benchmark_lightgbm_zenodo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b09e92b7",
   "metadata": {},
   "source": [
    "## imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d540c53d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-05T15:29:40.004372Z",
     "start_time": "2022-07-05T15:29:36.659994Z"
    }
   },
   "outputs": [],
   "source": [
    "import math\n",
    "import os\n",
    "import sys\n",
    "\n",
    "import collections\n",
    "from copy import copy, deepcopy\n",
    "import json\n",
    "from collections import Counter\n",
    "from io import BytesIO\n",
    "import zipfile\n",
    "from glob import glob\n",
    "import shelve\n",
    "\n",
    "from tqdm.notebook import tqdm\n",
    "from IPython.display import display\n",
    "import lightgbm as ltb\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "pd.set_option(\"display.precision\", 4)\n",
    "import rasterio\n",
    "from rasterio.io import MemoryFile\n",
    "import torch\n",
    "from torch.utils.data import Dataset\n",
    "import torchvision.transforms as transforms\n",
    "try:\n",
    "    import torchvision.transforms.F as F\n",
    "except:\n",
    "    import torchvision.transforms.functional as F\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import joblib\n",
    "from sklearn import datasets\n",
    "from sklearn.metrics import f1_score, precision_score, recall_score, accuracy_score, average_precision_score, classification_report, confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from datadings.reader import ZipFileReader, MsgpackReader\n",
    "from datadings.writer import FileWriter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cf09a056",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:31:00.419745Z",
     "start_time": "2022-07-04T19:30:46.905358Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/bin/bash: warning: setlocale: LC_ALL: cannot change locale (en_US.UTF-8)\n",
      "Collecting scikit-multilearn\n",
      "  Downloading scikit_multilearn-0.2.0-py3-none-any.whl (89 kB)\n",
      "\u001b[K     |████████████████████████████████| 89 kB 12.1 MB/s eta 0:00:01\n",
      "\u001b[?25hInstalling collected packages: scikit-multilearn\n",
      "Successfully installed scikit-multilearn-0.2.0\n",
      "/bin/bash: warning: setlocale: LC_ALL: cannot change locale (en_US.UTF-8)\n",
      "Collecting pickle-mixin\n",
      "  Downloading pickle-mixin-1.0.2.tar.gz (5.1 kB)\n",
      "Building wheels for collected packages: pickle-mixin\n",
      "  Building wheel for pickle-mixin (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for pickle-mixin: filename=pickle_mixin-1.0.2-py3-none-any.whl size=5998 sha256=81cf0b41da700922e84b0791b811f3ff07923989a2f96a4137c59f49fe71f04a\n",
      "  Stored in directory: /root/.cache/pip/wheels/58/c6/8b/061bd4edc8cea2b2235758c3e50473d08499236fbfdd21e6b3\n",
      "Successfully built pickle-mixin\n",
      "Installing collected packages: pickle-mixin\n",
      "Successfully installed pickle-mixin-1.0.2\n",
      "/bin/bash: warning: setlocale: LC_ALL: cannot change locale (en_US.UTF-8)\n",
      "Collecting flaml\n",
      "  Downloading FLAML-1.0.7-py3-none-any.whl (196 kB)\n",
      "\u001b[K     |████████████████████████████████| 196 kB 12.0 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: pandas>=1.1.4 in /usr/local/lib/python3.9/dist-packages (from flaml) (1.4.2)\n",
      "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.9/dist-packages (from flaml) (1.8.0)\n",
      "Requirement already satisfied: lightgbm>=2.3.1 in /usr/local/lib/python3.9/dist-packages (from flaml) (3.3.2)\n",
      "Collecting xgboost<=1.3.3,>=0.90\n",
      "  Downloading xgboost-1.3.3-py3-none-manylinux2010_x86_64.whl (157.5 MB)\n",
      "\u001b[K     |████████████████████████████████| 157.5 MB 335 kB/s  eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: NumPy>=1.17.0rc1 in /usr/lib/python3/dist-packages (from flaml) (1.19.5)\n",
      "Requirement already satisfied: scikit-learn>=0.24 in /usr/local/lib/python3.9/dist-packages (from flaml) (1.0.2)\n",
      "Requirement already satisfied: wheel in /usr/lib/python3/dist-packages (from lightgbm>=2.3.1->flaml) (0.34.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.9/dist-packages (from pandas>=1.1.4->flaml) (2022.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.9/dist-packages (from pandas>=1.1.4->flaml) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.1->pandas>=1.1.4->flaml) (1.16.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.9/dist-packages (from scikit-learn>=0.24->flaml) (3.1.0)\n",
      "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.9/dist-packages (from scikit-learn>=0.24->flaml) (1.1.0)\n",
      "Installing collected packages: xgboost, flaml\n",
      "Successfully installed flaml-1.0.7 xgboost-1.3.3\n"
     ]
    }
   ],
   "source": [
    "!pip install scikit-multilearn\n",
    "from skmultilearn.model_selection import IterativeStratification\n",
    "\n",
    "!pip install pickle-mixin\n",
    "import pickle\n",
    "\n",
    "#!pip install flaml[notebook]\n",
    "!pip install flaml\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore', message='pandas.Int64Index', category=FutureWarning)\n",
    "os.environ[\"PYTHONWARNINGS\"] = \"ignore:pandas.Int64:FutureWarning\"\n",
    "\n",
    "from flaml import AutoML"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36cf2204",
   "metadata": {},
   "source": [
    "## global variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7ab05df8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:31:00.743803Z",
     "start_time": "2022-07-04T19:31:00.731670Z"
    }
   },
   "outputs": [],
   "source": [
    "# BASE_PATH must be the directory with the files downloaded from Zenodo.\n",
    "# For instance, '/home/<username>/treesatai_benchmark_archive/zenodo/'.\n",
    "#\n",
    "# RESNET_WEIGHTS_FILE is the file containing the pre-trained ResNet18 weights used to extract features\n",
    "# that are fed to the LightGBM classifier. See section 3 of the paper for details. By default, it is\n",
    "# located in the same directory as this notebook file, but feel free to store it somewhere else and\n",
    "# update the path accordingly.\n",
    "\n",
    "BASE_PATH = '/path/to/treesatai_benchmark_archive/zenodo/' # this is an example; must be adapted\n",
    "LABELS_FILE = os.path.join(BASE_PATH, 'labels.zip/labels/TreeSatBA_v9_60m_multi_labels.json')\n",
    "RES_SENTINEL = '60m'                                       # resolution of the Sentinel (1 and 2) sensor\n",
    "\n",
    "TRAIN_FILENAMES = open(os.path.join(BASE_PATH, 'train_filenames.lst')).read().split()\n",
    "TEST_FILENAMES = open(os.path.join(BASE_PATH, 'test_filenames.lst')).read().split()\n",
    "RESNET_WEIGHTS_FILE = 'ResNet_PT_FT_Aerial3Bands_Norm_Imgnet_v9_100.pt'\n",
    "\n",
    "TIME_BUDGET = 30*60                                        # time for param tuning in seconds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0ac42d0",
   "metadata": {},
   "source": [
    "## loading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e880b360",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:31:00.729556Z",
     "start_time": "2022-07-04T19:31:00.437258Z"
    }
   },
   "outputs": [],
   "source": [
    "def read_multilabels(fn):\n",
    "    zfn,inner = fn.split('.zip/')\n",
    "    zfn = zfn + '.zip'\n",
    "    with zipfile.ZipFile(zfn) as z:\n",
    "        with z.open(inner) as f:\n",
    "            return json.load(f)\n",
    "multilabels_dict = read_multilabels(LABELS_FILE)\n",
    "\n",
    "# hack for missing file?!?\n",
    "#multilabels_dict['Betula_spec._4_324455_BI_NLF.tif'] = [['Betula', 1.0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b7200c24",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:31:04.202942Z",
     "start_time": "2022-07-04T19:31:04.167150Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Quercus', 15524),\n",
       " ('Fagus', 14382),\n",
       " ('Picea', 14169),\n",
       " ('Pinus', 13748),\n",
       " ('Cleared', 7080),\n",
       " ('Larix', 6388),\n",
       " ('Pseudotsuga', 5754),\n",
       " ('Betula', 4346),\n",
       " ('Alnus', 4207),\n",
       " ('Acer', 3819),\n",
       " ('Fraxinus', 3378),\n",
       " ('Abies', 1616),\n",
       " ('Populus', 684),\n",
       " ('Prunus', 482),\n",
       " ('Tilia', 286)]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_counts = Counter()\n",
    "for mls in multilabels_dict.values():\n",
    "    for ml, p in mls:\n",
    "        class_counts[ml] += 1\n",
    "class_counts.most_common()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c7c6721a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:31:04.747089Z",
     "start_time": "2022-07-04T19:31:04.742620Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Abies',\n",
       " 'Acer',\n",
       " 'Alnus',\n",
       " 'Betula',\n",
       " 'Cleared',\n",
       " 'Fagus',\n",
       " 'Fraxinus',\n",
       " 'Larix',\n",
       " 'Picea',\n",
       " 'Pinus',\n",
       " 'Populus',\n",
       " 'Prunus',\n",
       " 'Pseudotsuga',\n",
       " 'Quercus',\n",
       " 'Tilia']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes = sorted(class_counts)\n",
    "classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7896caa5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:31:04.923868Z",
