=   0.3s\n",
            "[CV 2/5; 73/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
            "[CV 2/5; 73/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.556 total time=   0.3s\n",
            "[CV 3/5; 73/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
            "[CV 3/5; 73/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.618 total time=   0.3s\n",
            "[CV 4/5; 73/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
            "[CV 4/5; 73/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.594 total time=   0.3s\n",
            "[CV 5/5; 73/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
            "[CV 5/5; 73/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.573 total time=   0.3s\n",
            "[CV 1/5; 74/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=400\n",
            "[CV 1/5; 74/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.549 total time=   0.6s\n",
            "[CV 2/5; 74/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=400\n",
            "[CV 2/5; 74/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.576 total time=   0.6s\n",
            "[CV 3/5; 74/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=400\n",
            "[CV 3/5; 74/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.611 total time=   0.6s\n",
            "[CV 4/5; 74/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=400\n",
            "[CV 4/5; 74/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.566 total time=   0.6s\n",
            "[CV 5/5; 74/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=400\n",
            "[CV 5/5; 74/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.573 total time=   0.6s\n",
            "[CV 1/5; 75/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=2000\n",
            "[CV 1/5; 75/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=2000;, score=0.549 total time=   3.2s\n",
            "[CV 2/5; 75/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=2000\n",
            "[CV 2/5; 75/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=2000;, score=0.542 total time=   3.0s\n",
            "[CV 3/5; 75/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=2000\n",
            "[CV 3/5; 75/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=2000;, score=0.604 total time=   3.0s\n",
            "[CV 4/5; 75/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=2000\n",
            "[CV 4/5; 75/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=2000;, score=0.594 total time=   3.0s\n",
            "[CV 5/5; 75/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=2000\n",
            "[CV 5/5; 75/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=2000;, score=0.573 total time=   3.0s\n",
            "[CV 1/5; 76/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
            "[CV 1/5; 76/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.556 total time=   0.3s\n",
            "[CV 2/5; 76/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
            "[CV 2/5; 76/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.542 total time=   0.3s\n",
            "[CV 3/5; 76/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
            "[CV 3/5; 76/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.611 total time=   0.3s\n",
            "[CV 4/5; 76/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
            "[CV 4/5; 76/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.587 total time=   0.3s\n",
            "[CV 5/5; 76/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
            "[CV 5/5; 76/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.559 total time=   0.3s\n",
            "[CV 1/5; 77/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=400\n",
            "[CV 1/5; 77/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.562 total time=   0.6s\n",
            "[CV 2/5; 77/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=400\n",
            "[CV 2/5; 77/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.569 total time=   0.6s\n",
            "[CV 3/5; 77/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=400\n",
            "[CV 3/5; 77/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.604 total time=   0.6s\n",
            "[CV 4/5; 77/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=400\n",
            "[CV 4/5; 77/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.580 total time=   0.6s\n",
            "[CV 5/5; 77/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=400\n",
            "[CV 5/5; 77/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.580 total time=   0.7s\n",
            "[CV 1/5; 78/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=2000\n",
            "[CV 1/5; 78/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=2000;, score=0.549 total time=   3.0s\n",
            "[CV 2/5; 78/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=2000\n",
            "[CV 2/5; 78/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=2000;, score=0.535 total time=   3.0s\n",
            "[CV 3/5; 78/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=2000\n",
            "[CV 3/5; 78/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=2000;, score=0.604 total time=   3.0s\n",
            "[CV 4/5; 78/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=2000\n",
            "[CV 4/5; 78/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=2000;, score=0.573 total time=   3.0s\n",
            "[CV 5/5; 78/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=2000\n",
            "[CV 5/5; 78/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=2000;, score=0.573 total time=   3.0s\n",
            "[CV 1/5; 79/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
            "[CV 1/5; 79/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.549 total time=   0.3s\n",
            "[CV 2/5; 79/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
            "[CV 2/5; 79/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.576 total time=   0.3s\n",
            "[CV 3/5; 79/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
            "[CV 3/5; 79/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.597 total time=   0.3s\n",
            "[CV 4/5; 79/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
            "[CV 4/5; 79/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.566 total time=   0.3s\n",
            "[CV 5/5; 79/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
            "[CV 5/5; 79/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.545 total time=   0.3s\n",
            "[CV 1/5; 80/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=400\n",
            "[CV 1/5; 80/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.542 total time=   0.6s\n",
            "[CV 2/5; 80/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=400\n",
            "[CV 2/5; 80/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.535 total time=   0.6s\n",
            "[CV 3/5; 80/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=400\n",
            "[CV 3/5; 80/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.611 total time=   0.6s\n",
            "[CV 4/5; 80/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=400\n",
            "[CV 4/5; 80/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.580 total time=   0.6s\n",
            "[CV 5/5; 80/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=400\n",
            "[CV 5/5; 80/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.566 total time=   0.6s\n",
            "[CV 1/5; 81/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=2000\n",
            "[CV 1/5; 81/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=2000;, score=0.562 total time=   2.9s\n",
            "[CV 2/5; 81/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=2000\n",
            "[CV 2/5; 81/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=2000;, score=0.542 total time=   2.9s\n",
            "[CV 3/5; 81/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=2000\n",
            "[CV 3/5; 81/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=2000;, score=0.618 total time=   3.0s\n",
            "[CV 4/5; 81/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=2000\n",
            "[CV 4/5; 81/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=2000;, score=0.580 total time=   3.0s\n",
            "[CV 5/5; 81/162] START bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=2000\n",
            "[CV 5/5; 81/162] END bootstrap=True, max_depth=110, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=2000;, score=0.580 total time=   3.1s\n",
            "[CV 1/5; 82/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
            "[CV 1/5; 82/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.514 total time=   0.3s\n",
            "[CV 2/5; 82/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
            "[CV 2/5; 82/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.528 total time=   0.3s\n",
            "[CV 3/5; 82/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
            "[CV 3/5; 82/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.583 total time=   0.3s\n",
            "[CV 4/5; 82/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
            "[CV 4/5; 82/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.601 total time=   0.3s\n",
            "[CV 5/5; 82/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
            "[CV 5/5; 82/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.587 total time=   0.3s\n",
            "[CV 1/5; 83/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400\n",
            "[CV 1/5; 83/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.528 total time=   0.6s\n",
            "[CV 2/5; 83/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400\n",
            "[CV 2/5; 83/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.562 total time=   0.7s\n",
            "[CV 3/5; 83/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400\n",
            "[CV 3/5; 83/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.604 total time=   0.7s\n",
            "[CV 4/5; 83/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400\n",
            "[CV 4/5; 83/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.615 total time=   0.6s\n",
            "[CV 5/5; 83/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400\n",
            "[CV 5/5; 83/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.573 total time=   0.6s\n",
            "[CV 1/5; 84/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=2000\n",
            "[CV 1/5; 84/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=2000;, score=0.528 total time=   3.2s\n",
            "[CV 2/5; 84/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=2000\n",
            "[CV 2/5; 84/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=2000;, score=0.556 total time=   3.3s\n",
            "[CV 3/5; 84/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=2000\n",
            "[CV 3/5; 84/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=2000;, score=0.604 total time=   3.2s\n",
            "[CV 4/5; 84/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=2000\n",
            "[CV 4/5; 84/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=2000;, score=0.601 total time=   3.2s\n",
            "[CV 5/5; 84/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=2000\n",
            "[CV 5/5; 84/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=2000;, score=0.559 total time=   3.3s\n",
            "[CV 1/5; 85/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
            "[CV 1/5; 85/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.514 total time=   0.3s\n",
            "[CV 2/5; 85/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
            "[CV 2/5; 85/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.514 total time=   0.3s\n",
            "[CV 3/5; 85/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
            "[CV 3/5; 85/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.604 total time=   0.3s\n",
            "[CV 4/5; 85/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
            "[CV 4/5; 85/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.580 total time=   0.3s\n",
            "[CV 5/5; 85/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
            "[CV 5/5; 85/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.573 total time=   0.3s\n",
            "[CV 1/5; 86/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400\n",
            "[CV 1/5; 86/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.549 total time=   0.6s\n",
            "[CV 2/5; 86/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400\n",
            "[CV 2/5; 86/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.521 total time=   0.6s\n",
            "[CV 3/5; 86/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400\n",
            "[CV 3/5; 86/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.597 total time=   0.6s\n",
            "[CV 4/5; 86/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400\n",
            "[CV 4/5; 86/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.573 total time=   0.6s\n",
            "[CV 5/5; 86/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400\n",
            "[CV 5/5; 86/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.552 total time=   0.7s\n",
            "[CV 1/5; 87/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=2000\n",
            "[CV 1/5; 87/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=2000;, score=0.542 total time=   3.1s\n",
            "[CV 2/5; 87/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=2000\n",
            "[CV 2/5; 87/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=2000;, score=0.542 total time=   3.1s\n",
            "[CV 3/5; 87/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=2000\n",
            "[CV 3/5; 87/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=2000;, score=0.590 total time=   3.1s\n",
            "[CV 4/5; 87/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=2000\n",
            "[CV 4/5; 87/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=2000;, score=0.580 total time=   3.1s\n",
            "[CV 5/5; 87/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=2000\n",
            "[CV 5/5; 87/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=2000;, score=0.594 total time=   3.1s\n",
            "[CV 1/5; 88/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
            "[CV 1/5; 88/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.556 total time=   0.3s\n",
            "[CV 2/5; 88/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
            "[CV 2/5; 88/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.549 total time=   0.3s\n",
            "[CV 3/5; 88/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
            "[CV 3/5; 88/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.604 total time=   0.3s\n",
            "[CV 4/5; 88/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
            "[CV 4/5; 88/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.545 total time=   0.3s\n",
            "[CV 5/5; 88/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
            "[CV 5/5; 88/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.573 total time=   0.3s\n",
            "[CV 1/5; 89/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400\n",
            "[CV 1/5; 89/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.556 total time=   0.6s\n",
            "[CV 2/5; 89/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400\n",
            "[CV 2/5; 89/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.549 total time=   0.6s\n",
            "[CV 3/5; 89/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400\n",
            "[CV 3/5; 89/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.576 total time=   0.6s\n",
            "[CV 4/5; 89/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400\n",
            "[CV 4/5; 89/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.573 total time=   0.7s\n",
            "[CV 5/5; 89/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400\n",
            "[CV 5/5; 89/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.587 total time=   0.6s\n",
            "[CV 1/5; 90/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=2000\n",
            "[CV 1/5; 90/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=2000;, score=0.549 total time=   3.1s\n",
            "[CV 2/5; 90/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=2000\n",
            "[CV 2/5; 90/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=2000;, score=0.542 total time=   3.1s\n",
            "[CV 3/5; 90/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=2000\n",
            "[CV 3/5; 90/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=2000;, score=0.604 total time=   3.2s\n",
            "[CV 4/5; 90/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=2000\n",
            "[CV 4/5; 90/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=2000;, score=0.566 total time=   3.1s\n",
            "[CV 5/5; 90/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=2000\n",
            "[CV 5/5; 90/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=2000;, score=0.580 total time=   3.3s\n",
            "[CV 1/5; 91/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
            "[CV 1/5; 91/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.556 total time=   0.3s\n",
            "[CV 2/5; 91/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
            "[CV 2/5; 91/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.556 total time=   0.3s\n",
            "[CV 3/5; 91/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
            "[CV 3/5; 91/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.611 total time=   0.3s\n",
            "[CV 4/5; 91/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
            "[CV 4/5; 91/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.601 total time=   0.3s\n",
            "[CV 5/5; 91/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
            "[CV 5/5; 91/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.566 total time=   0.3s\n",
            "[CV 1/5; 92/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400\n",
            "[CV 1/5; 92/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.549 total time=   0.6s\n",
            "[CV 2/5; 92/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400\n",
            "[CV 2/5; 92/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.542 total time=   0.6s\n",
            "[CV 3/5; 92/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400\n",
            "[CV 3/5; 92/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.597 total time=   0.6s\n",
            "[CV 4/5; 92/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400\n",
            "[CV 4/5; 92/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.594 total time=   0.6s\n",
            "[CV 5/5; 92/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400\n",
            "[CV 5/5; 92/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.559 total time=   0.6s\n",
            "[CV 1/5; 93/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=2000\n",
            "[CV 1/5; 93/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=2000;, score=0.549 total time=   3.0s\n",
            "[CV 2/5; 93/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=2000\n",
            "[CV 2/5; 93/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=2000;, score=0.535 total time=   3.1s\n",
            "[CV 3/5; 93/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=2000\n",
            "[CV 3/5; 93/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=2000;, score=0.597 total time=   3.1s\n",
            "[CV 4/5; 93/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=2000\n",
            "[CV 4/5; 93/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=2000;, score=0.601 total time=   3.1s\n",
            "[CV 5/5; 93/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=2000\n",
            "[CV 5/5; 93/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=2000;, score=0.552 total time=   3.1s\n",
            "[CV 1/5; 94/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
            "[CV 1/5; 94/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.535 total time=   0.3s\n",
            "[CV 2/5; 94/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
            "[CV 2/5; 94/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.569 total time=   0.3s\n",
            "[CV 3/5; 94/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
            "[CV 3/5; 94/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.604 total time=   0.3s\n",
            "[CV 4/5; 94/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
            "[CV 4/5; 94/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.587 total time=   0.3s\n",
            "[CV 5/5; 94/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
            "[CV 5/5; 94/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.531 total time=   0.3s\n",
            "[CV 1/5; 95/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400\n",
            "[CV 1/5; 95/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.562 total time=   0.6s\n",
            "[CV 2/5; 95/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400\n",
            "[CV 2/5; 95/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.542 total time=   0.6s\n",
            "[CV 3/5; 95/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400\n",
            "[CV 3/5; 95/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.583 total time=   0.6s\n",
            "[CV 4/5; 95/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400\n",
            "[CV 4/5; 95/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.580 total time=   0.6s\n",
            "[CV 5/5; 95/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400\n",
            "[CV 5/5; 95/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.524 total time=   0.6s\n",
            "[CV 1/5; 96/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=2000\n",
            "[CV 1/5; 96/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=2000;, score=0.556 total time=   3.1s\n",
            "[CV 2/5; 96/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=2000\n",
            "[CV 2/5; 96/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=2000;, score=0.562 total time=   3.1s\n",
            "[CV 3/5; 96/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=2000\n",
            "[CV 3/5; 96/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=2000;, score=0.611 total time=   3.1s\n",
            "[CV 4/5; 96/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=2000\n",
            "[CV 4/5; 96/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=2000;, score=0.580 total time=   3.1s\n",
            "[CV 5/5; 96/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=2000\n",
            "[CV 5/5; 96/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=2000;, score=0.552 total time=   3.1s\n",
            "[CV 1/5; 97/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
            "[CV 1/5; 97/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.556 total time=   0.3s\n",
            "[CV 2/5; 97/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
            "[CV 2/5; 97/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.542 total time=   0.3s\n",
            "[CV 3/5; 97/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
            "[CV 3/5; 97/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.590 total time=   0.3s\n",
            "[CV 4/5; 97/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
            "[CV 4/5; 97/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.587 total time=   0.3s\n",
            "[CV 5/5; 97/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
            "[CV 5/5; 97/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.559 total time=   0.3s\n",
            "[CV 1/5; 98/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400\n",
            "[CV 1/5; 98/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.556 total time=   0.6s\n",
            "[CV 2/5; 98/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400\n",
            "[CV 2/5; 98/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.528 total time=   0.6s\n",
            "[CV 3/5; 98/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400\n",
            "[CV 3/5; 98/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.597 total time=   0.7s\n",
            "[CV 4/5; 98/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400\n",
            "[CV 4/5; 98/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.580 total time=   0.6s\n",
            "[CV 5/5; 98/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400\n",
            "[CV 5/5; 98/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.566 total time=   0.6s\n",
            "[CV 1/5; 99/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=2000\n",
            "[CV 1/5; 99/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=2000;, score=0.542 total time=   3.0s\n",
            "[CV 2/5; 99/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=2000\n",
            "[CV 2/5; 99/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=2000;, score=0.535 total time=   3.0s\n",
            "[CV 3/5; 99/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=2000\n",
            "[CV 3/5; 99/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=2000;, score=0.604 total time=   3.0s\n",
            "[CV 4/5; 99/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=2000\n",
            "[CV 4/5; 99/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=2000;, score=0.552 total time=   3.2s\n",
            "[CV 5/5; 99/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=2000\n",
            "[CV 5/5; 99/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=2000;, score=0.566 total time=   3.1s\n",
            "[CV 1/5; 100/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
            "[CV 1/5; 100/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.549 total time=   0.3s\n",
            "[CV 2/5; 100/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
            "[CV 2/5; 100/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.549 total time=   0.3s\n",
            "[CV 3/5; 100/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
            "[CV 3/5; 100/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.611 total time=   0.3s\n",
            "[CV 4/5; 100/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
            "[CV 4/5; 100/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.566 total time=   0.3s\n",
            "[CV 5/5; 100/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
            "[CV 5/5; 100/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.566 total time=   0.3s\n",
            "[CV 1/5; 101/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400\n",
            "[CV 1/5; 101/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.556 total time=   0.6s\n",
            "[CV 2/5; 101/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400\n",
            "[CV 2/5; 101/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.535 total time=   0.6s\n",
            "[CV 3/5; 101/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400\n",
            "[CV 3/5; 101/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.604 total time=   0.6s\n",
            "[CV 4/5; 101/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400\n",
            "[CV 4/5; 101/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.580 total time=   0.6s\n",
            "[CV 5/5; 101/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400\n",
            "[CV 5/5; 101/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.587 total time=   0.6s\n",
            "[CV 1/5; 102/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=2000\n",
            "[CV 1/5; 102/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=2000;, score=0.556 total time=   3.0s\n",
            "[CV 2/5; 102/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=2000\n",
            "[CV 2/5; 102/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=2000;, score=0.542 total time=   2.9s\n",
            "[CV 3/5; 102/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=2000\n",
            "[CV 3/5; 102/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=2000;, score=0.604 total time=   2.9s\n",
            "[CV 4/5; 102/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=2000\n",
            "[CV 4/5; 102/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=2000;, score=0.573 total time=   3.0s\n",
            "[CV 5/5; 102/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=2000\n",
            "[CV 5/5; 102/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=2000;, score=0.559 total time=   3.0s\n",
            "[CV 1/5; 103/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
            "[CV 1/5; 103/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.556 total time=   0.3s\n",
            "[CV 2/5; 103/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
            "[CV 2/5; 103/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.556 total time=   0.3s\n",
            "[CV 3/5; 103/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
            "[CV 3/5; 103/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.611 total time=   0.3s\n",
            "[CV 4/5; 103/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
            "[CV 4/5; 103/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.559 total time=   0.3s\n",
            "[CV 5/5; 103/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
            "[CV 5/5; 103/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.524 total time=   0.3s\n",
            "[CV 1/5; 104/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400\n",
            "[CV 1/5; 104/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.542 total time=   0.6s\n",
            "[CV 2/5; 104/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400\n",
            "[CV 2/5; 104/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.549 total time=   0.6s\n",
            "[CV 3/5; 104/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400\n",