     "start_time": "2022-07-04T19:31:04.920174Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ece56d37",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:31:05.111453Z",
     "start_time": "2022-07-04T19:31:05.109092Z"
    }
   },
   "outputs": [],
   "source": [
    "def convert_binary(labels):\n",
    "    vec = np.zeros(len(classes), dtype = np.uint8)\n",
    "    for lab, _ in labels:\n",
    "        # get index from original class list\n",
    "        idx = classes.index(lab)\n",
    "        vec[idx] = 1\n",
    "    return vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c0a92e5f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:31:05.285610Z",
     "start_time": "2022-07-04T19:31:05.283679Z"
    }
   },
   "outputs": [],
   "source": [
    "def decode_images(sample):\n",
    "    sample['data'] = MemoryFile(sample['data']).open().read()\n",
    "    return sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c0b67ce2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:31:05.441268Z",
     "start_time": "2022-07-04T19:31:05.436935Z"
    }
   },
   "outputs": [],
   "source": [
    "def initfun(sample):\n",
    "    key = sample['path'].rsplit('/', 1)[-1]\n",
    "    sample['key'] = key\n",
    "    sample['label'] = key.split('_', 1)[0]\n",
    "\n",
    "def _gen_from_zip_file(filename, pattern):\n",
    "    for sample in ZipFileReader(filename, pattern, initfun=initfun, numeric_labels=False):\n",
    "        \n",
    "        #sample['data'] = MemoryFile(sample['data']).open().read()\n",
    "        \n",
    "        key = sample['key']\n",
    "        try:\n",
    "            labels = multilabels_dict[key]\n",
    "            sample['multilabels'] = labels\n",
    "            sample['labelvec'] = convert_binary(labels)\n",
    "\n",
    "            yield sample\n",
    "        except KeyError:\n",
    "            print('WARNING: no entry for', key, 'in multilabels file')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "02f9ed98",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:31:05.612799Z",
     "start_time": "2022-07-04T19:31:05.609427Z"
    }
   },
   "outputs": [],
   "source": [
    "import multiprocessing as mp\n",
    "def gen_from_zip_files(filepat, inner_pattern):\n",
    "    with mp.Pool(16) as pool:\n",
    "        fns = sorted(glob(filepat))\n",
    "        for fn in fns:\n",
    "            for sample in pool.imap(decode_images, _gen_from_zip_file(fn, inner_pattern)):\n",
    "                yield sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e52bf97a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:31:06.179767Z",
     "start_time": "2022-07-04T19:31:06.177380Z"
    }
   },
   "outputs": [],
   "source": [
    "fn_pats = (\n",
    "    (\"aerial\", os.path.join(BASE_PATH, 'aerial_*.zip'), '*.tif'),\n",
    "    (\"s1\", os.path.join(BASE_PATH, 's1.zip'), os.path.join('s1', RES_SENTINEL, '*.tif')),\n",
    "    (\"s2\", os.path.join(BASE_PATH, 's2.zip'), os.path.join('s2', RES_SENTINEL, '*.tif')),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8e38b611",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:31:06.264004Z",
     "start_time": "2022-07-04T19:31:06.251777Z"
    }
   },
   "outputs": [],
   "source": [
    "# converting input files into datadings msgpack format\n",
    "# useful for much faster reading in future, e.g., by iterating dataloaders (not keeping all in mem)\n",
    "# only has to be done once\n",
    "for name, fnpat, pat in fn_pats:\n",
    "    if os.path.exists(os.path.join(BASE_PATH, name + '.msgpack')):\n",
    "        continue\n",
    "    with FileWriter(os.path.join(BASE_PATH, name + '.msgpack'), overwrite=True) as writer:\n",
    "        for sample in gen_from_zip_files(fnpat, pat):\n",
    "            writer.write(sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "80c361a7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:31:06.292773Z",
     "start_time": "2022-07-04T19:31:06.287681Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50382"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(multilabels_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6ec78baf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:31:37.396426Z",
     "start_time": "2022-07-04T19:31:06.354531Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5e4b8c84f30740a0bbfe13fd96a5e221",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50381 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'key': 'Tilia_spec._9_99911_WEFL_NLF.tif',\n",
       " 'label': 'Tilia',\n",
       " 'path': 'Tilia_spec._9_99911_WEFL_NLF.tif',\n",
       " '_additional_info': [],\n",
       " 'data': array([[[213, 214, 205, ..., 213, 215, 207],\n",
       "         [210, 188, 205, ..., 199, 209, 194],\n",
       "         [205, 171, 195, ..., 189, 205, 179],\n",
       "         ...,\n",
       "         [203, 194, 191, ..., 175, 188, 196],\n",
       "         [221, 209, 203, ..., 178, 186, 184],\n",
       "         [204, 194, 196, ..., 182, 192, 194]],\n",
       " \n",
       "        [[143, 144, 135, ..., 171, 173, 165],\n",
       "         [139, 117, 134, ..., 157, 167, 152],\n",
       "         [136, 102, 125, ..., 147, 163, 137],\n",
       "         ...,\n",
       "         [132, 123, 120, ..., 137, 152, 159],\n",
       "         [150, 138, 132, ..., 140, 150, 147],\n",
       "         [134, 124, 126, ..., 146, 155, 157]],\n",
       " \n",
       "        [[129, 130, 121, ..., 144, 146, 138],\n",
       "         [128, 106, 123, ..., 130, 140, 125],\n",
       "         [126,  92, 118, ..., 122, 138, 112],\n",
       "         ...,\n",
       "         [131, 122, 119, ..., 112, 126, 136],\n",
       "         [149, 137, 131, ..., 115, 124, 124],\n",
       "         [130, 120, 122, ..., 120, 132, 134]],\n",
       " \n",
       "        [[117, 123, 118, ..., 166, 168, 162],\n",
       "         [125, 105, 122, ..., 152, 162, 149],\n",
       "         [130,  94, 118, ..., 142, 159, 133],\n",
       "         ...,\n",
       "         [114, 107, 107, ..., 141, 156, 158],\n",
       "         [130, 121, 118, ..., 145, 157, 148],\n",
       "         [114, 106, 111, ..., 148, 161, 157]]], dtype=uint8),\n",
       " 'multilabels': [['Tilia', 1.0]],\n",
       " 'labelvec': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], dtype=uint8)}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with MsgpackReader(os.path.join(BASE_PATH, 'aerial.msgpack')) as reader:\n",
    "    for sample in tqdm(reader):\n",
    "        pass\n",
    "sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2378fb9a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:31:37.401604Z",
     "start_time": "2022-07-04T19:31:37.398427Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_item_by_key(self, key):\n",
    "    self.seek_key(key)\n",
    "    return next(self)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "14150dd2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:31:37.431712Z",
     "start_time": "2022-07-04T19:31:37.402959Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'key': 'Abies_alba_0_2545_WEFL_NLF.tif',\n",
       " 'label': 'Abies',\n",
       " 'path': 'Abies_alba_0_2545_WEFL_NLF.tif',\n",
       " '_additional_info': [],\n",
       " 'data': array([[[160, 139, 157, ..., 154, 149, 156],\n",
       "         [160, 147, 154, ..., 151, 153, 150],\n",
       "         [153, 158, 157, ..., 157, 162, 152],\n",
       "         ...,\n",
       "         [159, 154, 163, ..., 178, 173, 177],\n",
       "         [150, 147, 146, ..., 189, 189, 189],\n",
       "         [138, 140, 137, ..., 147, 150, 157]],\n",
       " \n",
       "        [[164, 145, 161, ..., 139, 134, 139],\n",
       "         [167, 154, 161, ..., 136, 136, 131],\n",
       "         [164, 169, 168, ..., 139, 143, 130],\n",
       "         ...,\n",
       "         [135, 128, 133, ..., 162, 150, 150],\n",
       "         [129, 123, 119, ..., 184, 177, 170],\n",
       "         [117, 118, 110, ..., 152, 148, 148]],\n",
       " \n",
       "        [[145, 128, 144, ..., 113, 108, 114],\n",
       "         [152, 140, 147, ..., 111, 112, 108],\n",
       "         [153, 160, 157, ..., 120, 122, 108],\n",
       "         ...,\n",
       "         [104,  98, 104, ..., 122, 114, 118],\n",
       "         [ 98,  93,  90, ..., 140, 135, 131],\n",
       "         [ 91,  92,  84, ..., 107, 103, 102]],\n",
       " \n",
       "        [[191, 163, 178, ..., 117, 113, 119],\n",
       "         [196, 176, 183, ..., 113, 114, 111],\n",
       "         [196, 197, 194, ..., 116, 110, 115],\n",
       "         ...,\n",
       "         [132, 124, 130, ..., 160, 166, 155],\n",
       "         [123, 116, 112, ..., 189, 184, 180],\n",
       "         [110, 110, 103, ..., 157, 156, 158]]], dtype=uint8),\n",
       " 'multilabels': [['Abies', 0.81464], ['Quercus', 0.18421], ['Picea', 0.00115]],\n",
       " 'labelvec': array([1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0], dtype=uint8)}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with MsgpackReader(os.path.join(BASE_PATH, 'aerial.msgpack')) as reader:\n",
    "    s = get_item_by_key(reader, TRAIN_FILENAMES[0])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "78b3a7b0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:32:02.156211Z",