            "[CV 3/5; 104/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.618 total time=   0.6s\n",
            "[CV 4/5; 104/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400\n",
            "[CV 4/5; 104/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.587 total time=   0.6s\n",
            "[CV 5/5; 104/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400\n",
            "[CV 5/5; 104/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.552 total time=   0.6s\n",
            "[CV 1/5; 105/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=2000\n",
            "[CV 1/5; 105/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=2000;, score=0.556 total time=   3.0s\n",
            "[CV 2/5; 105/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=2000\n",
            "[CV 2/5; 105/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=2000;, score=0.556 total time=   3.0s\n",
            "[CV 3/5; 105/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=2000\n",
            "[CV 3/5; 105/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=2000;, score=0.604 total time=   3.0s\n",
            "[CV 4/5; 105/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=2000\n",
            "[CV 4/5; 105/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=2000;, score=0.573 total time=   3.0s\n",
            "[CV 5/5; 105/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=2000\n",
            "[CV 5/5; 105/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=2000;, score=0.559 total time=   3.0s\n",
            "[CV 1/5; 106/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
            "[CV 1/5; 106/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.528 total time=   0.3s\n",
            "[CV 2/5; 106/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
            "[CV 2/5; 106/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.556 total time=   0.3s\n",
            "[CV 3/5; 106/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
            "[CV 3/5; 106/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.590 total time=   0.3s\n",
            "[CV 4/5; 106/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
            "[CV 4/5; 106/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.608 total time=   0.3s\n",
            "[CV 5/5; 106/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
            "[CV 5/5; 106/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.573 total time=   0.3s\n",
            "[CV 1/5; 107/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400\n",
            "[CV 1/5; 107/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.549 total time=   0.6s\n",
            "[CV 2/5; 107/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400\n",
            "[CV 2/5; 107/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.542 total time=   0.6s\n",
            "[CV 3/5; 107/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400\n",
            "[CV 3/5; 107/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.618 total time=   0.6s\n",
            "[CV 4/5; 107/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400\n",
            "[CV 4/5; 107/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.601 total time=   0.6s\n",
            "[CV 5/5; 107/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400\n",
            "[CV 5/5; 107/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.587 total time=   0.6s\n",
            "[CV 1/5; 108/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=2000\n",
            "[CV 1/5; 108/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=2000;, score=0.562 total time=   3.0s\n",
            "[CV 2/5; 108/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=2000\n",
            "[CV 2/5; 108/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=2000;, score=0.549 total time=   3.0s\n",
            "[CV 3/5; 108/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=2000\n",
            "[CV 3/5; 108/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=2000;, score=0.618 total time=   4.0s\n",
            "[CV 4/5; 108/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=2000\n",
            "[CV 4/5; 108/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=2000;, score=0.594 total time=   3.0s\n",
            "[CV 5/5; 108/162] START bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=2000\n",
            "[CV 5/5; 108/162] END bootstrap=True, max_depth=110, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=2000;, score=0.566 total time=   3.0s\n",
            "[CV 1/5; 109/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
            "[CV 1/5; 109/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.521 total time=   0.3s\n",
            "[CV 2/5; 109/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
            "[CV 2/5; 109/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.576 total time=   0.3s\n",
            "[CV 3/5; 109/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
            "[CV 3/5; 109/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.597 total time=   0.3s\n",
            "[CV 4/5; 109/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
            "[CV 4/5; 109/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.622 total time=   0.4s\n",
            "[CV 5/5; 109/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
            "[CV 5/5; 109/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.587 total time=   0.4s\n",
            "[CV 1/5; 110/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=400\n",
            "[CV 1/5; 110/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.521 total time=   0.6s\n",
            "[CV 2/5; 110/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=400\n",
            "[CV 2/5; 110/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.562 total time=   0.7s\n",
            "[CV 3/5; 110/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=400\n",
            "[CV 3/5; 110/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.611 total time=   0.7s\n",
            "[CV 4/5; 110/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=400\n",
            "[CV 4/5; 110/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.580 total time=   0.6s\n",
            "[CV 5/5; 110/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=400\n",
            "[CV 5/5; 110/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.580 total time=   0.6s\n",
            "[CV 1/5; 111/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=2000\n",
            "[CV 1/5; 111/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=2000;, score=0.528 total time=   3.1s\n",
            "[CV 2/5; 111/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=2000\n",
            "[CV 2/5; 111/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=2000;, score=0.556 total time=   3.2s\n",
            "[CV 3/5; 111/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=2000\n",
            "[CV 3/5; 111/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=2000;, score=0.597 total time=   3.2s\n",
            "[CV 4/5; 111/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=2000\n",
            "[CV 4/5; 111/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=2000;, score=0.587 total time=   3.2s\n",
            "[CV 5/5; 111/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=2000\n",
            "[CV 5/5; 111/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=2000;, score=0.559 total time=   3.2s\n",
            "[CV 1/5; 112/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
            "[CV 1/5; 112/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.556 total time=   0.3s\n",
            "[CV 2/5; 112/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
            "[CV 2/5; 112/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.535 total time=   0.3s\n",
            "[CV 3/5; 112/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
            "[CV 3/5; 112/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.583 total time=   0.3s\n",
            "[CV 4/5; 112/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
            "[CV 4/5; 112/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.587 total time=   0.3s\n",
            "[CV 5/5; 112/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
            "[CV 5/5; 112/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.552 total time=   0.3s\n",
            "[CV 1/5; 113/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=400\n",
            "[CV 1/5; 113/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.535 total time=   0.6s\n",
            "[CV 2/5; 113/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=400\n",
            "[CV 2/5; 113/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.528 total time=   0.6s\n",
            "[CV 3/5; 113/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=400\n",
            "[CV 3/5; 113/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.604 total time=   0.6s\n",
            "[CV 4/5; 113/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=400\n",
            "[CV 4/5; 113/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.580 total time=   0.6s\n",
            "[CV 5/5; 113/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=400\n",
            "[CV 5/5; 113/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.587 total time=   0.6s\n",
            "[CV 1/5; 114/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=2000\n",
            "[CV 1/5; 114/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=2000;, score=0.542 total time=   3.1s\n",
            "[CV 2/5; 114/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=2000\n",
            "[CV 2/5; 114/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=2000;, score=0.528 total time=   3.2s\n",
            "[CV 3/5; 114/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=2000\n",
            "[CV 3/5; 114/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=2000;, score=0.597 total time=   3.1s\n",
            "[CV 4/5; 114/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=2000\n",
            "[CV 4/5; 114/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=2000;, score=0.580 total time=   3.1s\n",
            "[CV 5/5; 114/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=2000\n",
            "[CV 5/5; 114/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=2000;, score=0.580 total time=   3.1s\n",
            "[CV 1/5; 115/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
            "[CV 1/5; 115/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.542 total time=   0.3s\n",
            "[CV 2/5; 115/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
            "[CV 2/5; 115/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.514 total time=   0.3s\n",
            "[CV 3/5; 115/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
            "[CV 3/5; 115/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.604 total time=   0.3s\n",
            "[CV 4/5; 115/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
            "[CV 4/5; 115/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.559 total time=   0.3s\n",
            "[CV 5/5; 115/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
            "[CV 5/5; 115/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.552 total time=   0.3s\n",
            "[CV 1/5; 116/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=400\n",
            "[CV 1/5; 116/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.549 total time=   0.6s\n",
            "[CV 2/5; 116/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=400\n",
            "[CV 2/5; 116/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.542 total time=   0.6s\n",
            "[CV 3/5; 116/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=400\n",
            "[CV 3/5; 116/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.590 total time=   0.6s\n",
            "[CV 4/5; 116/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=400\n",
            "[CV 4/5; 116/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.580 total time=   0.6s\n",
            "[CV 5/5; 116/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=400\n",
            "[CV 5/5; 116/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.566 total time=   0.6s\n",
            "[CV 1/5; 117/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=2000\n",
            "[CV 1/5; 117/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=2000;, score=0.549 total time=   3.0s\n",
            "[CV 2/5; 117/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=2000\n",
            "[CV 2/5; 117/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=2000;, score=0.542 total time=   3.1s\n",
            "[CV 3/5; 117/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=2000\n",
            "[CV 3/5; 117/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=2000;, score=0.604 total time=   3.1s\n",
            "[CV 4/5; 117/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=2000\n",
            "[CV 4/5; 117/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=2000;, score=0.559 total time=   3.3s\n",
            "[CV 5/5; 117/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=2000\n",
            "[CV 5/5; 117/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=2000;, score=0.580 total time=   3.1s\n",
            "[CV 1/5; 118/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
            "[CV 1/5; 118/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.528 total time=   0.3s\n",
            "[CV 2/5; 118/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
            "[CV 2/5; 118/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.556 total time=   0.3s\n",
            "[CV 3/5; 118/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
            "[CV 3/5; 118/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.583 total time=   0.3s\n",
            "[CV 4/5; 118/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
            "[CV 4/5; 118/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.573 total time=   0.3s\n",
            "[CV 5/5; 118/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
            "[CV 5/5; 118/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.559 total time=   0.3s\n",
            "[CV 1/5; 119/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=400\n",
            "[CV 1/5; 119/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.549 total time=   0.6s\n",
            "[CV 2/5; 119/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=400\n",
            "[CV 2/5; 119/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.542 total time=   0.6s\n",
            "[CV 3/5; 119/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=400\n",
            "[CV 3/5; 119/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.611 total time=   0.6s\n",
            "[CV 4/5; 119/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=400\n",
            "[CV 4/5; 119/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.594 total time=   0.6s\n",
            "[CV 5/5; 119/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=400\n",
            "[CV 5/5; 119/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.559 total time=   0.7s\n",
            "[CV 1/5; 120/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=2000\n",
            "[CV 1/5; 120/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=2000;, score=0.556 total time=   3.1s\n",
            "[CV 2/5; 120/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=2000\n",
            "[CV 2/5; 120/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=2000;, score=0.556 total time=   3.1s\n",
            "[CV 3/5; 120/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=2000\n",
            "[CV 3/5; 120/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=2000;, score=0.590 total time=   3.1s\n",
            "[CV 4/5; 120/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=2000\n",
            "[CV 4/5; 120/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=2000;, score=0.580 total time=   3.1s\n",
            "[CV 5/5; 120/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=2000\n",
            "[CV 5/5; 120/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=2000;, score=0.545 total time=   3.2s\n",
            "[CV 1/5; 121/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
            "[CV 1/5; 121/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.569 total time=   0.3s\n",
            "[CV 2/5; 121/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
            "[CV 2/5; 121/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.521 total time=   0.3s\n",
            "[CV 3/5; 121/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
            "[CV 3/5; 121/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.590 total time=   0.3s\n",
            "[CV 4/5; 121/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
            "[CV 4/5; 121/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.587 total time=   0.3s\n",
            "[CV 5/5; 121/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
            "[CV 5/5; 121/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.552 total time=   0.3s\n",
            "[CV 1/5; 122/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=400\n",
            "[CV 1/5; 122/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.556 total time=   0.6s\n",
            "[CV 2/5; 122/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=400\n",
            "[CV 2/5; 122/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.556 total time=   0.6s\n",
            "[CV 3/5; 122/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=400\n",
            "[CV 3/5; 122/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.590 total time=   0.6s\n",
            "[CV 4/5; 122/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=400\n",
            "[CV 4/5; 122/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.580 total time=   0.6s\n",
            "[CV 5/5; 122/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=400\n",
            "[CV 5/5; 122/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.566 total time=   0.6s\n",
            "[CV 1/5; 123/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=2000\n",
            "[CV 1/5; 123/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=2000;, score=0.549 total time=   3.1s\n",
            "[CV 2/5; 123/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=2000\n",
            "[CV 2/5; 123/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=2000;, score=0.535 total time=   3.1s\n",
            "[CV 3/5; 123/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=2000\n",
            "[CV 3/5; 123/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=2000;, score=0.590 total time=   3.1s\n",
            "[CV 4/5; 123/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=2000\n",
            "[CV 4/5; 123/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=2000;, score=0.587 total time=   3.1s\n",
            "[CV 5/5; 123/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=2000\n",
            "[CV 5/5; 123/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=2000;, score=0.566 total time=   3.1s\n",
            "[CV 1/5; 124/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
            "[CV 1/5; 124/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.528 total time=   0.3s\n",
            "[CV 2/5; 124/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
            "[CV 2/5; 124/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.514 total time=   0.3s\n",
            "[CV 3/5; 124/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
            "[CV 3/5; 124/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.597 total time=   0.3s\n",
            "[CV 4/5; 124/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
            "[CV 4/5; 124/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.566 total time=   0.3s\n",
            "[CV 5/5; 124/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
            "[CV 5/5; 124/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.538 total time=   0.3s\n",
            "[CV 1/5; 125/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=400\n",
            "[CV 1/5; 125/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.556 total time=   0.6s\n",
            "[CV 2/5; 125/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=400\n",
            "[CV 2/5; 125/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.562 total time=   0.6s\n",
            "[CV 3/5; 125/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=400\n",
            "[CV 3/5; 125/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.611 total time=   0.6s\n",
            "[CV 4/5; 125/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=400\n",
            "[CV 4/5; 125/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.573 total time=   0.6s\n",
            "[CV 5/5; 125/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=400\n",
            "[CV 5/5; 125/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.566 total time=   0.6s\n",
            "[CV 1/5; 126/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=2000\n",
            "[CV 1/5; 126/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=2000;, score=0.549 total time=   3.1s\n",
            "[CV 2/5; 126/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=2000\n",
            "[CV 2/5; 126/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=2000;, score=0.556 total time=   3.2s\n",
            "[CV 3/5; 126/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=2000\n",
            "[CV 3/5; 126/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=2000;, score=0.611 total time=   3.0s\n",
            "[CV 4/5; 126/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=2000\n",
            "[CV 4/5; 126/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=2000;, score=0.559 total time=   3.0s\n",
            "[CV 5/5; 126/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=2000\n",
            "[CV 5/5; 126/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=2000;, score=0.580 total time=   3.0s\n",
            "[CV 1/5; 127/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
            "[CV 1/5; 127/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.549 total time=   0.3s\n",
            "[CV 2/5; 127/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
            "[CV 2/5; 127/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.562 total time=   0.3s\n",
            "[CV 3/5; 127/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
            "[CV 3/5; 127/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.604 total time=   0.4s\n",
            "[CV 4/5; 127/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
            "[CV 4/5; 127/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.566 total time=   0.3s\n",
            "[CV 5/5; 127/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
            "[CV 5/5; 127/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.531 total time=   0.3s\n",
            "[CV 1/5; 128/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=400\n",
            "[CV 1/5; 128/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.535 total time=   0.6s\n",
            "[CV 2/5; 128/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=400\n",
            "[CV 2/5; 128/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.542 total time=   0.6s\n",
            "[CV 3/5; 128/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=400\n",
            "[CV 3/5; 128/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.590 total time=   0.6s\n",
            "[CV 4/5; 128/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=400\n",
            "[CV 4/5; 128/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.580 total time=   0.6s\n",
            "[CV 5/5; 128/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=400\n",
            "[CV 5/5; 128/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.566 total time=   0.6s\n",
            "[CV 1/5; 129/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=2000\n",
            "[CV 1/5; 129/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=2000;, score=0.556 total time=   3.0s\n",
            "[CV 2/5; 129/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=2000\n",
            "[CV 2/5; 129/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=2000;, score=0.542 total time=   3.0s\n",
            "[CV 3/5; 129/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=2000\n",
            "[CV 3/5; 129/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=2000;, score=0.604 total time=   3.0s\n",
            "[CV 4/5; 129/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=2000\n",
            "[CV 4/5; 129/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=2000;, score=0.580 total time=   3.0s\n",
            "[CV 5/5; 129/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=2000\n",
            "[CV 5/5; 129/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=2000;, score=0.559 total time=   3.0s\n",
            "[CV 1/5; 130/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
            "[CV 1/5; 130/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.542 total time=   0.3s\n",
            "[CV 2/5; 130/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
            "[CV 2/5; 130/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.562 total time=   0.3s\n",
            "[CV 3/5; 130/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
            "[CV 3/5; 130/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.590 total time=   0.3s\n",
            "[CV 4/5; 130/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
            "[CV 4/5; 130/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.587 total time=   0.3s\n",
            "[CV 5/5; 130/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
            "[CV 5/5; 130/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.538 total time=   0.3s\n",
            "[CV 1/5; 131/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=400\n",
            "[CV 1/5; 131/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.542 total time=   0.6s\n",
            "[CV 2/5; 131/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=400\n",
            "[CV 2/5; 131/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.562 total time=   0.6s\n",
            "[CV 3/5; 131/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=400\n",
            "[CV 3/5; 131/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.604 total time=   0.6s\n",
            "[CV 4/5; 131/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=400\n",
            "[CV 4/5; 131/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.580 total time=   0.6s\n",
            "[CV 5/5; 131/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=400\n",
            "[CV 5/5; 131/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.552 total time=   0.6s\n",
            "[CV 1/5; 132/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=2000\n",
            "[CV 1/5; 132/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=2000;, score=0.562 total time=   2.9s\n",
            "[CV 2/5; 132/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=2000\n",
            "[CV 2/5; 132/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=2000;, score=0.542 total time=   3.0s\n",
            "[CV 3/5; 132/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=2000\n",
            "[CV 3/5; 132/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=2000;, score=0.611 total time=   2.9s\n",
            "[CV 4/5; 132/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=2000\n",
            "[CV 4/5; 132/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=2000;, score=0.580 total time=   3.0s\n",
            "[CV 5/5; 132/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=2000\n",
            "[CV 5/5; 132/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=2000;, score=0.566 total time=   3.1s\n",
            "[CV 1/5; 133/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
            "[CV 1/5; 133/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.556 total time=   0.3s\n",
            "[CV 2/5; 133/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
            "[CV 2/5; 133/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.569 total time=   0.3s\n",
            "[CV 3/5; 133/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
            "[CV 3/5; 133/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.590 total time=   0.3s\n",
            "[CV 4/5; 133/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
            "[CV 4/5; 133/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.594 total time=   0.3s\n",
            "[CV 5/5; 133/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
            "[CV 5/5; 133/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.559 total time=   0.3s\n",
            "[CV 1/5; 134/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=400\n",
            "[CV 1/5; 134/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.562 total time=   0.6s\n",
            "[CV 2/5; 134/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=400\n",
            "[CV 2/5; 134/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.556 total time=   0.6s\n",
            "[CV 3/5; 134/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=400\n",
            "[CV 3/5; 134/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.611 total time=   0.6s\n",
            "[CV 4/5; 134/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=400\n",
            "[CV 4/5; 134/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.580 total time=   0.6s\n",
            "[CV 5/5; 134/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=400\n",
            "[CV 5/5; 134/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.566 total time=   0.6s\n",
            "[CV 1/5; 135/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=2000\n",
            "[CV 1/5; 135/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=2000;, score=0.562 total time=   2.9s\n",
            "[CV 2/5; 135/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=2000\n",
            "[CV 2/5; 135/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=2000;, score=0.549 total time=   3.0s\n",
            "[CV 3/5; 135/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=2000\n",