     "start_time": "2022-07-04T19:31:37.433456Z"
    }
   },
   "outputs": [],
   "source": [
    "#del multilabels_test, multilabels_train, labels_test, labels_train, aerial_test, aerial_train, s1_test, s1_train, s2_test, s2_train\n",
    "\n",
    "v = vars()\n",
    "\n",
    "if os.path.exists(os.path.join(BASE_PATH, 'aerial_test.npy')):\n",
    "    # slight shortcut for above\n",
    "    for _n in 'multilabels_test, multilabels_train, labels_test, labels_train, aerial_test, aerial_train, s1_test, s1_train, s2_test, s2_train'.split(', '):\n",
    "        fn = os.path.join(BASE_PATH, _n + '.npy')\n",
    "        v[_n] = np.load(fn)\n",
    "else:\n",
    "    for t in ['train', 'test']:\n",
    "        fns = v[f'{t.upper()}_FILENAMES']\n",
    "\n",
    "        for ds in ['s1', 's2', 'aerial']:\n",
    "\n",
    "            with MsgpackReader(os.path.join(BASE_PATH, f'{ds}.msgpack')) as reader:\n",
    "                if ds == 's1':\n",
    "                    # use s1 to get labels as it's the smallest / fastest\n",
    "                    v[f'multilabels_{t}'] = np.array([get_item_by_key(reader, fn)['labelvec'] for fn in tqdm(fns, desc=f'multilabels_{t}')])\n",
    "                    v[f'labels_{t}'] = np.array([classes.index(get_item_by_key(reader, fn)['label']) for fn in tqdm(fns, desc=f'labels_{t}')])\n",
    "\n",
    "                v[f'{ds}_{t}'] = np.array([get_item_by_key(reader, fn)['data'] for fn in tqdm(fns, desc=f'{ds}_{t}')])\n",
    "\n",
    "    # save results of this cell\n",
    "    for _n in 'multilabels_test, multilabels_train, labels_test, labels_train, aerial_test, aerial_train, s1_test, s1_train, s2_test, s2_train'.split(', '):\n",
    "        fn = os.path.join(BASE_PATH, _n + '.npy')\n",
    "        np.save(fn, v[_n])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "562c47d3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:32:02.163167Z",
     "start_time": "2022-07-04T19:32:02.158743Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((45337, 15), (5044, 15))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multilabels_train.shape, multilabels_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "403f5d9e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:32:02.166996Z",
     "start_time": "2022-07-04T19:32:02.164340Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((45337,), (5044,))"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_train.shape, labels_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f3da9d8a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:32:02.171303Z",
     "start_time": "2022-07-04T19:32:02.168479Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((45337, 4, 304, 304), (5044, 4, 304, 304))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aerial_train.shape, aerial_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "db5934c1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:32:02.174984Z",
     "start_time": "2022-07-04T19:32:02.172450Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((45337, 3, 6, 6), (5044, 3, 6, 6))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1_train.shape, s1_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8de907b2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:32:02.178958Z",
     "start_time": "2022-07-04T19:32:02.175998Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((45337, 12, 6, 6), (5044, 12, 6, 6))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2_train.shape, s2_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "86f51bbc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:32:02.184355Z",
     "start_time": "2022-07-04T19:32:02.181074Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0], dtype=uint8)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multilabels_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f5a8c95f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:32:02.187928Z",
     "start_time": "2022-07-04T19:32:02.185429Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b8250dee",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:32:02.347862Z",
     "start_time": "2022-07-04T19:32:02.188947Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f034f80a400>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(aerial_train[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b7425349",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:32:02.448436Z",
     "start_time": "2022-07-04T19:32:02.349197Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f034f7084c0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show aerial RGB bands (3,1,2)... 0 is near infrared\n",
    "plt.imshow(np.dstack(aerial_train[0][[3,1,2]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "97a01bad",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:32:02.452531Z",
     "start_time": "2022-07-04T19:32:02.449768Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45337, 4, 304, 304)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aerial_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "11eef194",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:32:09.742827Z",
     "start_time": "2022-07-04T19:32:02.453569Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([150.97992526,  93.01403697,  85.00857057,  80.97255149])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# doesn't look green? let's try getting mean values for bands...\n",
    "aerial_train.mean(axis=(0, 2, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "bff86d43",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:32:09.747192Z",
     "start_time": "2022-07-04T19:32:09.744902Z"
    }
   },
   "outputs": [],
   "source": [
    "# weird, would've expected NIR-R-G-B, so G = aerial_train[:, 2] to have a higher values than R or B???"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "96d7426f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:32:09.751615Z",
     "start_time": "2022-07-04T19:32:09.748533Z"
    }
   },
   "outputs": [],
   "source": [
    "def show(imgs):\n",
    "    if not isinstance(imgs, list):\n",
    "        imgs = [imgs]\n",
    "    fix, axs = plt.subplots(ncols=len(imgs), squeeze=False, figsize=(4*len(imgs), 3))\n",
    "    for i, img in enumerate(imgs):\n",
    "        axs[0, i].imshow(np.asarray(img))\n",
    "        axs[0, i].set(xticklabels=[], yticklabels=[], xticks=[], yticks=[])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4986f90e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:32:09.942085Z",
     "start_time": "2022-07-04T19:32:09.752946Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x216 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show(list(aerial_train[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "792fd3d4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:32:10.012506Z",
     "start_time": "2022-07-04T19:32:09.943410Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f034f51ae50>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(s1_train[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "aa74ae3d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:32:10.249474Z",
     "start_time": "2022-07-04T19:32:10.013645Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAo0AAAC0CAYAAADxRxYBAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAHg0lEQVR4nO3cTYhddxnH8efM3DuvyZhOZlqbF6qLglITCY2Yhm7sJuBC0FVBF0KhxbooFMSN4saFGxULgqAbBXVTdKFFUkFXLV2oLZVK1AgmMYG8zdhkOjP3zstxIXcx4OTprc6TXPx8tnPg95+Ze+585wxJ07ZtAADAnYzd7QMAAHDvE40AAKREIwAAKdEIAEBKNAIAkOoMc/H4zGzbPTC/V2fZoZ3aLtm5G5qxwn+xvjpetxURYxs1O/3bS7G5/k5Tszaa9s932sXDkyVbs02/ZGfgQq/mfSgior3aLdsqV/RWtL62HBt99+ud3Dc/1h4+MtSP5Pfs2ub+kp2Bt1eny7Y6hS+zzbpP6986NV20ef2fsXX7P38hh3qFdg/Mx0NPP/+/OVWi//Bayc5AU/jMdWKyqKwiIl6fq9uKiJmrNT+Fzv382yU7o2zx8GR8/WePlGx9bOpSyc7As+efLNvqffPBsq1qTdHv5n945YWaoRF2+EgnXnxpoWTrhWtPlOwMvPT68bKthddqwjsiYulY8X9ZuNgrmbnyle/u+jF/ngYAICUaAQBIiUYAAFKiEQCAlGgEACAlGgEASIlGAABSohEAgJRoBAAgJRoBAEiJRgAAUqIRAICUaAQAICUaAQBIiUYAAFKiEQCAlGgEACAlGgEASIlGAABSohEAgJRoBAAgJRoBAEiJRgAAUqIRAIBUZ5iLDx1ciq999qd7dZYdHp++VLIz8Kk3nirbWvjGVNlW8+qrZVsREee/dapkZ+tXJTMjbW17It5cPVqy9eT+5ZKdgdlur2zrr2fGy7b2Xaj9PX7f5e2SnXa8KdkZZRf/fn88+/kvlmxNXKq9XztPD5Ua/5XvffU7ZVuPTk6UbUVEfOgHXyjZaXq7vw950ggAQEo0AgCQEo0AAKREIwAAKdEIAEBKNAIAkBKNAACkRCMAACnRCABASjQCAJASjQAApEQjAAAp0QgAQEo0AgCQEo0AAKREIwAAKdEIAEBKNAIAkBKNAACkRCMAACnRCABASjQCAJASjQAApEQjAAAp0QgAQEo0AgCQEo0AAKQ6w1zc3+7Ehf7CXp1lh5mxXsnOwH0za2Vb108cLNtaO3O6bCsi4rFTfyrZObtvvWRnlC2vz8SL506UbK1sTZbsDCz3Zsq2Tp38c9nW7x44WrYVEbFybbpkZ+OVkpmR1jYR292a5zibi3MlOwOTS03Z1lbUbT3+5mfKtiIi5t9qS3au3CGHPGkEACAlGgEASIlGAABSohEAgJRoBAAgJRoBAEiJRgAAUqIRAICUaAQAICUaAQBIiUYAAFKiEQCAlGgEACAlGgEASIlGAABSohEAgJRoBAAgJRoBAEiJRgAAUqIRAICUaAQAICUaAQBIiUYAAFKiEQCAlGgEACAlGgEASHWGufhmbzZ+/LeTe3WWHT79wYmSnYEvfeBs2dZPPvfxsq0D3bWyrYiIX/zxeMnOyvpkyc4o615v4uj3h7rF37OzT9S8Lww8+olzZVsfnftH2db6oZrv18DywZmSnZvTGyU7o2y728Q7D3RLtvb3t0t2Bo78+u2yrae2nivbmr7Wlm1FRNz4ZK9kZ+O13T8vTxoBAEiJRgAAUqIRAICUaAQAICUaAQBIiUYAAFKiEQCAlGgEACAlGgEASIlGAABSohEAgJRoBAAgJRoBAEiJRgAAUqIRAICUaAQAICUaAQBIiUYAAFKiEQCAlGgEACAlGgEASIlGAABSohEAgJRoBAAgJRoBAEiJRgAAUp1hLn7/9K348odf3quz7HBs8nLJzsDxiamyrceOni3b2mi3y7YiIn751rHSPXbX3F6Nzm9+X7LVOXW6ZGfgxPsulm2dX72/bmtpoWwrImJ1dbJkp78xXrIzyjq3+rHw25rXdbu+XrIzsHXjZtnWkXOzZVvRtnVbEXHzxPGaoa1m1w950ggAQEo0AgCQEo0AAKREIwAAKdEIAEBKNAIAkBKNAACkRCMAACnRCABASjQCAJASjQAApEQjAAAp0QgAQEo0AgCQEo0AAKREIwAAKdEIAEBKNAIAkBKNAACkRCMAACnRCABASjQCAJASjQAApEQjAAAp0QgAQEo0AgCQEo0AAKQ6w1w83fTjkYkre3WWHbbbpmRn4Ee3Fsq25sbXy7YOjK2WbUVERNW3rfblMZL6h2bj4jOnS7bWH9wo2RlY2Zoq23r5jY+UbT38w9qv43a35rnBjRtu2FTbRtvrl0w13W7JzkD/zMmyranLK2VbzdWbZVsREXN/qblfr94hUTxpBAAgJRoBAEiJRgAAUqIRAICUaAQAICUaAQBIiUYAAFKiEQCAlGgEACAlGgEASIlGAABSohEAgJRoBAAgJRoBAEiJRgAAUqIRAICUaAQAICUaAQBIiUYAAFKiEQCAlGgEACAlGgEASIlGAABSohEAgJRoBAAgJRoBAEg1bdu++4ub5npEXNi748C79lDbtot3+xD3Mvcr9xD3a8L9yj1k1/t1qGgEAOD/kz9PAwCQEo0AAKREIwAAKdEIAEBKNAIAkBKNAACkRCMAACnRCABASjQCAJD6F3ROCqWo4VvRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x216 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show(list(s1_train[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "906f21b2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:32:10.319707Z",