            "[CV 3/5; 135/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=2000;, score=0.611 total time=   3.0s\n",
            "[CV 4/5; 135/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=2000\n",
            "[CV 4/5; 135/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=2000;, score=0.587 total time=   3.1s\n",
            "[CV 5/5; 135/162] START bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=2000\n",
            "[CV 5/5; 135/162] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=2000;, score=0.566 total time=   3.0s\n",
            "[CV 1/5; 136/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
            "[CV 1/5; 136/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.528 total time=   0.3s\n",
            "[CV 2/5; 136/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
            "[CV 2/5; 136/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.535 total time=   0.3s\n",
            "[CV 3/5; 136/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
            "[CV 3/5; 136/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.590 total time=   0.4s\n",
            "[CV 4/5; 136/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
            "[CV 4/5; 136/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.608 total time=   0.3s\n",
            "[CV 5/5; 136/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
            "[CV 5/5; 136/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.580 total time=   0.3s\n",
            "[CV 1/5; 137/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400\n",
            "[CV 1/5; 137/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.528 total time=   0.6s\n",
            "[CV 2/5; 137/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400\n",
            "[CV 2/5; 137/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.549 total time=   0.7s\n",
            "[CV 3/5; 137/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400\n",
            "[CV 3/5; 137/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.590 total time=   0.7s\n",
            "[CV 4/5; 137/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400\n",
            "[CV 4/5; 137/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.615 total time=   0.7s\n",
            "[CV 5/5; 137/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400\n",
            "[CV 5/5; 137/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.552 total time=   0.7s\n",
            "[CV 1/5; 138/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=2000\n",
            "[CV 1/5; 138/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=2000;, score=0.514 total time=   3.2s\n",
            "[CV 2/5; 138/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=2000\n",
            "[CV 2/5; 138/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=2000;, score=0.549 total time=   3.2s\n",
            "[CV 3/5; 138/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=2000\n",
            "[CV 3/5; 138/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=2000;, score=0.604 total time=   3.3s\n",
            "[CV 4/5; 138/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=2000\n",
            "[CV 4/5; 138/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=2000;, score=0.601 total time=   3.3s\n",
            "[CV 5/5; 138/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=2000\n",
            "[CV 5/5; 138/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=2000;, score=0.573 total time=   3.2s\n",
            "[CV 1/5; 139/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
            "[CV 1/5; 139/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.514 total time=   0.3s\n",
            "[CV 2/5; 139/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
            "[CV 2/5; 139/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.556 total time=   0.3s\n",
            "[CV 3/5; 139/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
            "[CV 3/5; 139/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.590 total time=   0.3s\n",
            "[CV 4/5; 139/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
            "[CV 4/5; 139/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.587 total time=   0.3s\n",
            "[CV 5/5; 139/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
            "[CV 5/5; 139/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.566 total time=   0.3s\n",
            "[CV 1/5; 140/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400\n",
            "[CV 1/5; 140/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.535 total time=   0.6s\n",
            "[CV 2/5; 140/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400\n",
            "[CV 2/5; 140/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.521 total time=   0.6s\n",
            "[CV 3/5; 140/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400\n",
            "[CV 3/5; 140/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.618 total time=   0.7s\n",
            "[CV 4/5; 140/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400\n",
            "[CV 4/5; 140/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.601 total time=   0.6s\n",
            "[CV 5/5; 140/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400\n",
            "[CV 5/5; 140/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.580 total time=   0.6s\n",
            "[CV 1/5; 141/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=2000\n",
            "[CV 1/5; 141/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=2000;, score=0.535 total time=   3.1s\n",
            "[CV 2/5; 141/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=2000\n",
            "[CV 2/5; 141/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=2000;, score=0.535 total time=   3.2s\n",
            "[CV 3/5; 141/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=2000\n",
            "[CV 3/5; 141/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=2000;, score=0.590 total time=   3.1s\n",
            "[CV 4/5; 141/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=2000\n",
            "[CV 4/5; 141/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=2000;, score=0.594 total time=   3.2s\n",
            "[CV 5/5; 141/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=2000\n",
            "[CV 5/5; 141/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=2000;, score=0.580 total time=   3.2s\n",
            "[CV 1/5; 142/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
            "[CV 1/5; 142/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.549 total time=   0.3s\n",
            "[CV 2/5; 142/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
            "[CV 2/5; 142/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.549 total time=   0.3s\n",
            "[CV 3/5; 142/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
            "[CV 3/5; 142/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.597 total time=   0.3s\n",
            "[CV 4/5; 142/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
            "[CV 4/5; 142/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.566 total time=   0.3s\n",
            "[CV 5/5; 142/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
            "[CV 5/5; 142/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.580 total time=   0.3s\n",
            "[CV 1/5; 143/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400\n",
            "[CV 1/5; 143/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.542 total time=   0.6s\n",
            "[CV 2/5; 143/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400\n",
            "[CV 2/5; 143/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.569 total time=   0.6s\n",
            "[CV 3/5; 143/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400\n",
            "[CV 3/5; 143/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.604 total time=   0.6s\n",
            "[CV 4/5; 143/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400\n",
            "[CV 4/5; 143/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.580 total time=   0.6s\n",
            "[CV 5/5; 143/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400\n",
            "[CV 5/5; 143/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.573 total time=   0.6s\n",
            "[CV 1/5; 144/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=2000\n",
            "[CV 1/5; 144/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=2000;, score=0.549 total time=   3.0s\n",
            "[CV 2/5; 144/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=2000\n",
            "[CV 2/5; 144/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=2000;, score=0.549 total time=   3.2s\n",
            "[CV 3/5; 144/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=2000\n",
            "[CV 3/5; 144/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=2000;, score=0.604 total time=   3.1s\n",
            "[CV 4/5; 144/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=2000\n",
            "[CV 4/5; 144/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=2000;, score=0.573 total time=   3.1s\n",
            "[CV 5/5; 144/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=2000\n",
            "[CV 5/5; 144/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=2000;, score=0.580 total time=   3.1s\n",
            "[CV 1/5; 145/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
            "[CV 1/5; 145/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.549 total time=   0.3s\n",
            "[CV 2/5; 145/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
            "[CV 2/5; 145/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.562 total time=   0.4s\n",
            "[CV 3/5; 145/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
            "[CV 3/5; 145/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.597 total time=   0.3s\n",
            "[CV 4/5; 145/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
            "[CV 4/5; 145/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.608 total time=   0.3s\n",
            "[CV 5/5; 145/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
            "[CV 5/5; 145/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.573 total time=   0.3s\n",
            "[CV 1/5; 146/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400\n",
            "[CV 1/5; 146/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.549 total time=   0.6s\n",
            "[CV 2/5; 146/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400\n",
            "[CV 2/5; 146/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.528 total time=   0.6s\n",
            "[CV 3/5; 146/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400\n",
            "[CV 3/5; 146/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.583 total time=   0.6s\n",
            "[CV 4/5; 146/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400\n",
            "[CV 4/5; 146/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.566 total time=   0.6s\n",
            "[CV 5/5; 146/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400\n",
            "[CV 5/5; 146/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.559 total time=   0.7s\n",
            "[CV 1/5; 147/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=2000\n",
            "[CV 1/5; 147/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=2000;, score=0.549 total time=   3.0s\n",
            "[CV 2/5; 147/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=2000\n",
            "[CV 2/5; 147/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=2000;, score=0.556 total time=   3.1s\n",
            "[CV 3/5; 147/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=2000\n",
            "[CV 3/5; 147/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=2000;, score=0.604 total time=   3.1s\n",
            "[CV 4/5; 147/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=2000\n",
            "[CV 4/5; 147/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=2000;, score=0.601 total time=   3.1s\n",
            "[CV 5/5; 147/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=2000\n",
            "[CV 5/5; 147/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=2000;, score=0.566 total time=   3.1s\n",
            "[CV 1/5; 148/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
            "[CV 1/5; 148/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.535 total time=   0.3s\n",
            "[CV 2/5; 148/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
            "[CV 2/5; 148/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.528 total time=   0.3s\n",
            "[CV 3/5; 148/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
            "[CV 3/5; 148/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.597 total time=   0.3s\n",
            "[CV 4/5; 148/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
            "[CV 4/5; 148/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.538 total time=   0.3s\n",
            "[CV 5/5; 148/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
            "[CV 5/5; 148/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.601 total time=   0.3s\n",
            "[CV 1/5; 149/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400\n",
            "[CV 1/5; 149/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.542 total time=   0.6s\n",
            "[CV 2/5; 149/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400\n",
            "[CV 2/5; 149/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.521 total time=   0.6s\n",
            "[CV 3/5; 149/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400\n",
            "[CV 3/5; 149/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.583 total time=   0.6s\n",
            "[CV 4/5; 149/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400\n",
            "[CV 4/5; 149/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.587 total time=   0.6s\n",
            "[CV 5/5; 149/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400\n",
            "[CV 5/5; 149/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.573 total time=   0.6s\n",
            "[CV 1/5; 150/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=2000\n",
            "[CV 1/5; 150/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=2000;, score=0.556 total time=   3.0s\n",
            "[CV 2/5; 150/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=2000\n",
            "[CV 2/5; 150/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=2000;, score=0.549 total time=   3.1s\n",
            "[CV 3/5; 150/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=2000\n",
            "[CV 3/5; 150/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=2000;, score=0.590 total time=   3.2s\n",
            "[CV 4/5; 150/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=2000\n",
            "[CV 4/5; 150/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=2000;, score=0.580 total time=   3.2s\n",
            "[CV 5/5; 150/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=2000\n",
            "[CV 5/5; 150/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=2000;, score=0.545 total time=   3.1s\n",