     "start_time": "2022-07-04T19:32:10.251144Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f034f5bbac0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPUAAAD4CAYAAAA0L6C7AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAKv0lEQVR4nO3dX2hehR3G8efp27TaqKu6TmrTrW5KUQR1hN4ogxUcrYqO3ayCwkDIxSbUbSB66c3uJl5sN0FlGzqL4B+cc3MF61zBP01rdfaPWycO2zqyUbs2kbVN+ttF3rrEtM15T8/JOfz2/UAw6RteH2u/PXnf5D3HESEAeSxoegCAahE1kAxRA8kQNZAMUQPJLKzjTjv9/dG39JI67roUn2x6wWwLTjS9YKaFh//T9IQZTlx6XtMTZplc3PSC/5k4dEiTY+M+3W21RN239BKt/P4P67jrUvrGTvvf3qjzR9v1rcRlz+9tesIMBzdc3fSEWY5+tT1Hh4M/feSMt/HlN5AMUQPJEDWQDFEDyRA1kAxRA8kQNZAMUQPJEDWQDFEDyRA1kAxRA8kQNZBMoahtr7P9vu19th+oexSA8uaM2nZH0s8lrZd0jaQ7bV9T9zAA5RQ5Uq+RtC8iPoiI45I2Sbqj3lkAyioS9QpJH037eH/312awPWR7xPbI5Ph4VfsA9KiyJ8oiYjgiBiNisNPfX9XdAuhRkagPSFo57eOB7q8BaKEiUW+TdJXtK2wvkrRB0gv1zgJQ1pwnHoyICdv3SnpZUkfS4xGxq/ZlAEopdDbRiHhJ0ks1bwFQAX6iDEiGqIFkiBpIhqiBZIgaSIaogWSIGkiGqIFkiBpIhqiBZIgaSIaogWQKvaCjVz4p9Y25jrsuZfyKiaYnzNI5XstvfWmTn3zS9IQZFh+OpifMMvGP9hwDF5w4y23zNwPAfCBqIBmiBpIhaiAZogaSIWogGaIGkiFqIBmiBpIhaiAZogaSIWogGaIGkiFqIBmiBpKZM2rbj9setf3efAwCcG6KHKl/IWldzTsAVGTOqCPiNUmH5mELgApU9pja9pDtEdsjE5+OV3W3AHpUWdQRMRwRgxExuHBJf1V3C6BHPPsNJEPUQDJFvqX1lKTXJa22vd/2PfXPAlDWnCefjog752MIgGrw5TeQDFEDyRA1kAxRA8kQNZAMUQPJEDWQDFEDyRA1kAxRA8kQNZAMUQPJzPmCjjK+cPG41n/39TruupRnd1/f9IRZjqxuesFMKy6+uOkJMyyYjKYnzHLRh+3Z1Dl25ts4UgPJEDWQDFEDyRA1kAxRA8kQNZAMUQPJEDWQDFEDyRA1kAxRA8kQNZAMUQPJEDWQTJEL5K20vcX2btu7bG+cj2EAyinyeuoJST+OiB22L5S03fbmiNhd8zYAJcx5pI6IjyNiR/f9o5L2SFpR9zAA5fT0mNr2Kkk3SHrzNLcN2R6xPfLp4bOclgFArQpHbfsCSc9Iui8ijnz+9ogYjojBiBhcsnRxlRsB9KBQ1Lb7NBX0kxHxbL2TAJyLIs9+W9JjkvZExMP1TwJwLoocqW+UdLektbZ3dt9uqXkXgJLm/JZWRGyV5HnYAqAC/EQZkAxRA8kQNZAMUQPJEDWQDFEDyRA1kAxRA8kQNZAMUQPJEDWQDFEDyRQ5R1nPjp5YrD9+fGUdd13K8ucXNT1hlsNf6zQ9YYYD37u66QkzTLbvf5ku/9N40xM+s+D4yTPfNo87AMwDogaSIWogGaIGkiFqIBmiBpIhaiAZogaSIWogGaIGkiFqIBmiBpIhaiAZogaSKXLVy/Nsv2X7Hdu7bD80H8MAlFPk9dTHJK2NiLHudaq32v5dRLxR8zYAJRS56mVIGut+2Nd9izpHASiv0GNq2x3bOyWNStocEW+e5nOGbI/YHpk48mnFMwEUVSjqiJiMiOslDUhaY/va03zOcEQMRsTgwouWVDwTQFE9PfsdEYclbZG0rpY1AM5ZkWe/l9le2n3/fEk3S9pb8y4AJRV59nu5pF/a7mjqL4GnI+LFemcBKKvIs9/vSrphHrYAqAA/UQYkQ9RAMkQNJEPUQDJEDSRD1EAyRA0kQ9RAMkQNJEPUQDJEDSRD1EAyRV6l1bMFox0t+dnSOu66lM6xiaYnzNJZ2Wl6wgyTi5peMNP46uNNT5jloPqbnvCZE3878/GYIzWQDFEDyRA1kAxRA8kQNZAMUQPJEDWQDFEDyRA1kAxRA8kQNZAMUQPJEDWQDFEDyRSOunvh+bdtc3E8oMV6OVJvlLSnriEAqlEoatsDkm6V9Gi9cwCcq6JH6kck3S/p5Jk+wfaQ7RHbIyeOj1exDUAJc0Zt+zZJoxGx/WyfFxHDETEYEYN9i9pz2hfg/02RI/WNkm63/aGkTZLW2n6i1lUASpsz6oh4MCIGImKVpA2SXomIu2pfBqAUvk8NJNPTKYIj4lVJr9ayBEAlOFIDyRA1kAxRA8kQNZAMUQPJEDWQDFEDyRA1kAxRA8kQNZAMUQPJEDWQDFEDyfT0Kq2iLl35b9398G/quOtSfrJ9fdMTZrnuy39tesIMQ5e/1vSEGdYtOdb0hFm+c+XNTU/4zMHnzvz7w5EaSIaogWSIGkiGqIFkiBpIhqiBZIgaSIaogWSIGkiGqIFkiBpIhqiBZIgaSIaogWQKvfSye23qo5ImJU1ExGCdowCU18vrqb8ZEf+qbQmASvDlN5BM0ahD0h9sb7c9dLpPsD1ke8T2yNFPTlS3EEBPin75fVNEHLD9JUmbbe+NiBnnv4mIYUnDkrTq2guj4p0ACip0pI6IA91/jkp6TtKaOkcBKG/OqG33277w1PuSviXpvbqHASinyJffl0l6zvapz/91RPy+1lUASpsz6oj4QNJ187AFQAX4lhaQDFEDyRA1kAxRA8kQNZAMUQPJEDWQDFEDyRA1kAxRA8kQNZAMUQPJOKL68xnY/qekv1dwV1+U1KbzorHn7Nq2R2rfpqr2fCUilp3uhlqirortkTaduZQ9Z9e2PVL7Ns3HHr78BpIhaiCZtkc93PSAz2HP2bVtj9S+TbXvafVjagC9a/uRGkCPiBpIppVR215n+33b+2w/0II9j9setd2KUyPbXml7i+3dtnfZ3tjwnvNsv2X7ne6eh5rcc4rtju23bb/Y9BZp6kKTtv9se6ftkdr+PW17TG27I+kvkm6WtF/SNkl3RsTuBjd9Q9KYpF9FxLVN7Zi2Z7mk5RGxo3tO9u2Svt3U75Gnzh/dHxFjtvskbZW0MSLeaGLPtF0/kjQo6aKIuK3JLd09H0oarPtCk208Uq+RtC8iPoiI45I2SbqjyUHdSwwdanLDdBHxcUTs6L5/VNIeSSsa3BMRMdb9sK/71ujRwvaApFslPdrkjia0MeoVkj6a9vF+NfgHtu1sr5J0g6Q3G97Rsb1T0qikzRHR6B5Jj0i6X9LJhndMN+eFJqvQxqhRkO0LJD0j6b6IONLkloiYjIjrJQ1IWmO7sYcptm+TNBoR25vacAY3RcTXJa2X9IPuw7rKtTHqA5JWTvt4oPtrmKb72PUZSU9GxLNN7zklIg5L2iJpXYMzbpR0e/cx7CZJa20/0eAeSfN3ock2Rr1N0lW2r7C9SNIGSS80vKlVuk9MPSZpT0Q83II9y2wv7b5/vqae5Nzb1J6IeDAiBiJilab+/LwSEXc1tUea3wtNti7qiJiQdK+klzX1BNDTEbGryU22n5L0uqTVtvfbvqfJPZo6Et2tqSPQzu7bLQ3uWS5pi+13NfWX8uaIaMW3kVrkMklbbb8j6S1Jv63rQpOt+5YWgHPTuiM1gHND1EAyRA0kQ9RAMkQNJEPUQDJEDSTzX95Ti5astGTYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(s2_train[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ee72c5f9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:32:10.388887Z",
     "start_time": "2022-07-04T19:32:10.321291Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f034f5967c0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# RGB are first in our s2 data\n",
    "plt.imshow(np.dstack(s2_train[0][[0,1,2]]/750))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "64a16551",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:32:10.457331Z",
     "start_time": "2022-07-04T19:32:10.390275Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f034f655a90>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show s2 RGB Bands (4,3,2)  # warning, were re-ordered\n",
    "plt.imshow(np.dstack(s2_train[0][[4,3,2]]/6000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8130ffd9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:32:10.815258Z",
     "start_time": "2022-07-04T19:32:10.458671Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3456x216 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show(list(s2_train[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc7c8f73",
   "metadata": {},
   "source": [
    "## preprocess aerial (ResNet features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7d84879b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:32:14.604837Z",
     "start_time": "2022-07-04T19:32:10.817527Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://github.com/pytorch/vision/archive/v0.10.0.zip\" to /root/.cache/torch/hub/v0.10.0.zip\n",
      "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "36debfe1c9484aa6849d6187e95351ca",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0.00/44.7M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "resnet = torch.hub.load('pytorch/vision:v0.10.0', 'resnet18', pretrained=True).eval()\n",
    "weights = torch.load(RESNET_WEIGHTS_FILE, map_location=torch.device('cpu'))\n",
    "torch.set_num_threads(min(64, joblib.cpu_count()))  # lots of red bars... sys time...\n",
    "resnet.weights = weights\n",
    "feature_extractor = torch.nn.Sequential(*list(resnet.children())[:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "cafa03d6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:33:23.987646Z",
     "start_time": "2022-07-04T19:32:14.607092Z"
    }
   },
   "outputs": [],
   "source": [
    "mean = aerial_train[:, [3,1,2], :, :].mean(axis=(0, 2, 3))\n",
    "stddev = aerial_train[:, [3,1,2], :, :].std(axis=(0, 2, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c312b975",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:33:46.868228Z",
     "start_time": "2022-07-04T19:33:23.990457Z"
    }
   },
   "outputs": [],
   "source": [
    "crop = (aerial_train.shape[-1] - 224) // 2\n",
    "prep_aerial_train = (aerial_train[:, [3,1,2], crop:-crop, crop:-crop] - mean.reshape(1, 3, 1, 1)) / stddev.reshape(1, 3, 1, 1)\n",
    "prep_aerial_test = (aerial_test[:, [3,1,2], crop:-crop, crop:-crop] - mean.reshape(1, 3, 1, 1)) / stddev.reshape(1, 3, 1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f59f2ff1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:38:46.902757Z",
     "start_time": "2022-07-04T19:33:46.874677Z"
    }
   },
   "outputs": [],
   "source": [
    "with torch.no_grad():\n",
    "    feats_aerial_train = feature_extractor(torch.tensor(prep_aerial_train, dtype=torch.float32)).squeeze().detach().numpy()\n",
    "    feats_aerial_test = feature_extractor(torch.tensor(prep_aerial_test, dtype=torch.float32)).squeeze().detach().numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37fc8ccf",
   "metadata": {},
   "source": [
    "## simple single labels lightgbm training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "8be4b5b2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:38:46.908668Z",
     "start_time": "2022-07-04T19:38:46.904736Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train = s2_train.reshape(len(labels_train), -1)\n",
    "y_train = labels_train\n",
    "X_test = s2_test.reshape(len(labels_test), -1)\n",
    "y_test = labels_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a9544748",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:39:00.842285Z",
     "start_time": "2022-07-04T19:38:46.909793Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMClassifier(n_jobs=32)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# clf = ltb.LGBMClassifier(\n",
    "#     boosting_type='gbdt',\n",
    "#     objective='multiclass',\n",
    "#     num_class=len(classes),\n",
    "#     learning_rate=LEARNING_RATE,\n",
    "#     n_estimators=NUM_ESTIMATORS,\n",
    "#     #verbose=1,\n",