            "[CV 1/5; 151/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
            "[CV 1/5; 151/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.549 total time=   0.3s\n",
            "[CV 2/5; 151/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
            "[CV 2/5; 151/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.535 total time=   0.3s\n",
            "[CV 3/5; 151/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
            "[CV 3/5; 151/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.597 total time=   0.3s\n",
            "[CV 4/5; 151/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
            "[CV 4/5; 151/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.566 total time=   0.3s\n",
            "[CV 5/5; 151/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
            "[CV 5/5; 151/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.531 total time=   0.3s\n",
            "[CV 1/5; 152/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400\n",
            "[CV 1/5; 152/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.549 total time=   0.6s\n",
            "[CV 2/5; 152/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400\n",
            "[CV 2/5; 152/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.535 total time=   0.6s\n",
            "[CV 3/5; 152/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400\n",
            "[CV 3/5; 152/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.597 total time=   0.6s\n",
            "[CV 4/5; 152/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400\n",
            "[CV 4/5; 152/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.601 total time=   0.6s\n",
            "[CV 5/5; 152/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400\n",
            "[CV 5/5; 152/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.545 total time=   0.6s\n",
            "[CV 1/5; 153/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=2000\n",
            "[CV 1/5; 153/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=2000;, score=0.549 total time=   3.1s\n",
            "[CV 2/5; 153/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=2000\n",
            "[CV 2/5; 153/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=2000;, score=0.542 total time=   3.2s\n",
            "[CV 3/5; 153/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=2000\n",
            "[CV 3/5; 153/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=2000;, score=0.604 total time=   3.1s\n",
            "[CV 4/5; 153/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=2000\n",
            "[CV 4/5; 153/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=2000;, score=0.559 total time=   3.1s\n",
            "[CV 5/5; 153/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=2000\n",
            "[CV 5/5; 153/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=2000;, score=0.559 total time=   3.0s\n",
            "[CV 1/5; 154/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
            "[CV 1/5; 154/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.562 total time=   0.3s\n",
            "[CV 2/5; 154/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
            "[CV 2/5; 154/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.562 total time=   0.3s\n",
            "[CV 3/5; 154/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
            "[CV 3/5; 154/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.604 total time=   0.3s\n",
            "[CV 4/5; 154/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
            "[CV 4/5; 154/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.587 total time=   0.3s\n",
            "[CV 5/5; 154/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
            "[CV 5/5; 154/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.566 total time=   0.4s\n",
            "[CV 1/5; 155/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400\n",
            "[CV 1/5; 155/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.542 total time=   1.0s\n",
            "[CV 2/5; 155/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400\n",
            "[CV 2/5; 155/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.528 total time=   1.0s\n",
            "[CV 3/5; 155/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400\n",
            "[CV 3/5; 155/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.604 total time=   0.8s\n",
            "[CV 4/5; 155/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400\n",
            "[CV 4/5; 155/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.573 total time=   0.6s\n",
            "[CV 5/5; 155/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400\n",
            "[CV 5/5; 155/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.552 total time=   0.6s\n",
            "[CV 1/5; 156/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=2000\n",
            "[CV 1/5; 156/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=2000;, score=0.562 total time=   2.9s\n",
            "[CV 2/5; 156/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=2000\n",
            "[CV 2/5; 156/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=2000;, score=0.542 total time=   3.0s\n",
            "[CV 3/5; 156/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=2000\n",
            "[CV 3/5; 156/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=2000;, score=0.604 total time=   3.0s\n",
            "[CV 4/5; 156/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=2000\n",
            "[CV 4/5; 156/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=2000;, score=0.580 total time=   3.0s\n",
            "[CV 5/5; 156/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=2000\n",
            "[CV 5/5; 156/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=2000;, score=0.559 total time=   3.1s\n",
            "[CV 1/5; 157/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
            "[CV 1/5; 157/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.535 total time=   0.3s\n",
            "[CV 2/5; 157/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
            "[CV 2/5; 157/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.569 total time=   0.3s\n",
            "[CV 3/5; 157/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
            "[CV 3/5; 157/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.590 total time=   0.3s\n",
            "[CV 4/5; 157/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
            "[CV 4/5; 157/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.587 total time=   0.3s\n",
            "[CV 5/5; 157/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
            "[CV 5/5; 157/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.545 total time=   0.3s\n",
            "[CV 1/5; 158/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400\n",
            "[CV 1/5; 158/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.528 total time=   0.6s\n",
            "[CV 2/5; 158/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400\n",
            "[CV 2/5; 158/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.542 total time=   0.6s\n",
            "[CV 3/5; 158/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400\n",
            "[CV 3/5; 158/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.611 total time=   0.6s\n",
            "[CV 4/5; 158/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400\n",
            "[CV 4/5; 158/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.573 total time=   0.6s\n",
            "[CV 5/5; 158/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400\n",
            "[CV 5/5; 158/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.566 total time=   0.6s\n",
            "[CV 1/5; 159/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=2000\n",
            "[CV 1/5; 159/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=2000;, score=0.556 total time=   3.0s\n",
            "[CV 2/5; 159/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=2000\n",
            "[CV 2/5; 159/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=2000;, score=0.549 total time=   3.0s\n",
            "[CV 3/5; 159/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=2000\n",
            "[CV 3/5; 159/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=2000;, score=0.604 total time=   3.0s\n",
            "[CV 4/5; 159/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=2000\n",
            "[CV 4/5; 159/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=2000;, score=0.580 total time=   3.1s\n",
            "[CV 5/5; 159/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=2000\n",
            "[CV 5/5; 159/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=2000;, score=0.573 total time=   3.0s\n",
            "[CV 1/5; 160/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
            "[CV 1/5; 160/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.542 total time=   0.3s\n",
            "[CV 2/5; 160/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
            "[CV 2/5; 160/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.556 total time=   0.3s\n",
            "[CV 3/5; 160/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
            "[CV 3/5; 160/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.625 total time=   0.3s\n",
            "[CV 4/5; 160/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
            "[CV 4/5; 160/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.566 total time=   0.3s\n",
            "[CV 5/5; 160/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
            "[CV 5/5; 160/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.552 total time=   0.3s\n",
            "[CV 1/5; 161/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400\n",
            "[CV 1/5; 161/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.556 total time=   0.6s\n",
            "[CV 2/5; 161/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400\n",
            "[CV 2/5; 161/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.549 total time=   0.6s\n",
            "[CV 3/5; 161/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400\n",
            "[CV 3/5; 161/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.611 total time=   0.6s\n",
            "[CV 4/5; 161/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400\n",
            "[CV 4/5; 161/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.580 total time=   0.6s\n",
            "[CV 5/5; 161/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400\n",
            "[CV 5/5; 161/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.580 total time=   0.6s\n",
            "[CV 1/5; 162/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=2000\n",
            "[CV 1/5; 162/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=2000;, score=0.556 total time=   3.0s\n",
            "[CV 2/5; 162/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=2000\n",
            "[CV 2/5; 162/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=2000;, score=0.542 total time=   3.0s\n",
            "[CV 3/5; 162/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=2000\n",
            "[CV 3/5; 162/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=2000;, score=0.618 total time=   2.9s\n",
            "[CV 4/5; 162/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=2000\n",
            "[CV 4/5; 162/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=2000;, score=0.580 total time=   3.0s\n",
            "[CV 5/5; 162/162] START bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=2000\n",
            "[CV 5/5; 162/162] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=2000;, score=0.559 total time=   3.0s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['mean_fit_time',\n",
              " 'mean_score_time',\n",
              " 'mean_test_score',\n",
              " 'param_bootstrap',\n",
              " 'param_max_depth',\n",
              " 'param_max_features',\n",
              " 'param_min_samples_leaf',\n",
              " 'param_min_samples_split',\n",
              " 'param_n_estimators',\n",
              " 'params',\n",
              " 'rank_test_score',\n",
              " 'split0_test_score',\n",
              " 'split1_test_score',\n",
              " 'split2_test_score',\n",
              " 'split3_test_score',\n",
              " 'split4_test_score',\n",
              " 'std_fit_time',\n",
              " 'std_score_time',\n",
              " 'std_test_score']"
            ]
          },
          "metadata": {},
          "execution_count": 203
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction_rf = clf.predict(X_test_2d)"
      ],
      "metadata": {
        "id": "8U9NsS-wQmNJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clf.cv_results_[\"mean_test_score\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c53a65e4-95b2-4760-fbd5-7a0d01f66640",
        "id": "9vMbE0YDVi9j"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.57804002, 0.57246503, 0.57248446, 0.56132479, 0.55996503,\n",
              "       0.56970668, 0.5654526 , 0.5752331 , 0.56406371, 0.57105672,\n",
              "       0.56269425, 0.56825952, 0.56128594, 0.55573038, 0.57104701,\n",
              "       0.55294289, 0.56825952, 0.56408314, 0.56960956, 0.57384421,\n",
              "       0.57520396, 0.56267483, 0.57241647, 0.57244561, 0.57382479,\n",
              "       0.57659285, 0.56824981, 0.5794289 , 0.58083722, 0.56827894,\n",
              "       0.56829837, 0.56689005, 0.55574981, 0.56689005, 0.55987762,\n",
              "       0.57244561, 0.56547203, 0.56826923, 0.5724359 , 0.57101787,\n",
              "       0.56688034, 0.56823038, 0.56267483, 0.56268454, 0.56686092,\n",
              "       0.56262626, 0.57386364, 0.57242618, 0.55432207, 0.56548174,\n",
              "       0.5738345 , 0.57104701, 0.56826923, 0.57521368, 0.56691919,\n",
              "       0.57107615, 0.57526224, 0.56551088, 0.5627331 , 0.5627331 ,\n",
              "       0.56408314, 0.56827894, 0.56128594, 0.5543512 , 0.57101787,\n",
              "       0.56824981, 0.55991647, 0.55990676, 0.56826923, 0.55571096,\n",
              "       0.56687063, 0.56964841, 0.57939977, 0.57519425, 0.57245532,\n",
              "       0.5710373 , 0.57939005, 0.56687063, 0.56682207, 0.56687063,\n",
              "       0.57661228, 0.56276224, 0.57665113, 0.56966783, 0.55715812,\n",
              "       0.55850816, 0.56968726, 0.56544289, 0.56827894, 0.56825952,\n",
              "       0.57801088, 0.56826923, 0.56688034, 0.56544289, 0.55847902,\n",
              "       0.57240676, 0.56687063, 0.56548174, 0.55988733, 0.56824009,\n",
              "       0.57245532, 0.5668512 , 0.56122766, 0.56963869, 0.56962898,\n",
              "       0.57108586, 0.5794289 , 0.57800117, 0.58084693, 0.57105672,\n",
              "       0.56548174, 0.56269425, 0.56689977, 0.56550117, 0.55432207,\n",
              "       0.56548174, 0.56686092, 0.55990676, 0.57104701, 0.5654526 ,\n",
              "       0.56408314, 0.56964841, 0.56549145, 0.5487568 , 0.57380536,\n",
              "       0.57102758, 0.56263598, 0.56270396, 0.56824981, 0.56406371,\n",
              "       0.56824009, 0.57242618, 0.57382479, 0.57520396, 0.57521368,\n",
              "       0.56831779, 0.56689977, 0.56829837, 0.56271368, 0.57108586,\n",
              "       0.56690948, 0.56825952, 0.57382479, 0.57104701, 0.5780303 ,\n",
              "       0.55711927, 0.5752331 , 0.55991647, 0.5613345 , 0.56406371,\n",
              "       0.55569153, 0.56548174, 0.56266511, 0.57660256, 0.55989705,\n",
              "       0.56963869, 0.56546232, 0.56408314, 0.5724359 , 0.56822067,\n",
              "       0.57522339, 0.57102758])"
            ]
          },
          "metadata": {},
          "execution_count": 205
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction_rf = clf.predict(X_test_2d)"
      ],
      "metadata": {
        "id": "gCZc7tAzVi9k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, prediction_rf))\n",
        "\n",
        "cm = confusion_matrix(y_test, prediction_rf)\n",
        "\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
        "\n",
        "disp.plot(cmap=plt.cm.Blues)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 440
        },
        "outputId": "4026820a-0cf2-4468-c058-8fe21de50daa",
        "id": "ZgJm8_D_Vi9l"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.54      0.49      0.51        92\n",
            "           1       0.51      0.56      0.53        88\n",
            "\n",
            "    accuracy                           0.52       180\n",
            "   macro avg       0.52      0.52      0.52       180\n",
            "weighted avg       0.52      0.52      0.52       180\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Neural Network"
      ],
      "metadata": {
        "id": "TLiOUT-cTYFM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, y_train = resample(X_train_raw, y_train_raw, replace=True, n_samples=3000, random_state=42)"
      ],
      "metadata": {
        "id": "HcQXMeavIO1V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train.shape)\n",
        "print(y_train.shape)"
      ],
      "metadata": {
        "id": "QD_Hg3bEIWPF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ca34bb10-56f9-4c8e-f98f-067e56eaf345"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([3000, 612, 768])\n",
            "torch.Size([3000])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "del(X_train_raw, y_train_raw, final_embeddings, labels)\n",
        "gc.collect()"
      ],
      "metadata": {
        "id": "kTmR1AUUKwnK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "909195a8-e373-40a4-e248-b73d642b62fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "970"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np_tensor_X = X_train.numpy()\n",
        "np_tensor_y = y_train.numpy()\n",
        "X_train = tf.convert_to_tensor(np_tensor_X)\n",
        "y_train = tf.convert_to_tensor(np_tensor_y)\n",
        "\n",
        "\n",
        "np_tensor_X = X_test.numpy()\n",
        "np_tensor_y = y_test.numpy()\n",
        "X_test = tf.convert_to_tensor(np_tensor_X)\n",
        "y_test = tf.convert_to_tensor(np_tensor_y)"
      ],
      "metadata": {
        "id": "vz8ywmNX3Lit"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "del(np_tensor_X, np_tensor_y)\n",
        "gc.collect()"
      ],
      "metadata": {
        "id": "MsHZUhE8LCmK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "196f2190-7d6b-4fb3-c971-3f12f0d64492"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "132"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train.shape)\n",
        "print(y_train.shape)\n",
        "print(X_test.shape)\n",
        "print(y_test.shape)"
      ],
      "metadata": {
        "id": "WimPJkyG3LlT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6d22c534-a5eb-4333-c90a-38ffe3a3f376"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(3000, 612, 768)\n",
            "(3000,)\n",
            "(180, 612, 768)\n",
            "(180,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.models.Sequential()\n",
        "model.add(tf.keras.layers.Dense(50, batch_input_shape=(None, 612, 768), activation=\"relu\"))\n",
        "# model.add(tf.keras.layers.Dropout(.5, input_shape=(2,)))\n",
        "\n",
        "model.add(tf.keras.layers.Dense(40, activation=\"relu\"))\n",
        "model.add(tf.keras.layers.Dropout(.5, input_shape=(2,)))\n",
        "\n",
        "\n",
        "model.add(tf.keras.layers.Dense(30, activation=\"relu\"))\n",
        "# model.add(tf.keras.layers.Dropout(.5, input_shape=(2,)))\n",
        "\n",
        "model.add(tf.keras.layers.Dense(20, activation=\"relu\"))\n",
        "model.add(tf.keras.layers.Dropout(.5, input_shape=(2,)))\n",
        "\n",
        "\n",
        "model.add(tf.keras.layers.Dense(10, activation=\"relu\"))\n",
        "# model.add(tf.keras.layers.Dropout(.5, input_shape=(2,)))\n",
        "\n",
        "\n",
        "model.add(tf.keras.layers.GlobalAveragePooling1D())\n",
        "\n",
        "\n",
        "model.add(tf.keras.layers.Dense(5, activation=\"relu\"))\n",
        "#model.add(tf.keras.layers.Dropout(.2, input_shape=(2,)))\n",
        "\n",
        "\n",
        "model.add(tf.keras.layers.Dense(1, activation=\"sigmoid\"))"
      ],
      "metadata": {
        "id": "D-pVt3OvTP4B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "epochs = 50\n",
        "\n",
        "opt = tf.keras.optimizers.Adam(learning_rate=0.001)\n",
        "\n",
        "model.compile(\n",
        "   loss = 'binary_crossentropy', optimizer = opt, metrics = [\"accuracy\"]\n",
        ")"
      ],
      "metadata": {
        "id": "f9jlbFwm3Lqm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0gmgcQNM4rKM",
        "outputId": "9b751c61-a388-48ee-86c9-a4a4f27421b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_13\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_91 (Dense)            (None, 612, 50)           38450     \n",
            "                                                                 \n",
            " dense_92 (Dense)            (None, 612, 40)           2040      \n",
            "                                                                 \n",
            " dropout_26 (Dropout)        (None, 612, 40)           0         \n",
            "                                                                 \n",
            " dense_93 (Dense)            (None, 612, 30)           1230      \n",
            "                                                                 \n",
            " dense_94 (Dense)            (None, 612, 20)           620       \n",
            "                                                                 \n",
            " dropout_27 (Dropout)        (None, 612, 20)           0         \n",
            "                                                                 \n",
            " dense_95 (Dense)            (None, 612, 10)           210       \n",
            "                                                                 \n",
            " global_average_pooling1d_13  (None, 10)               0         \n",
            "  (GlobalAveragePooling1D)                                       \n",
            "                                                                 \n",
            " dense_96 (Dense)            (None, 5)                 55        \n",
            "                                                                 \n",
            " dense_97 (Dense)            (None, 1)                 6         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 42,611\n",
            "Trainable params: 42,611\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "early_stopping_monitor = EarlyStopping(\n",
        "    monitor='val_accuracy',\n",
        "    min_delta=0,\n",
        "    patience=10,\n",
        "    verbose=0,\n",
        "    mode='auto',\n",
        "    baseline=None,\n",
        "    restore_best_weights=True\n",
        ")"
      ],
      "metadata": {
        "id": "yUg8rjwPXazt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train, y_train, batch_size = 32, epochs = epochs, validation_data=(X_test, y_test), callbacks=[early_stopping_monitor])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "suGjVNwz4rMu",
        "outputId": "93a64958-b5c2-4b04-b684-ad1295661965"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "94/94 [==============================] - 5s 40ms/step - loss: 0.6558 - accuracy: 0.6093 - val_loss: 0.7564 - val_accuracy: 0.5500\n",
            "Epoch 2/50\n",
            "94/94 [==============================] - 3s 37ms/step - loss: 0.6110 - accuracy: 0.6677 - val_loss: 0.7306 - val_accuracy: 0.5722\n",
            "Epoch 3/50\n",
            "94/94 [==============================] - 3s 37ms/step - loss: 0.5341 - accuracy: 0.7423 - val_loss: 0.8612 - val_accuracy: 0.6222\n",
            "Epoch 4/50\n",
            "94/94 [==============================] - 3s 37ms/step - loss: 0.4325 - accuracy: 0.8117 - val_loss: 1.0396 - val_accuracy: 0.6056\n",
            "Epoch 5/50\n",
            "94/94 [==============================] - 4s 42ms/step - loss: 0.3199 - accuracy: 0.8720 - val_loss: 1.3255 - val_accuracy: 0.5889\n",
            "Epoch 6/50\n",
            "94/94 [==============================] - 4s 43ms/step - loss: 0.2404 - accuracy: 0.9053 - val_loss: 1.3183 - val_accuracy: 0.5500\n",
            "Epoch 7/50\n",
            "94/94 [==============================] - 3s 37ms/step - loss: 0.1714 - accuracy: 0.9373 - val_loss: 1.4621 - val_accuracy: 0.5778\n",
            "Epoch 8/50\n",
            "94/94 [==============================] - 3s 37ms/step - loss: 0.1258 - accuracy: 0.9547 - val_loss: 2.0374 - val_accuracy: 0.5722\n",
            "Epoch 9/50\n",
            "94/94 [==============================] - 3s 37ms/step - loss: 0.0674 - accuracy: 0.9800 - val_loss: 2.0836 - val_accuracy: 0.5722\n",
            "Epoch 10/50\n",
            "94/94 [==============================] - 3s 36ms/step - loss: 0.0511 - accuracy: 0.9873 - val_loss: 2.1081 - val_accuracy: 0.5556\n",
            "Epoch 11/50\n",
            "94/94 [==============================] - 3s 37ms/step - loss: 0.0274 - accuracy: 0.9930 - val_loss: 2.4272 - val_accuracy: 0.5667\n",
            "Epoch 12/50\n",
            "94/94 [==============================] - 3s 37ms/step - loss: 0.0346 - accuracy: 0.9877 - val_loss: 2.3829 - val_accuracy: 0.5833\n",
            "Epoch 13/50\n",
            "94/94 [==============================] - 3s 37ms/step - loss: 0.0422 - accuracy: 0.9847 - val_loss: 2.3009 - val_accuracy: 0.5667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model on the test data using `evaluate`\n",
        "#print(\"Evaluate on test data\")\n",
        "#results = model.evaluate(X_test, y_test, batch_size=32)\n",
        "#print(\"test loss, test acc:\", results)\n",
        "\n",
        "# Generate predictions (probabilities -- the output of the last layer)\n",
        "# on new data using `predict`\n",
        "print(\"Generate predictions\")\n",
        "y_proba = model.predict(X_test)\n",
        "prediction_nn = np.argmax(y_proba, axis=-1)\n",
        "print(\"predictions shape:\", prediction_nn.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WfFXOkCU8ckM",
        "outputId": "5897a971-b3b2-4154-90f7-ae891a61b84e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Generate predictions\n",
            "predictions shape: (180,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction_nn = (model.predict(X_test) > 0.5).astype(\"int32\")"
      ],
      "metadata": {
        "id": "w6t93v45yghH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, prediction_nn))\n",
        "\n",
        "cm = confusion_matrix(y_test, prediction_nn)\n",
        "\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
        "\n",
        "disp.plot(cmap=plt.cm.Blues)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 723
        },
        "id": "fPcEN77b8cmz",
        "outputId": "24ab5b10-a290-4480-fe73-3918516d84e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.66      0.54      0.60        92\n",
            "           1       0.60      0.70      0.65        88\n",
            "\n",
            "    accuracy                           0.62       180\n",
            "   macro avg       0.63      0.62      0.62       180\n",
            "weighted avg       0.63      0.62      0.62       180\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# list all data in history\n",
        "print(history.history.keys())\n",
        "# summarize history for accuracy\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "# summarize history for loss\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "id": "GN9_fWHYPesN",
        "outputId": "8949c468-31f1-4ca6-fcd8-b5afe6e82a17"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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bXLd10NUa3Gt5aq6E3W/AgptHLgs8nFisCb/1STvucOr1NsD3dERF+zXAKzWS8hfh8Fb4xPchb4q976jDPfitrsv9wlOOvz8UAb69YeDrporgXsvT1hWAgQWfD+51UmIsRdPbDR8+Dacss+MOmYX2/ihI02iAV2o4fX2w+qeQfxKcdgNkF9v7jx5y7pxHPoLqbfZyf7CQ9OA9pvA1hyjA9/XZ2TMl58HYILfYjLUc/O7X7dWQ+/8ro8DeRsFUSUdn0YjIfqAF6AV6jDFBbcCtVNiV/c3mVq9/GBKT7L+MfGdTNFuftGWB5376xMfScoMvVdDm2YM/ENxruR14Fxr3wwU/CP61Yi0Hv3WFDeru7QgzXQE+CqZKhmOa5IXGmMhfqyjlr75eWPMzWyLAM9jmFDs3yNrXZ2uon3wpZOaf+HioevAZBXZTjVANtG5+HFJz7MYewYqlHHxbg505tOirkOiq1d8f4CMf9nQevFJD+egZqNsJN/zp+Jks2cXO9eArP7AfHnOv9f54Wp4dwOvugOS0wM7R3mCDUFpuaAJ8R7OtvbLgJkjJCP71Ymlf1u1/tXX653sMhmdETw/e6Ry8AVaJyEYRWe7tABFZLiKlIlJaWxv5H4hSAPT2wFs/s/VUZg3qleYUO5eD37HSpmdOucz74+7VrMFsadfWaNNMeVNCE+C3/dUunFp4S/CvBa5t+zJjI8BvWQHj5sCE+QP3pWTY9kdBDt7pAL/UGHM6cDnwTRE5f/ABxpiHjDGLjDGLCgsLHW6OUj768Elo2AsX/uD43jvYed5t9bYXHUrGQNlKmHHh0CV20/LsbTBpmrZ6SB8TugC/+TEb5IpPD/613GKhHk3dLrs2Yv5NJ9bcycyP/x68MabKdVsDPAcsdvJ8SoXM2l9B8UKYefmJj+VMsLehzsMf3gLNB2H2NUMfE4p6NO0Ndjpf3mQbhLrbA3+tmh1QtREW3hpYYbGhxMKmH1tX2Dr93mruZBZGRQ7esQAvIpkiku3+GrgU2ObU+ZQKmaaDUL/LLjLyFrRyHJoqWbbS1pqZdeXQx7gDfKDlCoxxDbLmQ95Ue18wc+HLX7S3p30m8NfwJiUrugdZ+/pg61Mw4yLIHn/i4xkFcd+DLwLWishWYAPwkjHm7w6eT6nQ2P+uvS1Z6v3xnIn2NpQB3hibfy9ZOnyRrnR3iibAAN/ZAn09kD52YNFWMGmaPWtg/DzIGhf4a3gT7T34g+/B0UrvaxXA9uCjIAfv2CwaY8xeYP6IByoVbfavtQFw3Bzvj7t78C0hDPA1O6B+Nyz5xvDHBZuicQcd9yArBD4XvvMYVKyHs/8xsOcPJzU7tKtsQ638JUhMtatXvXHn4I0JberKT7qSVanB9r8NJeeeOLjqlpptl9OHsge/4wVATpyxM1iwAd5dpiBjLGSNtzN2Au3BH3gX+rqDKyw2lNTs4GYKOckYm5qaceHAoqzBMgvt9MkIX4VogFfKU+MBG/BKzhv+uJwQz4XfsRKmLIHsouGPS0qzRa0C7sG7SgVn5NsPsLzJgQf4Patte6acHdjzhxPNOfjqbfZnNvOKoY/pL1cQ2YFWDfBKeTowQv7dLZRz4ev32KAx3OwZN3dN+GBTNOmuPH/u5MDr0exdDVPPCXzB1XCiOQdf/hIg3mdYuUVJwTEN8Ep5cuffC2cPf1xOcegqSu5YaW99XeYfTD0azxQNBD4XvrkKasudSc+ATX30dtkdoqJN+Ysw+azhB5bdZSY0wCsVRfa/Y3vvQ+Xf3XKK4dgRu+I1WGUr7Zz7vMm+HZ8WxKYfbfV27rY7l583FY5V+z8Xfu8aezvDqQCfY287oyxN03jAVvscbiorRE25Ag3wSrn5mn8HG+BNnw2OwWiqgEObfEvPuAWVommwHxDuDTncM2maK/17nb2rIXMcjJsbWDtG4q5H0xVlaZqdL9vbkQJ8pubglYou+9fa25Hy7zAwFz7Y1aw7XrC3cz7l+3OCCfDtDXaA1S2QqZJ9fbYHP/2Cka90AhWtNeHLX7Lpu/wZwx+XnG4/pDRFo1SU2L/WBr/CWSMfm+0qVxDsTJodK20veKSA4SnYQVbPhVTutJA/c86rt9nUg1PpGfCoCR9FKZq2BjsIP1Lv3S2zQAO8UlFj/1rf8u8QmtWsLdVwcB3M8SM9AzbAtzcFtqlzW+PADBqwH1QJSf4NtO5dbW+nX+D/+X3Vn4OPoh78x3+3aTlfA3wUlCvQAK8U2N2Imn3Mv4PtBSemBhfgy18AjH/5d7ABvq87sCJh7jo0bgmJkDvJvwC/Z7VNU7hX9DohGnPw5S/ZvQCKF/p2fGah5uCVigr+5N/BzkfPmRBcgC9bafd6HTfClMzB0oMoGdzeABljjr/Pn6mS3e1w8H1n0zMQfTn4rjbY/YbtvftaeiAzX1M0SkWF/WvtJbUv+Xe3nImBB/jOFnvO2Vf7X6sk0HIFXW12N6iMQVsB+hPgD75vX8Op+e9u0ZaD37vGbmria3oGBkoGB5JKCxEN8EoZM5B/9yfYBlOuoLIUTC9MO2EPnJEFGuAHr2J1y51i5/T7soHJntW2fk3Juf6d21/Rtm1f+UuQmuv7FR7YDkNfd0Rr6miAV6pxv12u788fLwxsvh1ID61iAyAwcZH/zw10V6f+VaxeevDg24fV3tW2Zk5Kpn/n9pd7275oqEfT22Pnv59y6cDG2r6Igs23NcAr1Z9/93GA1S272C6nD6Tud8V6KJoLaTn+P7e/B+9nuYL+UsGDevC+zoU/VmtXcU6/wL/zBipaKkpWrLcfjv6kZ0ADvFJRoT//PtO/5/Xv7ORnmqavDyo/gMkB7mAZaA++zdWDH5yi8XXjj31v2VunB1jdUrOiIwdf/pKt4HnSJ/17XhSUK9AAr0a3QPPv4DEX3s/VrLXltmc6+Sz/nufm7vX73YMfIkXj61z4PW/aD5cJC/w7b6CioaKku/b79AsGZvb4yl1RMoJTJTXAq9GtcZ/dem2an+kZGNh8298efMV6extoDz4pFZLSA8/Bpw+aJpmYZK9GhgvwxtgB1ukXDNSxcVo01ISv3m5TV/6mZ8AjRaM9eKUiI9D8O0BWkd0k29+pkhUb7OX7mGn+n9MtkHIFbQ32eYledurMmzp8gK/72G5RGK70DNjVrJHuwe95094OtTXfcJJS7XtojdzerBrg1ei2f629lC44xf/nJiRC9vgAAvx6m54JZq9Od7kCf7TVn5h/d8ubMnw9Gnegc3r+u6fUrMgH+Npyu7Vh9vjAnp+RH989eBFJFJHNIvKi0+dSyi/B5N/dcor923y7tR4a9gSennELpAff3nDiDBq3vCl2yqe3DTaMgc1P2KJoY6b639ZARUMOvrbc/8F3TxEuVxCOHvwdwI4wnEcp/zTstfnzQNIzbtl+liuo3GBvAx1gdUsPYNOPwXVoPOVNAYz3uvAH3oXqj+Cs2/xuZlBSsyObgzcGanf6t7p5sAhXlHQ0wIvIJOBK4A9OnkepgOx+w94Gk1f2t1xBxXq7ErQ4yJkoAeXgG4dO0eS6ywZ7ycOvu98+b94N/p0vWCkR3rbvaJX9gAmmB58R2Xo0TvfgfwV8D+gb6gARWS4ipSJSWlsb2dKaapTZ86Yd6Bw7PfDXyCm2QaDDxwU5FRtgwny7IUQwAk7RDNeD58QA37jfzgM/40vBt9lfkd62r6bc3gbVg3elaCJUj8axAC8iVwE1xpiNwx1njHnIGLPIGLOosLDQqeYodbyeLrv/6oyLgnud/sVOPvTie7uhamPw6RkYCPC+Bo6eTvtBNLiSpFvORDsjqHnQQOuG39vB5DO/Flx7A9FfcCxCq1lrQxHgC6CvJ/BN0oPkZA/+XOAaEdkPPAlcJCKPO3g+pXxXucEGvJMuDu51/FnNeuRDW4kx2AFWsAHe9Pqeox5qkZNbYpIN8p49+M4W2PSY3U4wd2Jw7Q2Ee2GRv3n47g7406fgwPvBnb+23E5nzRziZ+YL92KnCE2VdCzAG2PuNsZMMsaUADcCbxpjbnHqfEr5ZfcbdvVmMAOsMBDgfdmbtSJEA6zgf0XJoSpJehpcNnjLCuhshrO+EVgbgxVoRcmqjba870dPB3f+YAdYYeADNUJTJXUevBqd9rwBkxYHVuzLU//erD6kaCrW29K87hWwwfC3Hk1/JcnhAvzkgQDf1wfrH7DVLiefGXg7gxFoDt69Uvjg+sDP3T+DJogBVoh4uYKwBHhjzBpjzFXhOJdSI2qtg8Nb4aQg8+9gVytmFPiWoqnYEJr0DATQgx8hRQO2B3/0kB2f2P26na+/JEK9dwg8B+8O8DVl/i8Gc2s5Yq9egu3BR7hcgfbgVfQ6uN7+oflqywp44LyRp9XtcW0aHewAq1tO8cg9+OZK+yEQsQDvY4oGY2vzrLvPXp3M+VRQzQxKINv29fXZAD+mBDBQVRrYufsHWIPswfdXlIyzHLxSQWmqgD9eAW/93Pfn7FplBzK3PTv8cXvetIEuVFURcyaOXFEy2AJjg/kb4H1K0bimSu563W7scebX/NvgItT6N972I0VTvwvaG+24gSQEnqap3Wlvg+3BJ6XYnaC0B6+Uh/d+a6eXHfnI9+fUlNnbdfcNPX3QGBvgp18QuqqIvmzdV7EBkjOg6NTQnNOdg/c1BdHWYANmUurQx7gD/Fs/g6Q0OOPLwbUxWIEMsro/SE+62P6sK9YFdu7acvszzhoX2PM9ZebHdw5eKb8cq4FNj9p52dVl9rJ7JD2dULfLBqkjH9nl9d7UlNn9R4OdHukpZ4LtIXe3D31MxQaYeEboesSB5OCHS8+Aay58gk3nnPbZ4KYHhkJCgg3y/gyyHlxv32f+SXZrwcqNdss9f7ln0ARTEM4ts1B78Er1W3efXaJ+9jehu3XkreTAlrM1vfCJ79uBxHX3ez/OXZ4glFUR3Rt/DDVVsqvNpo5ClZ4BO289Jcu/HPxw6RmwHz7ZrmmfkRxc9eTvtn0V6wYqdU4+y/7+VPtxFQiuGTQ7gs+/u2UUaA5eKcCmHDb8AeZcOzDAV7195OfVuOrZFZ9uUwvlL0HDvhOP2/MmFM4O7cKdkVazHtps002hmP/uyZ9yBcNVkvRUvMDWPi+aG1zbQsWfTT9a66B+N0xx/ZynLLG3/ubhW+tsHj/Y/LtbZkF0p2hE5A4RyRHrYRHZJCKXOt04NQp98HvoaoHzvj3wB+bOrQ+nerst4lVwsh0cTEiE9Q8ef0xXGxx4L3SzZ9yyRwjw7rzwpBDPJ0/L9X0JfNswdWg83fAYfO6J4NoVSv6UDO5fSOYK7LmTIGfSwM/fV+4ZNONCGOBb63xLNYaYrz34rxhjjgKXAmOAW4GfOdYqFd2qNsGDn4DHPu17kS1fdLXa1MrJl8H40+w86DElPvbgy+ymHYnJNic+99Ow+fHj23fwPejtDM38d08jbd1XscG2zZcetD/86cH7koMHm/f2tuNTpPiz8XbFOlelzoUD901eHHiAD1kPvtCmDyNQj8bXAO8eabgCeMwYs93jPjVadLXBqnvgDxfb3uq+t+CPV9pB0VDY9CebKz7vOwP3FZ3qe4pm3OyB75d8w14JbPYof7T7TUhMhSnnhKa9bqnZdiqct6mSxrh2cAph/t3N1x58b7ddtONLDz7a+LNt38H1NsWUnDZw35Ql9oN3uN2qBqvdac+bHYIVx+AxFz78aRpfA/xGEVmFDfCvikg2w5QAVnFo39tw/zl2+uLpX4BvlcJNT9qc58OXes93+6OnE979DUxdOpBDBRg3x66oHG6GSkezrYJYNGfgvomn20v19Q9AX6+9b8+bMPUcSMkIrq3eeJsq2dsDmx+z+e9Q59/BTuPzpQff3mhvQ30FEQ4pWfaDeiQ9nXasY/DP2f29P7149y5OoZhBAwOrWSOQh/c1wH8VuAs40xjTBiQDEZ4kq8KivQlWfgsevdp+/8UX4Opf297jyZfAF1baXuQjl/k3Z32wrU/are/O+/bx9xfNBdM3cNnsjXuAddyggcEl37AzcHa+As1VdmZEKKdHesrx2Nmpq9Xm/3+70P7sCmfBzCtCf05fUzTuMgXpQ5QKjma+5uAPb7XpN/fAqlvRqZCcCQf9mA8fiho0niJYrsDXAH82sCHdIE4AACAASURBVNMY0yQitwD3AH7uNqBiSl8vbHwUfrfYpjnOuR2+8R5MO//44yafCV/+u63M+L9X2D1O/dXbA2t/aVeWDh4Adc/mqB5moNU9COvZgweYdZXdqWjd/QObRod6gNUtp9gW6lr9X/DLufDK9+zg640r4BvvD/yRh1Jarh1jGGnwrt2HOjTRyp2DH6nuvTuAD+7BJybBpEW+L3hqa4DWmtDl3yEmUjT3A20iMh/4DrAH+JNjrVKRY4xdqv7AUnjhdsibCl97Ay79j6FTG+NmwVdX2Z3nH/u0naLoj7K/QeM+OP+7J14Wj51uV1UON5OmugxSsge2nXNLTILFy+HAWpuqyRpvUz5OyJloL8Hf+jlMPRe+sgq++irMusIOXDohLRcwI6cw3HVoYjFFk5oNfd0j1xeqWG935/K28nTKEjuO48uVQKhKFHjqLxkcvQG+xxhjgE8B/88Y8zsg27lmqYg48hE8dh08cT10t8FnH7WBe+LpIz83dxJ85VUYfyo881U45uPlqDHwzr1QMBNmXnni4wmJ9o+tetvQr1FTZgdYveVMT7/VXqJXb7O991DlVQebfyOc91345gdw4xPHjyM4xb2adaRyBb5UkoxWKT5s+mGM7cEPTs+4TV5s03yVPhQeC1WRMU9JKfb/Kopz8C0icjd2euRLIpKAzcOreNDTBc//k63EeHgLLPuZDVRzr/UvIGaMhesetLsWrbvPt+eUvwg1223ufaiebtHcoVM0xtgAPzg945Y+BhbcbL92Kv8O9krj4h9C4SnOnWMwX8sV+FJJMlr1V5QcZjpuw14bPIcayJ50JiC+DbTW7rQdgpxJfjd1WBEqV+BrgP8c0ImdD38EmAT8wrFWqfAqf8HO9li8HG7fbAcnk1ICe62Ck+0K1A/+MHLPsq8X3vwJ5J8Mp35m6OOK5tq8qLergpYjdpbIcKmXpf9iZ/6ccplv7yFWpPu46Ud7g01zOTF7yGn9NeGH6cG7A/dQPfi0XPs75MtAa225/ZAOdVotoyB6UzSuoP4EkOvaTLvDGKM5+HhRthIyx8Gyn4ZmpsV537Y9rg9+P/xx25+zM1suvHv4xTXu4F3jZT68+77hAnzuRLjmtwO9wXjhcw++MTbTM+BbTfiD6+zPomCYtMrks2yKxj1ldiih2KbPm8yCgSupMPK1VMENwAbgs8ANwHoRGabLpWJGdzvseg1mXxW68rkT5sPJl8L799kpg9709tgZJ+Pmwpzrhn89d4ldb2ka931ODZ5GM39SNLGYngHfcvAV6+32i8P1uqcssYPRwy2a62i2U3VDmX93yyyI6hTNv2LnwH/RGPMFYDHwQ+eapcJm9xu24t7sa0L7uud9x6YGNj7q/fEPn7QLmC7615Evh7MKbQ7T2x9nzQ7IKop8adtI8DXA+1poLBqN1INva7BplZEGtX1Z8FT7sb11pAdfaD9ow1yPxtcAn2CM8VyPXu/Hc1U027HSrogsWRra152yxK5Kfe+3J05x6+mCNT+3NUN8XQBUNHfoFM1o7L3DwKbUI5Ur8KVUcLTqz8EPEeDdM2MmD5F/d8ubYksPDJeHd2IGjVtGgZ3J415VHCa+Bum/i8irIvIlEfkS8BLw8nBPEJE0EdkgIltFZLuI/DjYxqoQ6+mCnX+HWVc6szXbed+2l7xbVxx//+Y/QfNBuOge32fpjJsLNeXH51D7em3ONFpK24ZbQqKtgTNiisbHSpLRaKQefMU6uzHMxDOGfx0RV+GxDUMfU1tuB6PzpgbW1uFEqFyBr4OsdwIPAfNc/x4yxnx/hKd1AhcZY+YDC4BlIjLCx6wKq31v2yJUoU7PuM24yPbS1/5qYFed7nZ4+39gytkww49pi0VzoKf9+Jo3DfvslEzPImOjzUjlCvp6ba8xVnPwyZn2dqgc/MH1MGGebzOEJi+xHYuhyjrX7rSzwEI1FuUpQuUKfE6zGGOeNcZ82/XvOR+ON8YY9/9KsuvfCOuNVVjteN4OYs0I4e5GnkRsLr5xn50xA/DBw3bno4t+6N8ce3cv3TNN48sMmng3UoDvaAZM7KZoEhLs76i3HnxvN1RtHDk94+bO0w+VpnFqBg1ErFzBsAFeRFpE5KiXfy0iMmIhcBFJFJEtQA3wmjHmhBEOEVkuIqUiUlpbG5l9C0el3h5bUuCUy4bfiDlYM6+0fzRr77V1U9bea7fLKznXv9cpnGX3C/UcaK0uA8S5P8pYMFKAj+VVrG6pWd4D/P619qrO11XD4+fZjc9LH4HmyuMf6zxme/dO5N/BDrJCdPXgjTHZxpgcL/+yjTE5I724MabXGLMAuzBqsYicsKW8MeYhY8wiY8yiwsLCwN+J8s/B9+zg2xyH0jNuCQmw9Nt2temTN9tzXnSP/6+TnA5jZxwf4GvK7ArSWFzAEyppucMvKIvlVaxu3ipKNuyDv/4D5E7xvYBcYrK9cqzYAL85HV770cDPrs7BGTQwcAUV5rnwYZkJY4xpAlYDy8JxvpjU3QE7XrCXneFQthKS0uGkTzp/rlOvtwNX+9+BUy631f0CUTTn+KJj7ho0o9lIPfj+SpIxHOAH78vaWgePX2/3ub3l2YHpor44+x/hWxvh1E/b/Qd+swDe/53dFB2cC/CJyXYRYTT14IMhIoUikuf6Oh24BBimqPco1tcHf/s6PHWLHYAMx/l2vGBrs6RkOn++xCT4xPfsdmoX/iDw1yk61fbculrtYG3D3tE7g8YtfYRNP2K5kqSbZw++qw1W3Gg3V7npycBq/+RNhusegNvetpMAXv0BvPht+/s5Zlpo2+4po2DoAV6HONmDnwCsFpEPgQ+wOfgXHTxf7Hrz3+0g5Njp8M7/+LZFXTCqSuHYEVszJlwW3gLf/djOeAjUuDmAsdMla3faecWjeYAVbO+1q2VgltJgcZGDz7Y58r5eePZrdu779X8YuvaMrybMg1ufs//Gn2YnGzi5H+2Mi+Djvzv/9+3BsQBvjPnQGLPQGDPPGHOqMebfnTpXTNv4qN3sYtFX4Kuv20VHz39z6D/YUCh73vZWwl18K9hepLtiZPW2gVSNBnh7O1S1xfYG+3+dkhW+NoWauwf/8p2w8yW4/L9h9tWhe/0ZF8Ftb8Hn/xK61/Tmgrvs/9cr3x95A5MQ0dWokbTnTXjxX2we/PJf2OX2V/y33VvS13K7/jLGrl6dcaF/uctokFdi50XXlNleUGKqveoZzUYqV+BexepUHfxwSMmyM1xKH4Zz74Czlke6RYHJGAsX/qsdiyp7Piyn1AAfKTU74Okv2kGdz/zvwKXh3E/bqYWrfwL1e0J/3sNb7dZyTi1uclJCgh1Urd5ug3zhTGcvqWNBf4AfYiZNLK9idXOvZj31M3Dxv0W0KUE748t2LGnVPXY8wWEa4COhpRqeuMFO/bv5KUjzmHEqAlf+X9s7XXl76IsT7Vhpl3bP8rJ7UiwommsDfHWZpmfAhx58Q2xPkQQ7lffsf4Jr73Nu+8NwSUyCy38OzRXw3m8cP12M/7RikHsWQFudDe55k088JmeC3QP1wFrY9MfQndsYOz2yZGnszqoommvzyseODL2L02iSNsKmH7FcSdKteCFc9hNnF+SFU8lSmHudHXtrOujoqTTAh9v6B+DQJjsLoHjh0Med/gWYdj6s+hE0Vx3/WOMBW2v90atttUZf1e6E+l3OL25ykmevfdwonyIJvvXgYz3Ax6NL/gMQWOVs1XUN8OF28H2bdx8pRSICV/8GTK8diK3ebkvsPrAUfj0PXr3bbpL9+r8NvV/pYO/9BhKSYNZVQb+NiPGc9z7aFznB8AHeGNuDj/UUTTzKm2y3kiz7my365xAN8OFkjJ3DO9HHlZxjp9ml1btehfvPgTU/tbU0LvkP+NYm+KeNdgDqpe+MPO1qz5uw5Qk7CyF7fPDvJVIyxtq63mm5kFMc6dZEXkqWrdHjrVxB51G72jPWB1nj1bm321ILr9zl2LToUT4FIcwa99ke1aQRald7Ous2+4eaVWQ3x8guOv7xT/4YXrjd1lxfcLP31+hqhRfugPyT4PzvBd7+aFFyHnS3xfbUv1BJSLAbf3jrwcfDKtZ4lpwOl/0nPP0F2Pi/sPgfQn4KDfDhVLnR3k460/fnJCTaBRJDWXgrbH7cTrs6ZZn3P+bV/2UHc770MiSn+dfmaHTdA5FuQXTxVq7AGHjnXvv1cJtRq8iafY3tsKz+L1jw+ZAXztMUTThVfmBTLIUhzB0nJMBVv7SX6G942TSraqNdNLXoK/6X6I1WCYnObMoQq7wVHFv/AGx+DM6/078rRhVe7mnRn/+LI1VRNcCHU1WpnTkT6sU540+FJd+AjX+Eig8G7u/pgue/BVnjbSpHxafBAX73G7aA1qyr4IIgirup8CicGXiF1RFogA+Xnk4762WkvSMDdcFdkF0ML/3LwIDNu7+2ux5dde/xi6lUfPEM8HW74Zkv26vE6x6M/YVBKij6vx8uRz6C3i7/8u/+SM2GZT+15/ng93bO+9v/7Sp9cLkz51TRIS3Xlipob7KL6BKS4KYVdickNarpIGu4VLpSJw5digG2/O9Jn4Q3fwIfPmXz/Zf/3LnzqeiQlmeD+7NftTO1vrASxkyNdKtUFNAefLhUltoUipNzt0VsKdXeLluRctlPIWucc+dT0SEtz+5Nuvt1O2AXL4PpKmjagw+XqtLwzGbIn2Fn1Rz5CObf5Pz5VOSlu+rRLL4NzvhSRJuioosG+HBorYPG/XaqYjgs/Hx4zqOiw5xP2d2OzvxapFuioowG+HCoLLW3vpYoUMofWeNgydcj3QoVhTQHHw5VpbYGe/GCSLdEKTWKaIAPh8pSW+Y2JTPSLVFKjSKOBXgRmSwiq0WkTES2i8gdTp0rqvX1QdUmZ6dHKqWUF07m4HuA7xhjNolINrBRRF4zxvhYvDxO1O+CzmYN8EqpsHOsB2+MOWyM2eT6ugXYAUx06nxRSwdYlVIREpYcvIiUAAuB9eE4X1SpKrX1ugtOiXRLlFKjjOMBXkSygGeBfzbGHPXy+HIRKRWR0traWqebE36VrgqSWvRJKRVmjkYdEUnGBvcnjDF/9XaMMeYhY8wiY8yiwsJCJ5sTfl1tdi9VpwqMKaXUMJycRSPAw8AOY8y9Tp0nqh3eYjfN1gFWpVQEONmDPxe4FbhIRLa4/l3h4Pmijw6wKqUiyLFpksaYtcDo3hW5qhTypkBWnKWelFIxQUf+nFRZqvl3pVTEaIB3ytHDcLRK0zNKqYjRAO+UKlf+XQdYlVIRogHeKZUfQEIyjJ8X6ZYopUYpDfBO+OgZWHc/TD0HktMi3Rql1CilAT6UjIG3fmE3P550Jnz2j5FukVJqFNMdnUKlpwteuAO2/hnmfQ6u+S0kpUa6VUqpUUwDfCi0NcBTt8KBtXDBD+AT3wMZ3UsAlFKRpwE+WPV74M83QNNB+PQfYN5nI90ipZQCNMAHp3E/PLIM+nrgCyth6tmRbpFSSvXTAB+otgZ4/DPQ2wVfXQWFMyPdIqWUOo4G+EB0d8CKm2xa5gvPa3BXSkUlDfD+6uuD55ZDxXr47P9qWkYpFbV0Hryn7g7Y+qStIzOUVfdA2fNw6X/C3OvC1zallPKT9uA9rfsdvPHvIAlw0iWw8BY4ZRkkpdjH37/PHnPWN+Dsb0a2rUopNQIN8G693fDBwzD5LJh6LmxdAU/fChn5MO9GGDMVXv0BzL4aLvuJznNXSkU9DfBu5S/a8r5X/l+YeTlc+K+w503Y/BhseAj6um3w//TvISEx0q1VSqkRaYB3W/8gjCmBky+13ycmwSmX2n+t9bD7NTjlMkhOj2gzlVLKVzrICnBoCxx8HxYv9947z8yH+TdC+pjwt00ppQKkAR5sCiY5ExZ8PtItUUqpkNEAf6wWPvoLLLgJ0vMi3RqllAoZxwK8iDwiIjUiss2pc4TEpj/acgOLl0e6JUopFVJO9uD/CCxz8PWD554aOeMiLTeglIo7jgV4Y8zbQINTrx8SO1ZCy2E46+uRbolSSoVcxHPwIrJcREpFpLS2tja8J1//IIyZZletKqVUnIl4gDfGPGSMWWSMWVRYWBi+E1dtsgXDzroNEiL+Y1BKqZAbvZFt/YOQkgULbo50S5RSyhGjM8C3VMO2Z21wT8uNdGuUUsoRTk6TXAG8D8wUkUoR+apT5/KZMbDrdXjqFltbRqdGKqXimGO1aIwxNzn12n7r7YZtf4V3fw012yF7Alz9ayg4OdItU0opx8R3sbG2Blv29/374GglFM6Ga++HUz8zUONdKaXiVOwHeGPsXPbanVD38fG3rTX2mKlL4ap77XRInTGjlBolYj/A9/XS+8t5JJpu+31qLhSeYsv+Fp4CJUth4hmRbaNSSkVAzAf49l7hh71fp6onh4b0EhaePJNL5o7n3JMKSEvWjTmUUqOXGGMi3YZ+ixYtMqWlpX4/72hHN2t21vJaWTWry2s41tlDRkoi559cyDULirl0ThFJiZqaUUrFHxHZaIxZ5O2xmO/BA+SkJXPN/GKumV9MZ08v6/Y28FrZEV4rq+bv248waUw6Xz53Gp87czJZqXHxlpVSakRx0YMfSm+f4bWyah5eu5cP9jeSnZbEzYun8KVzS5iQq1vvKaVi33A9+LgO8J62VDTx+3f28spHh0kQ4er5xdx+8clMK8h05HxKKRUOGuA9VDS08b/v7mfFhoN09fbxmdMncfsnT2ZinvbolVKxRwO8FzUtHdy3eg9/Xn8QgJvPmsI/XjiDcdlpYTm/UkqFggb4YVQ1tfPbN3bxl42VpCQm8MVzSvj6J6aTl6ErXZVS0U8DvA/21bXyq9c/ZuXWQ2SlJnHb+dP58rnTyNRZN0qpKKYB3g/lR47yP69+zOs7qinISuGbF57EzWdNITVJF00ppaKPBvgAbDzQyC9eLWfd3gYm5qVzxydP5toFE0lJ0gVTSqnooQE+QMYY3t1dzy9eLWdrZTNjMpK5al4x1y6cyOlT8hCRSDdRKTXKaYAPkjGGtz6u5dlNVazafoTOnj6m5mdw7YKJXLtwos6lV0pFjAb4EGrp6ObV7dU8t7mS9/bUYwzMLc7h0jnjuWROEbMnZGvPXikVNhrgHXKkuYOVW6t4dXs1mw42YgxMzEvnkjlFXDqniEUlYzVnr5RylAb4MKht6eSNHdW8VlbNO7vr6OrpQwQKslKZkJtGUU4a43PSGJ+bRnFeGiX5mUwryNT59kqpoGiAD7O2rh7e/riOssNHqW7u4MjRDo64bpvbu487dkxGMiUFNthPHZtJbnoSmalJZKW6btPs1zlpyeSmJ5OWnDBiCsj9f6qpIqXiX8QCvIgsA34NJAJ/MMb8bLjj4yXAD6e9q5eqpnb217Wyr66VffWt7KttZX99K4ebO0Z8fnKikJueTE56MjlpyYjY12zr/9dDe3cviSLkZ6VQmJ1KQVYqhVmpFGTb26KcNMblpFKUbW+9bYzS2dNLa2cvrZ09pCQlkJmaREZyIgkJJ35otHb2UNvSSd2xTmpbOmnv7iUjJYnM1EQyU5PITEkiIyWR9JSB87h/7QwGQchOSxpxg5ae3j4aWruoaelEBCblZZCTnhTxD7Lu3j62VTVzoL6N4rx0pozNYFx2qtefVah19fSxtbKJ3TXHjrtazM9MCcv5nXa0o5v1exs4UN9KYXYqE3LTmZBrf28DWZsSj52fiNSDF5FE4HfAJUAl8IGIrDTGlDl1zliQnpLISeOyOGlc1gmPdfX0cayzh9bOnv7bFtft0fYemtu7aW7v5miH69Z1NVCYleoKoDaQZqQk0tNnqHMF3bpjXZQfbqHuWCc9fSd+oOekJVGQlUpnTx+tXfZ83b0nHicCmSnuq4tEunr7qGvpor27NyQ/m7TkBMZkpJCbnsyYjBTyMpJp6+qltqWTmpZOGlo7Gdz8zJREJo5JpzjP/stNT6aprZumti4aWrtoauumoa2Ljq5eSgoymTk+m1njs5k1PodZE7IpyEr1u53ugL5ubwPr9tZTur+B1q7jfwYpSQlMGmOD/aQx6RRkpTI2M4UxGSnH3WamJpKenOjzhjQ9vX1sO3SU9/bU8f6eekr3N3r9+ScnCuOy05iQm8aU/AxK8jOZmp/B1PxMSvIzyMtIoaO7lz21x/i4uoWPq4/x8ZEWPq5poaunz3YCslMZ57otykmjMCuVMZkpjMlIZmxmCjlpySH/EOns6WXzwSbe3V3H2t11fFjZTK+X31mw6c/ivDSm5mcyvSCT6YWZzCjMYlpBJpmpSbR39bKzuoWyQ0cpO9xM2aGjlB9pobfPUJidat+fq5MzLjuVwuxU8jNtR6ggK4WCLO+dH1909/axt7aVHYePsuPwUcoO23P39Rkm5KVRnGt/XyfkpvX/7p4xdUwwPzqvHOvBi8jZwL8ZYy5zfX83gDHmp0M9ZzT04COpr8/Q1N5NTUsHNUc7qT7aQU2Lva0/1kWqq6duU0SJZKUmkZGaRHdvn/3Q6ejhWGcvxzq7ae3sJSlR7NWB+yrB9YeRkZJEW1cPbV32CqC1s5fWrh46XIGoPyS4e1HGcLSjh6Y2G5Ab27ppbu+isa2b9OREV6CxVx+FrkBjjKGqqd3+a2znULO9PdrRw5iMZPIyUhjr+pAYm5lCSlICe2tbKT9ylLpjXf0/k/zMFDJSExEEEdu2BNcXQ4WuI80d/QH9lKIslkzPZ8n0fE4el8Xh5g4ONrRR0dBGRWMbBxvaqGxsp6mte4hXs5IThbRkG+zTUxJJGiJw1hztpKWzp//cZ0/P5+wZ+cyZkEtjWxeHmzuoPtrB4eYOjjS3c6i5g4qGthOuDrPTkmjt7On/wExKEGYUZnFyURZpyYnUtHRSc7SD2pZO6lu7BjcDgASBPNcHsmeH2LPl/vaUqxrb7RVogjBvUi5LTyrg3JMKmFmUTd2xTtf7sunOw80dVDW1s6/uGJWN7XiGsoKsFBpau/rfX3ZqErOLc5gzIYeUpARqXL/77vd5tKPHa3uyUpPITU8mMcH774e399fXZ6hsbKertw+AlMQETi7KYtZ4e+5DTe0cbm7ncFNH//9lQVYKpfdc4tfPyi0iKRoR+QywzBjzNdf3twJnGWP+adBxy4HlAFOmTDnjwIEDjrRHjQ7GmBGDSt2xTnYeaaH8SAu7a1ro6O7DGIPBpo4M0DfM30V+ZgpLpuezeNpYn68Aenr7aGrvprHVXlk0tnXR0NptU2pdvbR3238d3TbV5u1KCyAvPbn/A6Uw2/erj47uXg42tHGgvo0D9a0cbGgjLz2ZU8ZnM7Mom5KCTJKHuIro6unrT781uj6E3e+hsa2L5vae/tTHca0OILQUZqdy7kkFnDV9LDlpyX69vwP1beytPcbeulYO1LcyPieNOcW5zC3OYdKY9GF/Lzq6e/s/zOpaOqlvtVe+dcc6aW7r7v+dOO73Y6j3JzBpTDqzx+cwe0IO0wuH/tm2dHRzuLmDo+3dLCoZ6/P7Pe500RzgPWkPXiml/DNcgHdyknYVMNnj+0mu+5RSSoWBkwH+A+BkEZkmIinAjcBKB8+nlFLKg2OzaIwxPSLyT8Cr2GmSjxhjtjt1PqWUUsdzdDcLY8zLwMtOnkMppZR3WihFKaXilAZ4pZSKUxrglVIqTmmAV0qpOBVV1SRFpBYIdClrAVAXwubECn3fo4u+79HFl/c91RhT6O2BqArwwRCR0qFWc8Uzfd+ji77v0SXY960pGqWUilMa4JVSKk7FU4B/KNINiBB936OLvu/RJaj3HTc5eKWUUseLpx68UkopDxrglVIqTsV8gBeRZSKyU0R2i8hdkW6Pk0TkERGpEZFtHveNFZHXRGSX6zb0GztGkIhMFpHVIlImIttF5A7X/XH9vgFEJE1ENojIVtd7/7Hr/mkist71O/+Uqxx3XBGRRBHZLCIvur6P+/cMICL7ReQjEdkiIqWu+wL+XY/pAO+xsfflwBzgJhGZE9lWOeqPwLJB990FvGGMORl4w/V9POkBvmOMmQMsAb7p+j+O9/cN0AlcZIyZDywAlonIEuDnwC+NMScBjcBXI9hGp9wB7PD4fjS8Z7cLjTELPOa/B/y7HtMBHlgM7DbG7DXGdAFPAp+KcJscY4x5G2gYdPengEddXz8KXBvWRjnMGHPYGLPJ9XUL9o9+InH+vgGMdcz1bbLrnwEuAp5x3R93711EJgFXAn9wfS/E+XseQcC/67Ee4CcCFR7fV7ruG02KjDGHXV8fAYoi2RgniUgJsBBYzyh5365UxRagBngN2AM0GWN6XIfE4+/8r4DvAX2u7/OJ//fsZoBVIrJRRJa77gv4d93RDT9UeBljjIjE5bxXEckCngX+2Rhz1HbqrHh+38aYXmCBiOQBzwGzItwkR4nIVUCNMWajiFwQ6fZEwFJjTJWIjANeE5Fyzwf9/V2P9R68buwN1SIyAcB1WxPh9oSciCRjg/sTxpi/uu6O+/ftyRjTBKwGzgbyRMTdOYu33/lzgWtEZD825XoR8Gvi+z33M8ZUuW5rsB/oiwnidz3WA7xu7G3f7xddX38ReD6CbQk5V/71YWCHMeZej4fi+n0DiEihq+eOiKQDl2DHIFYDn3EdFlfv3RhztzFmkjGmBPv3/KYx5vPE8Xt2E5FMEcl2fw1cCmwjiN/1mF/JKiJXYHN27o29fxLhJjlGRFYAF2BLiFYD/wf4G/A0MAVbavkGY8zggdiYJSJLgXeAjxjIyf4Am4eP2/cNICLzsINqidjO2NPGmH8XkenY3u1YYDNwizGmM3ItdYYrRfNdY8xVo+E9u97jc65vk4A/G2N+IiL5BPi7HvMBXimllHexnqJRSik1BA3wSikVpzTAK6VUnNIAr5RScUoDvFJKxSkN8EqFgIhc4K58qFS00ACvlFJxSgO8GlVE5BZXjfUtIvKgq5jXMRH5pavm+hsiUug6doGIrBORD0XkyPkgAwAAAXpJREFUOXcdbhE5SURed9Vp3yQiM1wvnyUiz4hIuYg8IZ4Fc5SKAA3watQQkdnA54BzjTELgF7g80AmUGqMmQu8hV0hDPAn4PvGmHnYlbTu+58Afueq034O4K70txD4Z+zeBNOxdVWUihitJqlGk4uBM4APXJ3rdGzhpj7gKdcxjwN/FZFcIM8Y85br/keBv7hqhUw0xjwHYIzpAHC93gZjTKXr+y1ACbDW+bellHca4NVoIsCjxpi7j7tT5IeDjgu0fodnbZRe9O9LRZimaNRo8gbwGVetbfdel1OxfwfuSoU3A2uNMc1Ao4ic57r/VuAt165SlSJyres1UkUkI6zvQikfaQ9DjRrGmDIRuQe7Y04C0A18E2gFFrseq8Hm6cGWZn3AFcD3Al923X8r8KCI/LvrNT4bxrehlM+0mqQa9UTkmDEmK9LtUCrUNEWjlFJxSnvwSikVp7QHr5RScUoDvFJKxSkN8EopFac0wCulVJzSAK+UUnHq/wPd8p4sXxWwyQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qZmmVwBqUJo4"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}