    "# )\n",
    "\n",
    "clf = ltb.LGBMClassifier(n_jobs=min(32, joblib.cpu_count()))  # seems more than 32 actually slows things down\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b260eb6a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:39:01.538090Z",
     "start_time": "2022-07-04T19:39:00.843917Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LightGBM Model accuracy score: 0.5710\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       Abies       0.54      0.40      0.46        93\n",
      "        Acer       0.36      0.22      0.27       282\n",
      "       Alnus       0.25      0.11      0.15       247\n",
      "      Betula       0.37      0.22      0.28       281\n",
      "     Cleared       0.63      0.75      0.69       422\n",
      "       Fagus       0.55      0.73      0.63       639\n",
      "    Fraxinus       0.35      0.23      0.28       255\n",
      "       Larix       0.51      0.39      0.44       316\n",
      "       Picea       0.68      0.78      0.73       571\n",
      "       Pinus       0.69      0.80      0.74       759\n",
      "     Populus       0.62      0.06      0.11        81\n",
      "      Prunus       0.25      0.07      0.11        29\n",
      " Pseudotsuga       0.64      0.52      0.57       278\n",
      "     Quercus       0.53      0.68      0.59       769\n",
      "       Tilia       0.08      0.05      0.06        22\n",
      "\n",
      "    accuracy                           0.57      5044\n",
      "   macro avg       0.47      0.40      0.41      5044\n",
      "weighted avg       0.54      0.57      0.54      5044\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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ftYfLrojH5RI2rG/Me3Nqtm2Vq41lq7npigMgIlcCXwMdlVK7/ubiAOB0CLOfb0L8tgD8A53MWrKbTb8Gk7jHr8Yxx9y/jY3rGzL56X7YbC58/Rxsjm3I+7M74XJauGPsn1x38x7mvtPZq7If3OfHPSM6Au4DzEex21izJNSrmJV59Np25GUbswov/6oh337YiPGvxB8d9tID7Y6+v2tiAkfya96B0aGkYO77v+GAe5588PkPrF3dBF9fJy8+PZD7Ht5U49hOpzBnamv27gjCP9DBjC+3sOm3MMIjyxgwNItxo3rhKLMQGlFa43Gc6Mq70kna40tAsMuwmAANoksZdUcao4d1prTEwhNv7WPI5Vks/yLSkPhmbFcVWSyKcZMPMfGG1mQctjNz0R7WLQ01JL6Zsc2Kf+hgCPeNvdgT38UHH3/P2jUxdOuexoBzDzFu7MU4yqyEhnnfQZXZy7bG5aqHlyrq3xTVzL+A1Z7/vSIihhzJstLsxG8LAKCo0EpSvB+RjctqHC8gsIwu3TNZ+kMLABwOC4UFPmze0BCX070a7PoznMioIu8LX0GPwfkcPuBL2iFze2301vYNIeTnnGzRKc7/RyYrvzPm4NW9VxopyUGkpQaSlBjCoaRgr+Jlp/uwd0cQAEWFNhL3+hMZXcrIGw7z+f+a4ihzL9/cLO8ySeUiG5fSb1geiz9uYEi8E1ltCh8/FxarwtffRWaqMeUG47erE7XveYTkBB9SEn1xlFlYuTCMgSNy63zs2ojfvWcaKYcDSUsLZOTl8Xz+SUccZe7KeG6O9wd3s5ftmUREEkRkm4hsEZFYz7AIEVkuIns8/4d7houIzBCReBHZKiK9Thf/rK84iEgQMBi4E7jBM8wqIq+KyHbPjLzPM7y3iPwiIhtFZKmINPYMXykib3gW0ANGlzG6aSltuhSxa1NAjWM0alxIbo4PD03cxMw5K3jg0c34+jmO+83F/zhA7DpjU59Drshm5cLqp99PS8HkBXuYtWgnl96UYXz8Crr0zSc7w07yAX9D4l0wNImVPzU1JNaJGsYU06ZjIbv+CKZJyyI698nj9U+3MHX+H5zTxZgupcc+d4g5LzZBGZtsACAz1YcvZkczf902FsRupTDPyqZVIcaPCGO2qxM1aFRGevKxik7GYbthBy8zY9dG/AuGJLJyRXMAmjQtoHPXdF6f8SNTp63gnHZZho0HzFm2NaEAl7IY+qqGC5VSPZRSfTyfHwd+UkqdA/zk+QxwKXCO5zUaePt0gc/6igMwCliilNoNZIpIb9wzryXQQynVDfhIROzATOAapVRv4D3gPxXi+Cil+iilpp04AhEZLSKxIhJbRkm1CucX4OSpOQm883QTjhTUPFVutSranpPLom9acd9dF1JcbOW6m3Yf/f76W+JwOi2sWG7cAc1mdzHg4hx+/d74isPDV7fj3ks7MumWtlxxWzpd+pv3nIUhl2Xwy/fGZBtsNhf9zz3M6l+Mrzj4BTiZNGMns6e0pqjQhtWqCA4t46Hru/Puy62Z+MZO3Luymus/PJecDNvRMzujBYU6GHhRLrcP6sJNfbvhF+Bk6FWZho/HqO1KqxqbzUn/gcms/qUZAFaLi+DgUh66fxjvzu7GxCfX4u26WU4v25MaBczzvJ8HXFlh+AfKbR0QVn5SfDK64uC+PPGJ5/0nns/Dgf8qpRwASqksoD3QBVguIluAJ4GKe/9PTzYCpdRsT6Wij52qp+ytNsVTcxL4+atw1iwOq/oUVSIj3Z+MdD/idkYAsHplE9q0c6chh19ygH4DU3jlhd5gYPeofS/MI35bADkZdsNilstMcZ8Z5WbaWbMklA49jhg+DgCLVXHuiCx+/cGYtHyf/ins3R1GTrax112tNheTZuxg5XdR/LbcXcnJSPX1vBd2bwtGuYSQcO/OIDv1KWTAxXnMW/cnE986QPdB+Tw644ABU+DWc3A+qUk+5GbZcTqENUvC6di70LD4YOx2daLMFDtRTY61JYlsXEbGYWPWfzNjmx2/T98U9saHk+O5JJGREcBvq5sCwu64BigFIaHVO6mqjJnLtmYEp8GvKlLAMk92fLRnWLRS6rDnfQpQnl6OAZIq/O1Bz7CTOqsrDiISAQwF5ohIAjABuO5kPwf+9KR+eiiluiqlLq7wvbF7NxQPT0siaY8fX832vmVwdpYf6WkBxDRzn5n36J1OYkIwvfulcs2N8Tw3cQAlJca2lR0yKpuVCyMMjQng6+/EP9B59H3v8/NJiDOnAVTPQTkc3OdHRooxbTQuGJrELz83MyTWMYoHX9xD0t4Avn7/WF123Y8N6NYvB4CYlkew2V3kZXt3IJj7UhNu7tOZ2wZ0Zso9LfhjTTAv39/Cq5gVpR3yoUOvQnz9XICix6A8kuKNXLbGblcnitsSQEyrUqKblWCzuxgyKod1y4xpGGxmbLPjX3BhIr94LlMArPutCd16uJ/2GhOTj83mIi/X223M3GVbEyZdqogsz2B7XqMrGfVgpVQv3JchxonI+ceVSymFFymes/2uimuA+UqpMeUDROQX4A9gjIisUEo5PBWMOCBKRAYqpdZ6Ll20U0r9aUbBOvcrZPi12ezb4cdby92PlJ07pTEbfq759d53pnfl0ac2YrO7SEkO4PUpvXhj9i/YfVz857U1AMTtiGDWtB5el9/X30mv8/OY/njz0/+4msKjHDwzZx/gvgSz4ptwYld6t4N77PXddOufR0i4g/mrNzJ/elOWfR7NBSONaxTp6+egZ+80Zr52rO3RwMGHuPv+PwgNLeHZKWvYtzeUpx49r1pxO/XKY9iVaeyPC2Dm1+67M+a93pJlX0Xz4H9289a3G3GUCa893h4jM0pmiNsSyKpF4cxatAOnU9j7ZwCLFxgz/8Gc7aoil1N4c1IMkxfsw2KFZZ9EcGC3MRUfM2ObGd+93qcy843eR4ctW9KKBx/ZwFuzl+BwWHjtlX54u26avWzrkIwK7RYqpZQ65Pk/TUS+BvoBqSLSWCl12HMpIs3z80NAxbOZpp5hJyXuisfZSURWAFOVUksqDLsf6AgUAZcAZcD/lFKzRKQHMAMIxV3pekMp9T8RWQmMV0rFnm6cIRKh+ssww6elnLWtMffpn4xzX6Kp8c1k8TP3zg5p1sTU+BxOO/1vvODMN6+dCIBYzb3erByO0/9IM4W1c3tT4zv/jDMt9nr1E3kqy5QaddMuoWrcZ4MMjflE58UbT1VxEJFAwKKUyve8Xw48DwwDMpVSL4nI40CEUupRERkJ3Av8A+gPzFBK9TtVGc7qjINS6sJKhs2o8PHhE77bAhyX8vEMH2J02TRN0zStBqKBr0UE3Mf4BUqpJSKyAfhMRO4EDnDssvwi3JWGeOAIcMfpRnBWVxw0TdM0zSxKSXVvoTRgnGof8JduOJVSmbizDicOV8C46oxDVxw0TdM0zST6sdqapmmapp3VdMZB0zRN00ygAFcdv5OpJnTGQdM0TdO0KtMZB03TNE0zhdTLNg664lDPuJKSTY1vDQkyNb4zr8C02MrpNC02gOTkmRrfVeJ9l7ynIj7GPYWy0vhicsrW7H4iTJ7/ZzKXnz6UVMbdc6S+VKFpmqZp2llMVxM1TdM0zSTOenh+Xv+mSNM0TdM00+iMg6ZpmqaZQCG6jYOmaZqmaWc3nXHQNE3TNJO46uH5ua44aJqmaZoJlAJnPbxUoSsOdVifIXmMfSEZq0Wx+OMIPpsV7VW8h6buo//QHHIy7Yy9pCsAd01MpP+wHBxlQvIBP16b0IrC/OqvFpGNinlkShzhkWUoBUs+a8zCD2N4fNpOYlodASAo2EFBvo37ru7t1XQABIY4eOiVRFq2L0IpeO2RFuzcVPM+JiqbN+WuvuswoyclcV2vnuRl22s8DotFMf2j9WSm+fLsAz2JblLE4y9tJTi0jPidIbz6ZBccjpqdnVRW/lsfPsjAi7JxuYScTBvTxrcmK61mfTWYGf+hqfvod2E2OZl27r6029HhV9yawmW3pOJyCr+vCOO9qc0NK7tR631ljN5uayt2VJNSJkxPJCzKAQoWfdiAb96N8jruqMt3cemIvYjA4qVt+ObbDpw3KJGbb9xGs6a5PPDICPbEN/B6PA+/lkj/4fnkZNgYM7S91/G0k6t/OZTTEJErRUSJSAfP55Yisv3vLteJLBbFuMmHePKmVvzfkPZcOCqH5ucUexVz+ZeRPHn78RvUptWhjBnRlbsv7cqh/X5cf8/hGsV2OoQ5L7dm7OV9ePiGHlx2YzLN2hTy0iMdue/q3tx3dW/WLI/kt+WRXk1DubufO0jsyhDuGtKZuy/uSGK8n1fxKps3AJGNS+h9Xi6ph7zvHGnUjYkk7Q88+vnfD+zh649acNeowRTk27j4qkM1jl1Z+b+Y3Zi7L+3KuJFd+P3nMG66v27GX/5FJE/e0eG4Yd0G5DLgomzGjezK2Eu68eWcxoaW3aj1/kRmbLe1ERvc2/Ds55swekgHHrjsHC6/PcPr+C2a53DpiL088MgI7r7vUvr3PUTjxvkkHAjlhcnnsf3PhgaVHpZ9GsGkm1oZFs8oLiWGvuqCs67iAPwLWO35v85q3/MIyQk+pCT64iizsHJhGANH5HoVc/vvIeTnHH9WtWlVKC6ne2XctTmQyEalNYqdneHL3p3BABQdsZG4L4DIhhVjKc4bkc4vi7zfUQQEO+nav4AlH7vPUhxlFgrzvDtbrGzeAIx5KpE5LzV3dwHnhQYNi+k7OIOlX8d4hii69c1i9Y/u+fHjd00YOCS9xvErK/+RgmM9Kfr5u1Be7HTMjL99w19jj7wpjc/eaUJZqXsXlZtZ80yPmev9iczYbmsjNkBWmp34bQEAFBVaSYr3I7JxmVcxmzfLIy6uASUlNlwuC9u2N2TQwCSSDoZy8FCIEcU+avv6IPKzdRK9NpxVFQcRCQIGA3cCN1Ty/e0i8pWILBGRPSLycoXvCiq8v0ZE3ve8v1ZEtovIHyLyq1FlbdCojPTkY2e5GYftXm/Ep3PxdRnE/hLqdZyGTYpp07GAXVuDjw7r0juXnEwfkg/4ex2/UbMScrNsPPLaAd5cspMHXzmAr7/x3UkPuCibzBQf9u8M8DrWmAlxvDf9HFwu9+eQsDIK8224nO5NMCPVjwYNjTt7LHfb+CTmr9nChaMymf96zOn/oI7Ej2lVTJe++bz+1XZe/ngH7bqZ1xW5Ues9mLvd1uY+IbppKW26FLFrk3frfsKBUDp3Tic4uARfXwd9+yQTFXnEoFLWfe7bMS2GvuqCulGK2jMKWKKU2g1kikhlF9t7ANcDXYHrRaTZaWI+DYxQSnUHrqjsByIyWkRiRSS2jLrZ3/0N45JxOoSfv/HuWqNfgJNJ03cwe0obigqP1f4vGJnOSgOyDQBWm6JtlyN8Pz+KcZd0pPiIhevHpRoSu5yvn5Mb7knmAwMOhv3OSycny4f4ncaeYVXFvFebccugHqxY2IDLbzV2HpkZ32pVBIc6eOjqzsyZ0pyJM+PxOu1TCaPW+/rEL8DJU3MSeOfpJsdllWoi6WAon3/ZicnP/8yLz65g775wXK66kW6vLU7E0FddcLZVHP4FfOJ5/wmVX674SSmVq5QqBnYALU4Tcw3wvoj8H1DpVqaUmq2U6qOU6mPHt0oFzUyxE9XkWPo0snEZGYdrnq49lYv+mU7/odm8/GBr8GLFtNpcTHpjByu/b8hvPx5ry2CxKs4dnsGvi71vaAWQcdiH9MM+xG12txdY/UM4bbsaexbTuEUJjZqW8Pai7cxbtYXIRqXM+u5PwiOrn9Lu1COHARekM/eHVTz20ja69c1izIQ4AoMdWKzuFERkdDGZad610ziVnxc2YPAl2WdM/IwUH9YsDQeE3VuDUC4IjXAYFh+MW+8rMnO7rY19gtWmeGpOAj9/Fc6axWGGxFy6vA33PXQpEyZeREGBD4eSg0//R1qddtZUHEQkAhgKzBGRBGACcB1/3WNUTAk4OXbnScXTnaN7eKXUWOBJoBmwUUQMOXWJ2xJATKtSopuVYLO7GDIqh3XLjEmnVtT7/ByuGXOYZ/+vHSXF3pxdKB58YTdJ+wL4el7T477pOTCbg/sDyEytWqXpdLLT7WQk22na2p3a7zE4j8Q9xh50E+ICuKFvL247rwe3ndeDjBQf7r28M9kZ1W8k+f7Mc7j1kvO5Y+R5TH28K1s3RPDKpK5sjQ1n8PA0AIZfnsy6lcZUrMo1aXns0sfAi7JJ2mfsPDIz/trl4XQfkA9ATKsibHZFbpZx16+NW++PZ+Z2a/4+QfHwtCSS9vjx1Wzj1sXQUPd6EhVVyKBzD7Lil5aGxa7ryp+OWd8aR55NLUmuAeYrpcaUDxCRX3Af8KsiVUQ6AnHAVUC+J0YbpdR6YL2IXOqJl+ltYV1O4c1JMUxesA+LFZZ9EsGB3d7tmB+fHk+3AfmEhDuY/9tmPnyjKdffnYzdRzF5fhzgbig288nqt0zu1CuPYaPS2B8XyMyvNgIw741WxP4awfmXpvPLImMPim8+1YzHZiZg83GRcsCXaY+cLjF0apXNm6WfGVvmE82dfg6PvbSNW++JZ29cMEu/qfllkcrK33dIDk1bF6MUpB7yZeaklnUy/mPT4+nWP88de80m5k9vyrLPo3ho6j7eXrwVR5kwbULNswJmrvcnMmO7rY3YAJ37FTL82mz27fDjreXu+TJ3SmM2/OzdJbanJq4iOLgEp9PCm2/3obDQh3MHJHH3mFhCQ0t4/ulf2Lc/jEnPDPVqPI+/dYBuAwsIjXDwYewO5k+LZunH+hKUGUQp468b1kUisgKYqpRaUmHY/cClQDOlVBcRuR3oo5S61/P998CrSqmVInINMBVIB2KBIKXU7SLyFXAO7r3aT8CD6hQzNUQiVH8ZZs5EAuJrzFn9yVj8zUunAzjzzGsEJ3Zz68mWMOMzQhW5coxrQf93EDH3bMnsfZkqqZvtk+oC6d3Z1Phq45+mxV6vfiJPZZmyckZ1ilRXfTDS0Jj/6/vBRqVUH0ODVtNZk3FQSl1YybAZwIwKn98H3q/w+bIK778AvqgkxtUGF1XTNE2rJ1x1pEGjkc6aNg6apmmapnnvrMk4aJqmaVptqq/PqtAZB03TNE3TqkxnHDRN0zTNJHWlt0cj1b8p0jRN0zTNNDrjoGmapmkmcD+rov61cdAVh/rGafzDno4Lb2I/CwDW1s1Ni61SM0yLDYDL5D5RTF624uP9o8NPRZlcfrP70XCmppka31QW43rHrIw1u9DU+MZ2Nl679O2YmqZpmqad1XTGQdM0TdNMUP6sivpGZxw0TdM0TasynXHQNE3TNJPUx9sxdcVB0zRN08xQhx6FbaT6VxXSNE3TNM00OuNQh/UZksfYF5KxWhSLP47gs1nRhsW+6s5ULvlXBkpBwi5/po1vSVmJcfXIeWu3U1RoweUUnA7hvpEdvI4ZGFTKA49uoUWrPBTwxku9OPf8ZPqfm4LDYeHwoUBef6knhQXVv60wslEJ41/eTXiDUpQSFn8WzcIPYrjz0f30vzALR5lwONGP1ya2ozC/+puN3cfJy3M3Yre7sNoUq5c35KO32/Dy3Fj8A9w3m4VFlLJ7eygvPNS92vErMmPZPjQlnn5Ds8nJtHP3P3oA0LpjIfe9sA+7jwunU3jzmVbs3hpc/dhT99F/aA45mXbGXtL1uO+uvuswoyclcV2vnuRl22tUdruPk5ffjcXu48JqVaz+MZqP3mlD975Z3PnQbmx2F/E7Q3jjuU64nN5vA2Zut2bGBggMcfDQK4m0bF+EUvDaIy3YuSnIu5hBZdz/2GZatM4HBW9M6cnBxCAefz6Who2OkJYSwEtP96Eg37vbgaOalDJheiJhUQ5QsOjDBnzzbpRXMb2lqJ+3Y9bbioOIOIFtgABO4F6l1G+n+H0YcKNS6q0qxF4JjFdKxRpT2r+yWBTjJh9i4g2tyThsZ+aiPaxbGkriHj+vYzeILmXUHWmMHtaZ0hILT7y1jyGXZ7H8i0gDSn7Mo9e2Iy/buFVszP3b2Li+IZOf7ofN5sLXz8Hm2Ia8P9u9w79j7J9cd/Me5r7TudqxnU7hfy+1Yu+OIPwDHcz4cgub14SzeU0Yc6e1xOUU/j1+P9ePSeK9V1tVO35ZqYWJd/WiuMiG1ebi1fdjiV0dyaN39Dn6m0nT/mDtCu92dGYt2+VfNeTbDxsx/pX4o8PufOwAH81oSuyv4fS9IJs7H0vksZuqP++XfxnJdx9EM37avuOGRzYuofd5uaQe8u6AUlZqYeLo3sfm/Xsb2LS2AQ8/v50nxvTmUGIgN98dz/DLD7PsmxivxmXmdmtm7HJ3P3eQ2JUhvDimNTa7C19/l9cxRz+wjY3ro5nyVPl26+S6W3bzx8ZIPv+wHdfevJtrb97D3Lerv+5U5HQIs59vQvy2APwDncxasptNvwYbOn80t/p8qaJIKdVDKdUdmAhMOc3vw4B7TC9VFbXveYTkBB9SEn1xlFlYuTCMgSNyDYtvtSl8/FxYrApffxeZqeZ2/uOtgMAyunTPZOkPLQBwOCwUFviweUPDo2eJu/4MJzKqqEbxs9N92LvDfWZVVGgjaV8ADaJL2LQmHJfTfcawa0swkY1KazgFQnGRuxJlsymstuM7i/IPdNCtXzZrVzSsYfxjzFi22zeEkJ9zfCVQKQgIcnfqFBDsJDO1ZhmB7b//NTbAmKcSmfNSc/dpm1f+Ou9dTsFRZuFQYiAAm9c1YNCwVG9HZOp2a/Y+ISDYSdf+BSz5uAEAjjILhXneVfzLt9tl37s7dnNvt3YGnHeYHxe7h/24uDkDzjvsXeGBrDQ78dsCACgqtJIU70dk4zKv43rL5WnnYNSrLqi3GYcThADZ5R9EZAJwHeALfK2UegZ4CWgjIluA5cAPuLMKl3n+ZhYQq5R6v2JgEXkb6Av4A194YnmtQaMy0pOP7fAzDtvp0OuIEaHJTPXhi9nRzF+3jZJiC5t+DWHTqhBDYh+lYPKCPaDgh4+iWPyRd2e8jRoXkpvjw0MTN9G6TR7xu8N4Z0ZXSoqPrcIX/+MAv/7c1NuS0zCmmDYdC4n74/i0+8X/TOWXxTXPCFgsiukfr6dJ8yK+/7QpcduO9XQ48MI0/lgfQVGhd5tkrSxbj/++2JIX5+7krokHEFE8cl3X0/9RFQ24KJvMFB/27wwwJJ7Fopi+YB1NmhXx/afNiNsegtWmOKdTLnt2hDJ4eCpR0SVej8fM7dbM2ACNmpWQm2XjkdcO0LpTEXu2BfD2000pKap5r5ONGh9xb7dPbKZV2zzi40L57/SuhIWXkJ3pzgRkZ/oSFu79vK8oumkpbboUsWuTMetPTel+HM48/iKyRUR2AXOAFwBE5GLgHKAf0APoLSLnA48Dez1ZignVGM8kpVQfoBtwgYh0M3IizBAU6mDgRbncPqgLN/Xthl+Ak6FXZRo6joevbse9l3Zk0i1tueK2dLr0z/cqntWqaHtOLou+acV9d11IcbGV627affT762+Jw+m0sGK5dxUHvwAnT87YyX8nt+JIhYP4DWOTcDqFFd/WvOLgcgn3XT+AWy8eTLsuebRoe6z77iGXpvLLYu+vV9fGsi038sZUZv+nJbee15vZk1vy4JS9hsT19XNywz3JfPC6d5cNKnK5hPtuGMitI86jXZdcWrQp5KXHu/J/j+zm9fnrKSq04fQ+K39Gs9oUbbsc4fv5UYy7pCPFRyxcP867LIzF6qJtu1wWfdOS+/89hOJiG9fevOeEX4nnZQy/ACdPzUngnaebcKTA3K62z1b1ueJQfqmiA3AJ8IGICHCx57UZ2AR0wF2RqKnrRGSTJ15noNOJPxCR0SISKyKxZVStZp2ZYieqybG0eGTjMjIO1ywVfKKeg/NJTfIhN8uO0yGsWRJOx97G9jWfmeI+M8rNtLNmSSgdenh3ZpSR7k9Guh9xOyMAWL2yCW3audO0wy85QL+BKbzyQm+82QFZbS6enLGTFd815LflxzIkw69Kpd+QLF4e396r+OUK8+1s3RBO73PdB/SQsFLadcnj91XetzGpjWVbbvjV6axZ6l4eqxY1oH13Y55j0rhFCY2alvD2ou3MW7WFyEalzPruT8Ija3qZ6JjCAjtbY8PpfW4Gu7aG8eidfXnolv5s2xRO8oFAr+Obud2aGRsg47AP6Yd9iNvsng+rfwinbVfvttvM8u12h3s9WbOiCW3b5ZKT7Ut4g2IAwhsUk5NtzKVSq03x1JwEfv4qnDWLwwyJ6a36eKmiPlccjlJKrQUigSjce/4pnkpFD6VUW6XUu5X8mYPj589fWtiISCtgPDBMKdUN9+WNv/xOKTVbKdVHKdXHjm+Vyhy3JYCYVqVENyvBZncxZFQO65YZ8xCftEM+dOhViK+fC1D0GJRHUrxxDYh8/Z34BzqPvu99fj4Jcd7Fz87yIz0tgJhm7sxFj97pJCYE07tfKtfcGM9zEwdQUuJNml/x4H/2kLQvgK/fP3am2/u8bK696yDP3d2JkuKan72EhJcSGOy+3urj66TngCwOJrjTqIMvSuP3XyMpK/X+7MjsZVtRZqoPXfvnAdBjYB6HEowZT0JcADf07cVt5/XgtvN6kJHiw72XdyY7o2YHl5DwUgKDKsz7/lkcTAgkNNx9ELbZXVx7ewKLvvD+MpeZ262ZsQGy0+1kJNtp2tp9QO8xOM/rhoXu7db/6HbbvY97u12/ujHDL00EYPiliaxb1di7wgOgeHhaEkl7/Phq9t97N0V9d1a0cRCRDoAVyASWAi+IyEdKqQIRiQHKgHyg4kXtA0AnEfHF3X5hGLD6hNAhQCGQKyLRwKXASiPK7HIKb06KYfKCfVissOyTCA7sNmbHHLclkFWLwpm1aAdOp7D3zwAWLzDujorwKAfPzHG3kLdaFSu+CSd2pfc7uHemd+XRpzZis7tISQ7g9Sm9eGP2L9h9XPzntTUAxO2IYNa0HtWO3bl3HsOvTGd/XACzvtkMwLzXWjD2Sffthv+Zux2AXX8EM+uZttWOHxFZwiMv/onFAmJRrFoWze+/undu549I4fP3WlY7ZmXMWraPvb6bbv3zCAl3MH/1RuZPb8qMSa0Z81QCVquitMTCjEmtaxT78enxdBuQ747922Y+fKMpSz8zbscfEVnCI8//icWi3PN+eTS/r4ri3w/upt95GVgsih8+b8ofGyK8HpeZ262Zscu9+VQzHpuZgM3HRcoBX6Y90sLrmP99vRsTntmIzaZISQ7gjSk9EYHHn9/ARSMTSU/1Z8pTfb0eT+d+hQy/Npt9O/x4a3kcAHOnNGbDz+a08amK+vpYbVHK5EcB/00q3I4J7izDE0qpHzzfPQDc5fmuALhZKbVXRBbgbquwWCk1QUReBq4C9nt+961S6v2Kt2OKyPvAuUASkFv+m5OVK0QiVH8ZZuzEViA2c+uCyuRHR5/Jj9UWP3Nv+3JlZ5/+R17Qj9U+Nf1Y7ZOztWxmanzHvgTTYq9XP5Gnskw5uod2iFaDZ19vaMxFF8zc6GlX97eptxkHpdRJtxSl1HRgeiXDbzzh86PAo5X8bkiF97d7U05N0zSt/tIdQGmapmmaVjVK346paZqmadpZTmccNE3TNM0EugMoTdM0TdPOejrjoGmapmkmqY8ZB11x0DRN0zQT1Nd+HHTFoZ4x+155sZp7P7grKdm02Ltr0DFUdXR45sQ++I1l9rJVRTV7smhVmd1PhCszy9T4ZzSXueuOK+UM7uNCqzZdcdA0TdM0k6h6mHHQjSM1TdM0TasynXHQNE3TNJPoniM1TdM0TasSpXuO1DRN0zTtbKczDpqmaZpmEt04UtM0TdO0s5rOONRhfYbkMfaFZKwWxeKPI/hsVrSh8S0WxczFu8lMsfP0ba0NjX3Vnalc8q8MlIKEXf5MG9+SspKa11MfmrqP/kNzyMm0M/aSrsd9d/Vdhxk9KYnrevUkL9te5ZhS5qLpGzsQhwKnoqBnBFkjmxL6SwphK1LwyShh70u9cAUdi+m/O4+oLw+AU+EMsnHowU5VGpfdx8nL72/G7uPCalWsXt6Qj95qxYSXdnBOpzwcDgu7twcz8/n2OB3e1+fNXLZGx69s2d41MZH+w3JwlAnJB/x4bUIrCvON2V2N+ncql/4rAxFY/HEk37xr7HZl5nb78GuJ9B+eT06GjTFD2xsW16z4D02Jp9/QbHIy7dz9jx4APD59N01bufsMCQpxUpBn5d4runs9LjB/n1l99bMDqDM+4yAijUTkExHZKyIbRWSRiLQTke21XI5nRWS8UfEsFsW4yYd48qZW/N+Q9lw4Kofm5xQbFR6AK+9KJ2mPr6ExARpElzLqjjTuG9mRsRd1xmKFIZd71znP8i8jefL2v+7IIhuX0Pu8XFIPVb9zIWUTDt7fkcSJXUmc2IXAHTn47c+nuHUwh+7rSFnE8TEtRxxEfbaf5DHtSHyyGyl3nlPlcZWVWph4Zw/uvaYf917blz6DMmnfLZcVP0Qz+or+3HN1X3x8XYy4+nC1p6MyZi1bM+JXtmw3rQ5lzIiu3H1pVw7t9+P6e4yZLy3aFXHpvzJ44PKO3D2iE/2H5dK4hXHbldnb7bJPI5h0UyvD4pkdf/lXDXny3x2PG/bSA+2494ru3HtFd1YvjeC3ZRGGjKs29pk1oZQY+qoLzuiKg4gI8DWwUinVRinVG5gIGFLNFLe/ZR6173mE5AQfUhJ9cZRZWLkwjIEjcg2LH9m4lH7D8lj8cQPDYlZktSl8/FxYrApffxeZqd71Grj99xDyc/56xjnmqUTmvNTc/Ri66hJB+bp7whSnO+ugRChpFoijwV8PisGxmRR2j8AR4f7OGVz17AYIxUXu8ttsCqtNgYLYVQ0AAYTd20OIjC6pwYQcz+xla3T8ypbtplWhuJzuneSuzYFENio1ZFzNzykmbnMgJcUWXE5h27pgBl2aY0hsMH+73b4+iPxs8xLFRsffvqHy7dZNcf4/Mln5XaQh4zJ73mvHnNEVB+BCoEwp9U75AKXUH0BS+WcRsYrIKyKyQUS2isgYz/AgEflJRDaJyDYRGeUZ3lJE4kTkA2A70ExEJlT4++cqxJ4kIrtFZDVgaN6wQaMy0pOPHWwzDtuJbFxmWPyxzx1izotNUC7DQh6VmerDF7Ojmb9uGwtit1KYZ2XTqhDDxzPgomwyU3zYvzOg5kFciuZTttH68U0c6RBKScugk/7UnlaM5YiDmDd20GzqNoLXp1drVBaLYubnG1jwyxo2r4sgblvo0e+sNhdDL0th4xrvz77MXLa1Ef9EF1+XQewvoaf/YRUkxPnRuV8BwWEOfP1c9L0wl6jGxlRKwPzttj7p0jef7Aw7yQf8DYlXF+d9+WO1jXxVlefYt1lEvvd8biUi60UkXkQ+FREfz3Bfz+d4z/ctTxf7TK84dAE2nuY3dwK5Sqm+QF/g/0SkFVAMXKWU6oW7AjLNk8EAOAd4SynVGXeF4BygH9AD6C0i54tIb+AGz7B/eGKfEfoPzyUnw0b8Ni8OuKcQFOpg4EW53D6oCzf17YZfgJOhV2UaOg5fPyc33JPMB6/HeBfIIiRO7Mr+F3vid6AAn+QjJ/2puBS+SYUk392eQ+M6ELHkEPbUqj/fweUS7ru2L7cOH0i7Lnm0aFtw9Ltxk3azfWMYf24K82ZqTF+2Zsc/0Q3jknE6hJ+/MSa7kRTvz+dvN2LyR3t4cf4e9u7wx+WqG+nfs82QyzL45Xtjsg1apR4Adlb4PBV4XSnVFsjGfWzE83+2Z/jrnt+d0plecaiKi4FbRWQLsB5ogLsiIMBkEdkK/AjEcOwSxwGl1LoKf38xsBnYBHTw/P15wNdKqSNKqTzg25MVQERGi0isiMSWUbVUdGaKnagmx86EIhuXkXG4Oqnxk+vUp5ABF+cxb92fTHzrAN0H5fPojAOGxAboOTif1CQfcrPsOB3CmiXhdOxdaFh8gMYtSmjUtIS3F21n3qotRDYqZdZ3fxIeWbOzR1eAjaJ2IQTsOHlq0xHmw5GOYShfK64gO0VtQ/A9dPKKxskU5tvZuiGM3oPc7T5uHLuf0Igy/vdK2xqVvSKzl63Z8Su66J/p9B+azcsPtgYDe99b+mkk943syIRr21OQa+PQPuPagpi53dYnFqvi3BFZ/PqDcZfT6uS8V+5OoIx8VYWINAVGAnM8nwUYCnzh+ck84ErP+1Gez3i+H1bhJLpSZ3rF4U+g92l+I8B9SqkenlcrpdQy4CYgCuitlOoBpAJ+nr8pPOHvp1T4+7ZKqXerU0il1GylVB+lVB87VdtJxW0JIKZVKdHNSrDZXQwZlcO6Zcaka+e+1ISb+3TmtgGdmXJPC/5YE8zL97cwJDZA2iEfOvQqxNfPBSh6DMojKd7vtH9XHQlxAdzQtxe3ndeD287rQUaKD/de3pnsjKq3pbDml2E54gBASl0E7MqjLPrk5SzoFo7/3nxwKqTUiV9CAaWNqpZmDQkvJTDYnTb18XXSc0A2B/cHMOLqZHoNymLqo50Mafhk9rI1O3653ufncM2Ywzz7f+0oKTb2iayhDdzLIapJKYMuyWbFQmMa54G522190nNQDgf3+ZGRYlylTc/747wBPAqUX1BsAOQopRyezwdxnyzj+T8JwPN9ruf3J3Wm3475M+6swWil1GwAEekGVFxblgJ3i8jPSqkyEWkHHPL8Js0z7ELgZHu/pcALIvKRUqpARGKAMuBX4H0RmYJ7Pl4O/NeoCXM5hTcnxTB5wT4sVlj2SQQHdht78DVL3JZAVi0KZ9aiHTidwt4/A1i8wLuU5OPT4+k2IJ+QcAfzf9vMh280ZelnUV7FtOaVET1/L+JyN1Qs6BVBYddwQlemEP5jMra8MlpM3kZh5zDSbmpNWSN/CjuF0nzKVhAh79woSptULWUfEVXKIy/uxGJViMCqZVH8/msk321eSdphX6Z9uAmA336K5ON3zGs1XxdVtmyvvzsZu49i8vw4wN1AcuaTxsyXp/67j+BwB84y4c2nmlOYZ9xu0Ozt9vG3DtBtYAGhEQ4+jN3B/GnRLDWwEazR8R97fTfd+ue5l+3qjcyf3pRln0dzwUjjGkWWq6v7TBOeVREpIrEVPs8uP/4BiMhluI9tG0VkiNEjBxBV1dxHHSUiTXDXrnrjbreQADyI+zJCF89dES/iPrALkI47RWMHvgOCgFhgAHCpJ+z3SqkuFcbxAHCX52MBcLNSaq+ITAJuA9KARGCTUurVU5U3RCJUfxnm1TSf0qkzTN6Htxp79vcXJsbfPa2HabEBOjyzx9T4zqxsU+ObTXy8u7PmtJxOU8Mrh+P0PzpLWQLMbfPiOlL9S4JVtV79RJ7KMmXHGXBOY9Vh+p2n/2E1bB75n41KqT4n+95zMnsL4MCdRQ/BfffhCKCRUsohIgOBZ5VSI0Rkqef9WhGxASlAlDpF5eBMzziglEoGrqvkqy6e713AE57XiQaeJGyXih+UUtOB6ZWM+z/Af6pTXk3TNE0zi1JqIu5uCfBkHMYrpW4Skc+Ba4BPcJ/wLvT8ybeez2s93/98qkoD1IOKg6ZpmqbVTXWq58jHgE9E5EXcjf3L2+q9C8wXkXggC/fdgqekKw6apmmaVg8ppVYCKz3v9+HuVuDE3xQD11Ynrq44aJqmaZpJzvBmhJXSFQdN0zRNM0ldeb6Ekc70fhw0TdM0TatFOuOgaZqmaSZw9/ZY/zIOuuJQz1h8zXuUMph/L/tpejr1SvvHzH3SettfjHtYUmXihwabGt9ZYGy34CcSm7m7G1eZ7mfh72KJMudJrOVcB8zrx0GrPl1x0DRN0zST1KHbMQ2jKw6apmmaZpL6eFeFbhypaZqmaVqV6YyDpmmappmkPjaO1BkHTdM0TdOqTGccNE3TNM0ECtEZB03TNE3Tzm4641CH9RmSx9gXkrFaFIs/juCzWdFexXto6j76XZhNTqaduy/tdnT4FbemcNktqbicwu8rwnhvanNvi85Vd6Zyyb8yUAoSdvkzbXxLykpqXk+trOw3PXCQS65PIzfLDsC8V5uxYWVYjeJHNiph/Ct7CI8sQylY/Gk0C+c1YfAlGdx8fxLN2hTx4D+7sWd7ULXiKqfiwC0ObA2Fpm/YUEqR8ZaL/J9ciAXCrrEQfoMVZ54i5XknpQcVFh+h0dNWfNtW7UwlslEJj0yNI7xBKUoJSz5rxML5MUe/v+qOg/zfY/u5YcAA8nLs1Sp/ZQJDHDz0SiIt2xehFLz2SAt2bqrefDmx/JXN+6DQMiZO3010TAmph3yZcn97CvK832XNW7udokILLqfgdAj3jezgdcyKjN5uK3r4tUT6D88nJ8PGmKHtDYtrZvz3vvqJoiM29/x2Cg/++zxuHh3HgPNSUC4hJ9uH11/sQVaGn9fjMnPe11Q9vKni7Kg4iIgT2FZh0JVKqYS/qThVYrEoxk0+xMQbWpNx2M7MRXtYtzSUxD0137iWfxHJtx9EM/7VvUeHdRuQy4CLshk3sitlpRZCG5R5XfYG0aWMuiON0cM6U1pi4Ym39jHk8iyWfxFpaNkBvnmvMV/OaextkXE6hf9NacneHUH4BzqZ8fUfbF4TxoE9AbwwrgP3v7D39EEqkf2xC59WgsvTt1LedwpHqqLVFzbEIjiy3LuVzLkufNsJMa/aKElQpE110uztqm2eTqcwZ2prT9kdzPhyC5t+CyNpbyCRjUroNSibtEPGdQx293MHiV0ZwotjWmOzu/D1d3kV72TzfvjVaWz5LZTPZzfl2tEHuW7MQd57paUh0/Dote3IyzZ+92fGdlvRsk8j+HZuJBOmJxkSr7biTxw3kLxcn6Ofv/ywNR/OdldMLr92P//6927efLnbyf68Ssye9zVST3uOPFsuVRQppXpUeCX83QU6nfY9j5Cc4ENKoi+OMgsrF4YxcESuVzG3bwghP+f4neXIm9L47J0mlJW6V4XcTO/PSAGsNoWPnwuLVeHr7yIz1ef0f3QKlZXdSNnpPuzd4T5rLiq0krTXnwbRpSTtDeDQfv8axSxLVRSuUYReeWwzy/nCRYP/syIW987EFuH+v3SfIqCv+71vS6EsWeHIrNq5yvFlt5G415/IaHcvlqMn7uW9V1oZdtYTEOyka/8Clnzs7inQUWah0MsswMnm/cBhWfz4dUMAfvy6IQOHZ3lX+FpgxnZb0fb1QeSbUOGprfjlio4c28/4+TsNObiaPe+1Y86WisNxRCRIRH4SkU0isk1ERlX47ikRiROR1SLysYiM9wxfKSJ9PO8jRSTB876ziPwuIltEZKuInGNEGRs0KiM9+djBNuOwncjG3mcDThTTqpguffN5/avtvPzxDtp1K/A6ZmaqD1/Mjmb+um0siN1KYZ6VTatCDCjtX11+awpvLdrKQ1P3ERRiTJfDDWOKadOpkLg/ap5+B0ib5iTqfisVe9EuPaTIX+Yi4RYHB+93UJroPqT7thMKfna/L9ruoiwFHGk1LHvHQnb9EcyAoZlkpvqyP8676aioUbMScrNsPPLaAd5cspMHXzmAr7/TsPgV531YZBnZ6e5tIDvdTlikQeu/gskL9jBr0U4uvSnDmJgetbXdnkmUghemr2P63FVcMurA0eG3jtnF+9/8yJCLD/Hh/9p5PZ46O++Vwa864GypOPh7DuxbRORroBi4SinVC7gQmCZufYF/At2BS4E+VYg9FpiulOrh+f1BU6bAJFarIjjUwUNXd2bOlOZMnBmPt2tnUKiDgRflcvugLtzUtxt+AU6GXpVpTIEr+OGjaP49pAfjRnYlK83O/01K9DqmX4CTJ2fF8d//tOJIQc3PvApWubBFCH4djz+TUqUgvtByvo3QKy2kPO8+6EbcZsFZoEi4sYycT134tZdqb51+AU4mzdjJ7CmtcTmF68ckMX9GixpPQ2WsNkXbLkf4fn4U4y7pSPERC9ePSzUk9qnnvRjWA9/DV7fj3ks7MumWtlxxWzpd+ucbE1ir1KNjz+WB28/n6Yf7MfKfCXTu4d4XfPDfDtx+5XBWLovh8msS/t5CatVytlQcKl6quAoQYLKIbAV+BGKAaGAQsFApVayUyge+q0LstcATIvIY0EIpVXTiD0RktIjEikhsGSVVKnBmip2oJscemhTZuIyMw8ZcRqgoI8WHNUvDAWH31iCUC0IjvDtz7zk4n9QkH3Kz7Dgdwpol4XTsbfwDlHIy7Lhc7tudFn/S0OtsidXm4slZcaz4Norflnn30J6iPxQFv7rYe3kZyZOcHNmgSH7Kgb0hBF3o3uyCLhRK9riPhtYgofEzNlousNPoeSuObIU95lRj+GvZJ83YwcrvovhteSSNmxcT3bSYNxduYu5PvxMZXcKMrzYTHundg7gyDvuQftiHuM2BAKz+IZy2Xb1/AFFl8z4nw054lLu84VGlhl1Gy0xxn5XmZtpZsySUDj2Me4BSbW23Z5LMdPelvtxsX9b+0oj2nXKO+37l0hjOHZLi/Xjq6LxXSgx91QVnS8XhRDcBUUBvT6YgFThdCxoHx+bX0d8qpRYAVwBFwCIRGXriHyqlZiul+iil+tipWiO1uC0BxLQqJbpZCTa7iyGjcli3LLRKf1sda5eH032A+4wrplURNrsiN8u7a5xph3zo0KsQXz8XoOgxKI+keOMbKJUfVADOHZHFgd01a4vgpnhw8l6S9vrz9dwmXpct6l4rbRbZafOdnSb/sRLQV2jygo2gIRaKYj2XJDYqfFq4dwTOfIUqcw/P/UYR0FOwBlV1J6F48MU9JO0N4Ov3mwKQsDuQGwcN4I5h/bhjWD8yUn25/+qeZGd419YkO91ORrKdpq2LAegxOM+AxmeVz/t1P0cw/Cr39ZrhV6Wx9qcIL8cDvv5O/AOdR9/3Pj+fhDjj1s3a2m7PFL5+DvwDHEff9+qfwYF9wTRpeqySP+C8FA4eCPR6XHV13rsfrW3cqy44K+6qqEQokKaUKhORC4HyfO4a4L8iMgX3vLkMmO35LgHoDfwOXFMeSERaA/uUUjNEpDnQDfjZ2wK6nMKbk2KYvGAfFiss+ySCA7u928E9Nj2ebv3zCAl3MH/NJuZPb8qyz6N4aOo+3l68FUeZMG1Ca9wJmZqL2xLIqkXhzFq0A6dT2PtnAIsX1PyOipOVvVv/PFp3OgIKUg/6MmNSqxrH79w7n+FXpbN/VwCzvt0CwLxpLbD7uLj76f2ERpTx3P92sm9nIE/+u1ONxxNxu4XDTzrJWuDEEiBEP2kFoHS/4vCzngNaG6HRU9Yqx+zUK49hV6axPy6AmV9vcpf99ZbE/ur9gbYybz7VjMdmJmDzcZFywJdpj3h3OeRk8/6z/8bwxPTdjLg2jbRDvkx+wPvr4OFRDp6Zsw9wX6Zb8U04sSuNO7iYsd1W9PhbB+g2sIDQCAcfxu5g/rRoln5s3COtjY4fHlHCpJdiAff8/mVZDBvXNeSJybHENC9EKUhL8efNl7t6XXaz5712jKi6UoUxkYgUKKWCKnyOxH0ZIgiIBQYAlyqlEkTkWeBG3FmINGCJUup/ItIB+AxwAj8ANyulWorI48AtQBmQAtyolDpp8+8QiVD9ZZgZkwmAxc/cDUU5jGmAeDJiM7Eua636wbgmzvnFu8sApxM/1JuMyuk5C4y/nFSRxd/cddNVVGxqfFzGNQKtb2wtmpka33HAnNtPAdarn8hTWaZcA/BtE6OaTr7H0Jj7bnhyo1KqKu3vTHNWZBwqVho8nzOAgSf5+atKqWdFJAD4Fdjo+ZtduLMJ5Z70DH8JeMnwQmuapmlaHXRWVByqabaIdMLdjmGeUmrT310gTdM07QykgDrSoNFIuuJwAqXUjX93GTRN07T6oT62Bjhb76rQNE3TNK0GdMZB0zRN08yiMw6apmmapp3NdMZB0zRN00xRd3p7NJKuONQzEhxsanyVnW1qfFepmQ+lMfeBN/G3tDY1vjQwt58IOWJc18uV0f0snIKYfHAxu4We4wye91q16YqDpmmappmlHrZx0BUHTdM0TTODol5eqtCNIzVN0zRNqzKdcdA0TdM0s9TDSxU646BpmqZpWpXpjIOmaZqmmab+tXHQFQdN0zRNM0s9vFShKw51WJ8heYx9IRmrRbH44wg+mxVd41h2Hycvz92I3e7CalOsXt6Qj95uAyhuvXcv512chtMJiz5vyrcLmntd9qvuTOWSf2WgFCTs8mfa+JaUlRh3ZWze2u0UFVpwOQWnQ7hvZAfDYgMEhjh46JVEWrYvQil47ZEW7NwUdPo/PImYpvk8/tS6o58bNy5k/vudCQkpYcCgw7hckJvjy2sv9yUr07/a8d/7YjlFR2y4XILTKTx45wW0apvLuAlb8fd3kHo4gFee60XREXuNp6Gc2cvW6HlfUVSTUiZMTyQsygEKFn3YgG/ejTIkdjkjt9vKWCyKmYt3k5li5+nbjO07xKyyWyyKNz5YQ2aaH8893AdQ3Hr3HgYPO4zLJfzwZXO++7SlV+OojWWruZ1xFQcRcQLbKgy6UimV4GXMRcCNSqkcb+IYyWJRjJt8iIk3tCbjsJ2Zi/awbmkoiXv8ahSvrNTCxLt6UVxkw2pz8er7scSujqR560KiGhUzetRAlBJCI7zvZKhBdCmj7khj9LDOlJZYeOKtfQy5PIvlX0R6HbuiR69tR162Oavw3c8dJHZlCC+OaY3N7sLX3+VVvEMHg7lvzEWAe9l+8On3rF3dhPwCH+a/3wWAK67aw4237GTWG71qNI6J951LXq7v0c/3P/4H787qxPYtkVw0MpF/3rSXD//nXQWrNpat0fO+IqdDmP18E+K3BeAf6GTWkt1s+jW4xtvViYzebitz5V3pJO3xJSDYuPkC5pb9ihsSSNofRECgA4Dhlx8iMrqIMdee797vhJd4PQ6zl22N1cOMw5nYOLJIKdWjwiuh/Atxq/Y0KaX+UZcqDQDtex4hOcGHlERfHGUWVi4MY+CIXC8iCsVF7oOszaaw2txr8z+uO8iC/7Y+eq9xbpaPt0UHwGpT+Pi5sFgVvv4uMlONiVsbAoKddO1fwJKPGwDgKLNQmGdcBaV7z1RSkoNISws8LgPg5+c0tIO/mGYFbN/inobNG6IYdEGyIXHNXLZmz/usNDvx2wIAKCq0khTvR2Rj43oUNX67PV5k41L6DctjsWf+GMmssjdoWETfweksXdjs6LB//DORj+e0PbbfyfY92Z9XmdnLVjvmjMs4nEhEWgJLgfVAb+AfIvI40BfwB75QSj0jIqHA78AVSqk4EfkY+Fkp9T8RSQD6AEHAYmA1cC5wCBillCoSkZXAeKVUrIhEArFKqZYi0hmYC/jgroj9Uym1x9vpatCojPTkYzvkjMN2OvTyrktgi0Ux/eP1NGlexPefNiVuWyiNmxZx/ohUzh2aRm62D+9MbU9yYoBX48lM9eGL2dHMX7eNkmILm34NYdOqEK9i/oWCyQv2gIIfPopi8UfGnfE2alZCbpaNR147QOtORezZFsDbTzelpMhqSPwLLjzIyp+P7URv/fd2hl10gMJCO48/ckGNYiolvPD6OlCweGELlnzbksT9wQw4L4V1qxoz+MJkIqOLvC672cvW7HlfUXTTUtp0KWLXJu/W94rM2G4rGvvcIea82ISAIOO7eDar7KMf3sncGe3xD3AcHdY45gjnX3SYgUNSyc3x4b+vdiI5KdDrcZUzY9nWiAJ0B1B1gr+IbPG8vvYMOwd4SynVWSl1AJiklOoDdAMuEJFuSqlc4F7gfRG5AQhXSv2vkvjnAG8qpToDOcA/T1OescB0pVQP3JWPgyf+QERGi0isiMSW4X1KrqZcLuG+6wdw68WDadcljxZtC7D7uCgttfDAjf1Z8lUMDz63w+vxBIU6GHhRLrcP6sJNfbvhF+Bk6FWZBkzBMQ9f3Y57L+3IpFvacsVt6XTpn29YbKtN0bbLEb6fH8W4SzpSfMTC9eNSDYlts7nof24yq39tenTYB+914bZ/jWTlT825/Mr4GsV99O5BPPDvC3j6kQGMvDqBzt0zeWNyD0ZencD0d3/BP8CBo8z7zd3sZWvmvK/IL8DJU3MSeOfpJhwpML5SYob+w3PJybAdPas+E/QdnEZuti/xu0KPG+7e71h58LZBLP2mGQ88te0kEaqvri1bpYx91QVnYsWh4qWKqzzDDiil1lX4zXUisgnYDHQGOgEopZbjbh/xJnDXSeLvV0pt8bzfCLQ8TXnWAk+IyGNAC6XUX07rlFKzlVJ9lFJ97FQtJZeZYieqybH2BpGNy8g47H3DNoDCfDtbN4TT+9xMMlJ9+e2nhgD89lMUrc7x/gDcc3A+qUk+5GbZcTqENUvC6di70Ou4FWWmuM+McjPtrFkSSocexp3VZRz2If2wD3Gb3WdAq38Ip21XY+L36ZfC3j1h5GT/9brrip+aM+i8QzWKm5nhblCZm+PL2l8b0b5TNgcTg3nqoYE8cOcF/PJjDIcPeX9GZ/ayNXPel7PaFE/NSeDnr8JZszjM0Nhmbred+hQy4OI85q37k4lvHaD7oHwenXHAkNhgTtk7dc+m/3mpvLdwJY9N3kK3vpmMf/4PMtL8+G2Fu+HlbyuiDdnvgLnLVjvmpBUHEZkpIjNO9qrNQlbB0T2XiLQCxgPDlFLdgB8AP893FqAjcAQIP0msiikBJ8cu5zg4Nr+O7vWVUguAK4AiYJGIDPV2YgDitgQQ06qU6GYl2OwuhozKYd2y0NP/4UmEhJcSGOy+3ufj66TngCwOJgSwdkUU3fpmAdC1TzaHDnh/cEk75EOHXoX4+rkARY9BeSTFG9dAydffiX+g8+j73ufnkxBnXPzsdDsZyXaatnY/zbHH4DzDGlhdMDSRX34+dtdKk5hjO8wB5yZzMKn6Tzf19XMcTQP7+jno1S+dA/tCCA1zr8oiihtu283ib1p6V3jMX7Zmzns3xcPTkkja48dXs41vcW/0dlvR3JeacHOfztw2oDNT7mnBH2uCefn+FobEBnPKPu/N9tx22VD+PWoIU5/owdYNDXj16e6s+yWabr3dmaquvbI4lGjEZQpzl22NKYNfdcCp2jjE1lopjBWCuyKRKyLRwKXASs93DwE7gSeAuSIyUClV1dYzCbjbUPwOXFM+UERaA/uUUjNEpDnuyyM/ezsRLqfw5qQYJi/Yh8UKyz6J4MDumu9AIyJLeOTFP7FYQCyKVcui+f3XKP7cHMaEydu56uZEio7YmP5cR2+LTtyWQFYtCmfWoh04ncLePwNYvMC4NgjhUQ6embMPAKtVseKbcGJXGrNzLvfmU814bGYCNh8XKQd8mfaI9ztoXz8HPXunMfP13keH3XHXdmKa5aOUkJYaUKM7KsIjSpg0eQPgPuP6ZVkMG9c35Ipr93HZ1fsB+O2Xxiz/odmpwlSJ2csWzJn35Tr3K2T4tdns2+HHW8vjAJg7pTEbfjamnYbR221tqs2yf/5+aya88AdX3phA0REbM17s4nVMs5etdoyoKl40EZEApZSxOcMaEJECpVRQhc8tge+VUl0qDHsfd+PGJCAX+Bb3JYVvgH5KqXwReQ3I9zScTOBY48ijsURkPBCklHpWRDoAn+HOQvwA3OxpHPk4cAtQBqTgvq0z62TlD5EI1V+GGTIvKmONMrem7crONjW+ctWRKnUNWNsbe0/9iaTY+1tlT8WZVLPLJFVl+rJ1Gd9gsNaIyQ3oTL44botpYmp8xyFj7giqzHr1E3kqy5QF4NuyqWr05AOGxkz8v0c3etrw/W1Oe1eFiAwE3sV9UG0uIt2BMUqpe8wuXGUqVho8nxOALicMu/0kf96xwm8ervC+pedtRsVYSqlXK7zfhTubUO5Jz/CXgJeqPgWapmmaduaqSuPIN4ARQCaAUuoP4HwTy6RpmqZp9YIoY191QZX6cVBKJcnxqbQzOCeoaZqmabWgDjVoNFJVKg5JInIuoETEDjyAu4GhpmmapmlnmapUHMYC04EYIBl3L43jzCyUpmmapp35pF72HHnaioNSKgO4qRbKommapmlaHXfaxpEi0lpEvhORdBFJE5GFnr4LNE3TNE07lbOsA6hyC3B30VzevfMNwMdAf7MKVe+ZeM+2Mz3dtNgAYjf3KZdiZtfyytjHEP8l/P4kU+M7S8x9zoljaM0e511Vvlv2mxqfBifrDNYYzt17zQtucj8LZm+3qtDYLuXrlTpysDdSVW7HDFBKzVdKOTyvD6nQ5bKmaZqmaWePk2YcRCTC83axp3fET3DXna4HFtVC2TRN0zTtzFYPMw6nulSxEfckl+fVx1T4TgETzSqUpmmapml100krDkqpVrVZEE3TNE2rVxRn5+2YACLSBejE8Y+T/sCsQmmapmlafVBXuok2UlUecvUMMAR3xWER7sdUrwZ0xUHTNE3TzjJVuaviGmAYkKKUugPoDoSaWipN0zRNqw/O0n4cipRSLhFxiEgIkAY0M7lcZz27r4tpX8Zj93VhtcKqH0KZP62xIbGjmpQyYXoiYVEOULDowwZ8826U13EfemU//YfmkJNpZ+zF7qeTB4U6eOLNvUQ3LSH1oC+T72lDQV6VrpCdNvZ5/8ji5ocO0axtMQ9c0Yk92wK9noZyV92ZyiX/ykApSNjlz7TxLSkrqUo9u3IPTd1Hvwuzycm0c/elx57OfsWtKVx2Syoup/D7ijDem9rc67Ibse6Mv2sVA3omkZPnx10TrwZg9A2/M7BnEg6HheS0YF7+33kUHvE9+jcNGxTw3ktfMe/rnny+qGvVy+vj5OX3N2P3cWG1KlYvb8hHb7Viwks7OKdTHg6Hhd3bg5n5fHucjpotg8CgUh6YsIkWrfJQSnhjai+uvCaemOYFAAQFlVFQYOe+u4bVKH5FfYbkMfaFZKwWxeKPI/hsVrTXMc2Mbea2FdmomEemxBEeWYZSsOSzxiz8MIbWHQq495k92H1duBzCmy+0Zfe2EK+m4+HXEuk/PJ+cDBtjhrb3KpZ2alXZCmNFJAz4H+47LTYBa80sVG0QkQIv/raPiMwwsjwnKisRHr2uDXdf1IG7L25PnyH5dOhlTCcrTocw+/kmjB7SgQcuO4fLb8+g+TnFXsdd/nkkT97W7rhh199zmC1rQrhzSDe2rAnhunsOGxY7Ybc/L4xpy/b1wTUuc2UaRJcy6o407hvZkbEXdcZihSGXZ3kVc/kXkTx5R4fjhnUbkMuAi7IZN7IrYy/pxpdzjKkYGrHuLF11DhNfvvi4YRu3x3DnxKv4v0lXcTAllBsv33rc93ff+Du/b21a/fKWWph4Zw/uvaYf917blz6DMmnfLZcVP0Qz+or+3HN1X3x8XYy4umbrDsCYe7ey8fdoxtx6MffeOYykxGBeer4/9901jPvuGsaaX5rw269Nahy/nMWiGDf5EE/e1Ir/G9KeC0flGLJtmRnbzG3L6RDmvNyasZf34eEbenDZjck0a1PIvx/Zx4K3WnDf1b2ZP6sl/37E+87Bln0awaSbdJv+2nDaioNS6h6lVI5S6h3gIuA2zyWLs5KI2JRSsUqp+00eE8VH3N0o2mwKq10Z1rlcVpqd+G0BABQVWkmK9yOycZnXcbf/Hkx+zvHZhIEX5fDjlw0A+PHLBpx7cY5hsZPi/Tm4z79G8U7HalP4+LmwWBW+/i4yU73reW/7hpC/lH/kTWl89k4Tykrdm2Fupt2rcRzj/bqzLa4ReYW+xw3buD0Gl8td1h3xUURGHKuMDOp9gMPpQSQcDKtZeYtsx8prU6AgdlUD3HeDC7u3hxAZXbOeMwMCy+jSPYOlP7QEwOGwUFhQcXkqzrvwEL/85H0itX3PIyQn+JCS6IujzMLKhWEMHJHrdVwzY5u5bWVn+LJ3p7vyUXTERuK+ACIblqKUEBDoACAwyEFWmvc9W25fH0R+dvWzmVr1nbTiICK9TnwBEYDN877eEZHLRWS9iGwWkR9FJNoz/FkRmS8ia4D5IjJERL73fDddRJ72vB8hIr+KSM1z2hVYLIq3lu3i063b2fxrMHGbjUvFl4tuWkqbLkXs2hRgeGyAsMiyozuFrDQ7YZHeV1DMlpnqwxezo5m/bhsLYrdSmGdl0yrv0qiViWlVTJe++bz+1XZe/ngH7brVOAn2F2avO5desIcNf7izC36+ZdwwcisffN2zxvEsFsXMzzew4Jc1bF4XQdy2Y82orDYXQy9LYeOaiFNEOLlGjQvJzfHlocc3MvN/P/HAhI34+jmOft+lWyY52b4kHwqqcfnLNWhURnrysYNgxmG7IZVys2PXhoZNimnTsYBdW4OZ/VIb/j1hP/N+WsedE/bx/hv1N1MgytjXaccn4iciv4vIHyLyp4g85xneynN8ixeRT0XExzPc1/M53vN9y9ON41QHuGmneL16+uKfkVYDA5RSPXH3lPlohe86AcOVUv864W8mAteLyIXADOAOpY5/KIKIjBaRWBGJLaPqZ00ul3DPxR24qU8n2vc8Qov2RTWZppPyC3Dy1JwE3nm6CUcKzHxIRDmpK217Tiko1MHAi3K5fVAXburbDb8AJ0OvyjR8PFarIjjUwUNXd2bOlOZMnBmPUa2fzFx3brxiC06n8ONvbQC47erNfLGkM8UlNc+YuFzCfdf25dbhA2nXJY8WbY9VosZN2s32jWH8uSmsRrGtVkXbdjksWtia+/5vGMVFNq67Me7o9xcMS2KlAdkG7eT8ApxMmr6D2VPaUFRo4x83JPO/l1pz27AB/G9qGx54YfffXUTzKDH2dXolwFClVHegB3CJiAwApgKvK6XaAtnAnZ7f3wlke4a/7vndKZ204qCUuvAUr6FVKf0ZqCmwVES2AROAzhW++1Yp9Ze9r1LqCPB/wHJgllLqL0/CUUrNVkr1UUr1seN74tenVZhn4481QfQdkl/tvz0Zq03x1JwEfv4qnDWLwwyLe6KcDDsRDUsBiGhYSm6GUel48/QcnE9qkg+5WXacDmHNknA69jb+IT4ZKT6sWRoOCLu3BqFcEBrhOO3fVYfR686I8/YwsEcSk98eQnmnsh3bpDP6hlg+eu0z/jliBzde/gejhu+oWXnz7WzdEEbvQe42JTeO3U9oRBn/e6Vtjcucke5PRro/cTvdGYvVv8TQ5pwcACxWF+eel8yvK2JqHL+izBQ7UU1Kj36ObFxGxmFj1nkzY5vJanMx6Y0drPy+Ib/9GAnA8FGprFnufr9qSSTtuxq3bzvbKbfymrfd81LAUOALz/B5wJWe96M8n/F8P0zk1E9iNCSlXo/MxH3w74q7i+2KD/M61ZGjK5AJeN+6yiM0wkFgiPsg4uPnotf5+STtrX6lo3KKh6clkbTHj69me383xams+zGM4f90n60P/2cma5eHmTo+I6Qd8qFDr0J8/VyAosegPJLijX+u29rl4XQf4N5hxrQqwmZX5GZ5f43WrHWnb9eDXD9yG0++PpyS0mPlfPDFkdz08HXc9PB1fLm0Ewu+687CHztVOW5IeCmBwe6Uu4+vk54Dsjm4P4ARVyfTa1AWUx/thPKi973sLD/S0/yJaeae1z16p5F4wH3pqWfvNA4mBpOZbsylurgtAcS0KiW6WQk2u4sho3JYt8yYu9fNjG0exYMv7CZpXwBfzzvWcDYzzYeufd3tM7oPyOHQAXPaKv3tjL4V052QjCzPYHteo08crYhYRWQL7rsglwN7gRylVPmZyUGgvLYcAyQBeL7PBRqcarJ0S5LjhQKHPO9vq8ofiEgL4BGgJ7BIRL5RSq33tiAR0WWMfyMRi0VhscCv34Wx/kdjdhKd+xUy/Nps9u3w463l7pTt3CmN2fCzd9fxH5+xl24D8wkJdzB/3RY+fD2GT99qzBNvxTPi+nTSDvnyn3vaGBY7P8fG3c8dIDTCwfNzd7NvRwCTbvX+Nqy4LYGsWhTOrEU7cDqFvX8GsHhBpFcxH5seT7f+ee7yr9nE/OlNWfZ5FA9N3cfbi7fiKBOmTWjNsUfD1JwR686ke1bQvWMKoUHFfDL9E+Z91Yt/Xf4HdpuLlx9bCsDO+CjeeH+Q9+WNKuWRF3disSpEYNWyKH7/NZLvNq8k7bAv0z7cBMBvP0Xy8Ts1uxb+zozuPPrkBmw2FymHA3n9pd4AnD/0IL/8XP07QU7G5RTenBTD5AX7sFhh2ScRHNhtTKXTrNhmbludeuUxbFQa++MCmfnVRgDmvdGKGc+0Y8zEvVitirJSCzOfOcf76XjrAN0GFhAa4eDD2B3MnxbN0o9Pefw7U2Uopfqc6gdKKSfQw3NH5NdAh1P9vrpEmfwc+LpKRFxAcoVBr+Gulb2O+/rPz0BfpdQQEXkWKFBKver52yHAeOBy3LW5GUqpb0WkN/C+5+8qvU8qRCJUf8twMybJzeTlKXbvWz//bY5vemI4sZlbD3eV1OyugqpyDDW3zbPvFu9vuTulBuGmhnfu/stVyDOG2dutJdDcjIEzx5g7UyqzXv1Ensoy5YESvs2aqZiHHzI05v6HH9l4uopDRZ7G+0XAY0AjpZRDRAYCzyqlRojIUs/7tSJiA1KAKHWKykFVupwW4CagtVLqeRFp7hn571UteF2klDrZZZqFlfz22RM+rwRWej4OrzB8I+7LFpqmaZpW68+qEJEooEwplSMi/ri7UZgKrMDdE/QnuDPq5ce6bz2f13q+//lUlQao2qWKtwAX7oYVzwP5wJdA3+pOkKZpmqZppmoMzBMRK+52jJ8ppb4XkR3AJyLyIrAZeNfz+3dxdzMQD2QBN5xuBFWpOPRXSvUSkc0ASqns8vs/NU3TNE07hVrOOCiltuJuc3fi8H1Av0qGFwPXVmccVbmrosxTc1FwNA1i7sViTdM0TdPqpKpUHGbgbpXZUET+g7uTpMmmlkrTNE3T6oOz8emYSqmPRGQj7kdrC3ClUmqn6SXTNE3TtDNYVbuJPtNU5a6K5sAR4LuKw5RSiWYWTNM0TdO0uqcqjSN/wJ0gEdw9KbYC4ji+O2atOox5BlbllNO82IBymhvfTNZQ4x9UVZEqLT39j7wagbmnLrafN5kaP2hVzR5SVVUF/9TdFp+UxZRuCo5y5hn3gLZ6x4teT+uqqlyqOK5fAs+TMe8xrUSapmmaptVZ1e7qTim1SUT6m1EYTdM0TatXztI2Dg9X+GgBenF8V82apmmapp0lqpJxCK7w3oG7zcOX5hRH0zRN0+qPs+6uCk/HT8FKqfG1VB5N0zRNqz/qYcXhpM37RcTmeTSn98/N1TRN0zStXjhVxuF33O0ZtojIt8DnQGH5l0qpr0wu21kvMMTBQ68k0rJ9EUrBa4+0YOemIENi9xmSx9gXkrFaFIs/juCzWdGGxC1nZtkB5q3dTlGhBZdTcDqE+0Z697j5yEbFPDJlF+ENSlEKlnzehIUfNqVV+wLufXo3/gFOUpP9ePnRjhQVVv/x2ZGNShj/yh7CI8tQChZ/Gs3CeU0ICi1j4vTdRMeUkHrIlyn3t6cgz7vHc5u5bO2+LqZ9GY/d14XVCqt+CGX+tMY1jqecioK7crFEWQh8+djtskVvFFL6QzGhyxsAUPJNMaVfFYMFxF/wfzQQa6vqzSeLRTH9o/Vkpvny7AM9iW5SxOMvbSU4tIz4nSG8+mQXHA7vb5U2c/6bEfuhqfvoPzSHnEw7Yy9x30R318RE+g/LwVEmJB/w47UJrSjM9269bNq6mCfePvZo9UbNS5j/ahO+frehV3ErMnu/Vm1nawdQuPtuyMT9dMzy/hwUcEZWHETECWzDPe07cT9OtBNwq1Lq/r+zbCe6+7mDxK4M4cUxrbHZXfj6G/OIEItFMW7yISbe0JqMw3ZmLtrDuqWhJO7xMyQ+mFf2ih69th152d7tzMo5HcKcl9uwd2cw/gEOZny+kU1rw3ng+TjmvNKG7bFhXHTVYa75dxLzZ7aqfnyn8L8pLdm7Iwj/QCczvv6DzWvCGH51Glt+C+Xz2U25dvRBrhtzkPdeaVnj6TB72ZaVCI9e14biI1asNsVrX+9hw4oQdm0KrFG80s+Lsbawoo4c27s6djlQ+cevLz4X+eB7pXsaylaXUjzzCIGvVa9fjlE3JpK0P5CAQAcA/35gD19/1IJflzbi3kk7uPiqQyz6vFmNpqOcmfPfrNjLv4zkuw+iGT9t39Fhm1aH8t7LzXA5hX8/lsT19xzmvanezZuD+/y4Z0THo9PyUew21iwJ9SpmRbWxX9PcTlW9bui5o2I77gPtduBPz//ba6FsZilSSvVQSnUBSoGxSqnYulZpCAh20rV/AUs+dp9xOcosFHp5Jlqufc8jJCf4kJLoi6PMwsqFYQwckWtIbDC37GbJzvBl7053O+CiIzYS9wUQ2bCEmBZH2B7r3rltXhvOoIvSaxY/3Ye9O9wZl6JCK0l7/WkQXcrAYVn8+LX7jOvHrxsycHiWV9Nh9rIFofiIFQCbTWG1qxr3S+VKc1K2thSfy4/t2JVTUfxmIX53H18RkcBjuypVpNynL9XQoGExfQdnsPTrmPIodOubxeofPfP+uyYMHFKzZVuRmfPfrNjbfw8hP+f47XPTqlBcTvdM3rU5kMhGxnZu1mNwPocP+JJ2yNewmOav+zVUD59VcaqKgxUI8ryCK7wvf9UHq4C2IjJERL4HEJEgEZkrIttEZKuI/NMz/GIRWSsim0TkcxEJ8gx/WkQ2iMh2EZktIoZ0E9aoWQm5WTYeee0Aby7ZyYOvHMDX35heGxs0KiM9+diT0TMO24lsXGZIbDC37EcpmLxgD7MW7eTSmzIMDd2wSRFtOhawa2sIB+IDGTjUHf+8EelENirxPn5MMW06FRL3RxBhkWVkp7uXRXa6nbBI75aD2csW3Gd2by3bxadbt7P512DiNtcs21A04wj+dwceVwko/bIY+2AfLJF/3TWVfFlM/nXZFL99BL8HqzfOMRPieG/6Obg8iYyQsDIK8224nO7xZKT60aBhcY2moyIz539tLNvKXHxdBrG/GJcZABhyRTYrF4YbGvPvmj+ndZZVHA4rpZ5XSj1Xyev5WiuhSUTEBlyKO5tS0VNArlKqq1KqG/CziEQCTwLDlVK9gFigvH+LWUqpvp4Mhj9wmRHls9oUbbsc4fv5UYy7pCPFRyxcPy7ViNCmq42yP3x1O+69tCOTbmnLFbel06W/Md0N+wU4mPTGn8x+qS1FhTbeeKo9I29IZvpnsfgHOHGUeVcv9Atw8uSsOP77n1YcKTgxCyNm9yptCJdLuOfiDtzUpxPtex6hRfuiascoW1OKJUywdjg2D1wZLspWlOLzz8pTy77/9CP4s3D8xgZQMq/q4+x3Xjo5WT7E7zS3y/H66IZxyTgdws/fNDAsps3uYsDFOfz6vbEVB632nCp/XP862HbzF5EtnvergHeBcyt8Pxy4ofyDUipbRC7D3Q5ijSeh4AOs9fzkQhF5FAgAInBfzvmuQjxEZDQwGsCPgCoVMuOwD+mHfY6eza3+IZzrxqVUeSJPJTPFTlSTY6nHyMZlZBy2GxIbzC17ucwU95lFbqadNUtC6dDjCNvXB5/mr07NanMx6Y0/WflDNL/9GAXAwf2BPDm6OwAxLY7Q94JMr+I/OSuOFd9G8dsy9444J8NOeFQp2ek+hEeVkpvp3XIwe9lWVJhn4481QfQdks+BOP9q/a1zWxlla8ooW5cNpQpVqMi/OQfxgfwbctw/Kob867MJ/vT4A4x9uA9F0wr/GvQkOvXIYcAF6fQdnIHdx0VAoIMxE+IIDHZgsbpwOS1ERheTmeb9tXAz539tLluAi/6ZTv+h2Tx+UweMPBz0vTCP+G0B5GQYW/banj9VVR8bR54q4zCs1kpRu8rbOPRQSt2nlKrKxTsBllf4u05KqTtFxA94C7jG80yP/+FuTHocpdRspVQfpVQfO1W7ppedbicj2U7T1u70aY/BeYY18onbEkBMq1Kim5Vgs7sYMiqHdcuMS0WaWXYAX38n/oHOo+97n59PQpy38RUPPh9H0r4Avp53rBFYaIR79RBR3DDmAIs+bVLz+JP3krTXn6/nHoux7ucIhl+VBsDwq9JY+5N3D4Iye9mGRjgIDHE3LvTxc9Hr/HyS9lb/OrXf2EBCvg4n5ItwAp4NxtbbTuiSCEK+jSDkC/dw/DhaaXAmHbvU5fitDGvTqt/98P7Mc7j1kvO5Y+R5TH28K1s3RPDKpK5sjQ1n8HDPvL88mXUro6o9HScyc/6bvWwr6n1+DteMOcyz/9eOkmKrobGHjMpm5ULjH3hWm/PnbHfSjINSyrtWWmeu5cA44EEAEQkH1gFvikhbpVS8iAQCMUCa528yPG0ergG+MKogbz7VjMdmJmDzcZFywJdpj7QwJK7LKbw5KYbJC/ZhscKyTyI4sNvYlsdmlR0gPMrBM3PcLcCtVsWKb8KJXendDqJTr1yGjUplf1wgM7/cAMC8N1oT06KIy/51CIA1P0ay/OtGNYrfuXc+w69KZ/+uAGZ9u8Udf1oLPvtvDE9M382Ia9NIO+TL5AfaeTUdZi/biOgyxr+RiMWisFjg1+/CWP+j+Tvn0i+LccSWgQ0kWPCf5H0zq7nTz+Gxl7Zx6z3x7I0LZuk3Maf/o9Mwc/6bFfvx6fF0G5BPSLiD+b9t5sM3mnL93cnYfRST58cB7gaSM5+s/t1EJ/L1d9Lr/DymP97c61gnqo39muYm6ky4qGogESlQSgWdMGwIMF4pdZmnAvAm0BtwAs8ppb4SkaHAVDiaMnhSKfWtiLwI/AtIAXYDB5RSz55s/CESofpbLzZ4qipwmfzYa4uxZx+16Ux/rLarsOrp+Roxpl3vSYWa/lhtc9dNZ2ra6X9UR4mvcXcvVEaVOUyNb+Z+bb36iTyVZcrK7xfTTLUY8/Dpf1gNu595eKNSqo+hQaupbt8jZ4ITKw2eYSuBlZ73Bbj7djjxNz8DfSsZ/iTuhpOapmmaVu+ddRUHTdM0TasV9bTnSO/7V9U0TdM07ayhMw6apmmaZhadcdA0TdM07WymMw6apmmaZpZ6mHHQFQdN0zRNM4GgG0dqmqZpmnaW0xmHv4Ny/d0lqDmTO5iy+JnX05urwNwOlJSjDjyJzwvWYO+e9XE6hdeb2wnRos2LTI0/okkPU+ObyWJ2B1A2cw8lpnd+ZiadcdA0TdM07WymMw6apmmaZoZ62gGUrjhomqZpmlnqYcVBX6rQNE3TNK3KdMZB0zRN08yiMw6apmmapp3NdMahDrNYFDMX7yYzxc7Tt7U2NHafIXmMfSEZq0Wx+OMIPpsVbWj8h19LpP/wfHIybIwZ2t7reA9N3Ue/C7PJybRz96Xdjg6/4tYULrslFZdT+H1FGO9NbV6z+K/sp//QHHIy7Yy9uAsAQaEOnnhzL9FNS0g96Mvke9pQkGfMJnMmLdvIRiU8MjWO8AalKCUs+awRC+fHcNO9BxhxbQq5WXYA5r3ekthfI2o8HotF8cYHa8hM8+O5h/swdfY6AgIdAISGl7L7z1BenNC7yvEKcq28Pr4ZCbv8EHGvk03blDB5bEtSD/oQ3bSUSf9NIDjMfYvxH78F8c7TMTgcEBrh5NWv4qs9DVFNSpkwPZGwKAcoWPRhA755N6racU6mtpZtuavuOMj/PbafGwYMIC/HXqP441/ZQ3hkGUrB4k+jWTivCUGhZUycvpvomBJSD/ky5f72hmxbZu/XakI3jqwHRMQJbMM97TuB24BOwK1Kqfv/zrKd6Mq70kna40tAsLH9PlgsinGTDzHxhtZkHLYzc9Ee1i0NJXGPcX0oLPs0gm/nRjJhepIh8ZZ/Ecm3H0Qz/tW9R4d1G5DLgIuyGTeyK2WlFkIb1LwfheWfR/LdvIaMf23/0WHX33OYLWtC+Oztxlx392Guu+cw773UzKvpKHcmLVunU5gztTV7dwThH+hgxpdb2PRbGADfzIvhq/eaGlL2K25IIGl/0NHKwmOjBxz97ompm1j3S/UOAm8/HUOfIXk89b8EykqFkiILn8xoSM/B+Vx/XxqfzmzIp7MacteThynItTJrYlP+89FeGjYtIyejZrtGp0OY/XwT4rcF4B/oZNaS3Wz6NdiQbas2l23S3kAiG5XQa1A2aYdq3geE0yn8b0pLT3wnM77+g81rwhh+dRpbfgvl89lNuXb0Qa4bc5D3XmlZ4/FA7ezXaqQeVhzOxksVRUqpHkqpLkApMFYpFVvXKg2RjUvpNyyPxR83MDx2+55HSE7wISXRF0eZhZULwxg4ItfQcWxfH0R+tnH10u0bQsjPOT7eyJvS+OydJpSVulfj3MzqnxEdjf978F/iD7wohx+/dM//H79swLkX59Q4fkVn2rLNTvdh744gAIoKbSTu9ScyutSI4h7VoGERfQens3ThXytm/oFldO+TydpfGlY5XmGehW3rArnkxiwA7D6KoFAna5eGMvw697Dh12WxdkkoACu+DmPQP3Jo2NRd+QyLdNRoOrLS7MRvCwCgqNBKUrwfkY2N6Ristpft6Il7ee+VVl4d946PbyVprz8NoksZOCyLH792L88fv27IwOFZXk0H1M5+TXM7GysOFa0C2orIEBH5HkBEnhWR90RkpYjsE5H7PcNbisj28j8UkfEi8qzn/f0iskNEtorIJ0YUbOxzh5jzYhNTOpls0KiM9GSfo58zDtsN27nVpphWxXTpm8/rX23n5Y930K5bgaHxwyLLyEpzz6esNDthkcbMozN52TaMKaZNx0J2/eHuZfLym5J5c+FGHvzPboJCaj6e0Q/vZO6M9pXOk4EXpLFlQwOKCqteMUxJ9CW0gYNpDzXnnova8fojzSg+YiE7w06DaHelIKKhg+wMd8yD+/woyLEy4Z9tGTeiHcs/D6/xtJSLblpKmy5F7NoU4HUsqN1lO2BoJpmpvuyPCzI2fqdC4v4IIiyyjOx097RkpxuzbdXJ/Zoy4VUHnLUVBxGxAZfivmxxog7ACKAf8IyInG6P9TjQUynVDRjrbdn6D88lJ8N29MxFq5zVqggOdfDQ1Z2ZM6U5E2fGY96WJYZEPpOXrV+Ak0kzdjJ7SmuKCm388HFj7ryoL/de2YusdB/uemz/6YNUou/gNHKzfYnfFVrp9xeMSOaXpU2qFdPphPhtAVx2awZvLd+NX4CLT2cdn7EQAfFcgHY6YM+2AF6Yv4/JC/ay4I1GHNxb8xS9X4CTp+Yk8M7TTThSYK1xnNpScdm6nML1Y5KYP6OFofGfnBXHf//TiiMFJ2YiBVVHDoha1ZyNFQd/EdkCxAKJwLuV/OYHpVSJUioDSANOd3F1K/CRiNwM/CXHKSKjRSRWRGLLKDltATv1KWTAxXnMW/cnE986QPdB+Tw648Bp/66qMlPsRDU5lmqObFxGxuGap/n/LhkpPqxZGg4Iu7cGoVwQGlGzFHNlcjLsRDR0z6eIhqXkZng/j87UZWu1uZg0Ywcrv4vit+WRAORk+uByibtR3eeNaNc1v0axO3XPpv95qby3cCWPTd5Ct76ZjH/+DwBCQktp1ymXDWuq18AwsnEZUY3L6NDrCACDL8shfps/4ZFlZKa6D1yZqTbCGrjXl6jGZfS+IB+/ABehDZx07V/Avh01uzZutSmempPAz1+Fs2ZxWI1iVKa2lm3j5sVENy3mzYWbmPvT70RGlzDjq82ER9bs8pTV5uLJWXGs+DaK35a5L8/lZNgJj3LHC48q9eoyY7m6ul8TZeyrLjgbKw7lbRx6KKXuU0pVtjVUPLo7cTekdHD8/Kq4VxkJvAn0AjZ4shlHKaVmK6X6KKX62Dn9Wczcl5pwc5/O3DagM1PuacEfa4J5+X7jav9xWwKIaVVKdLMSbHYXQ0blsG5Z5Wd7ddna5eF0H+A+WMW0KsJmV+RmGdeuYt2PYQz/ZyYAw/+ZydrlYV7HPDOXreLBF/eQtDeAr98/1hCyfMcPcO7wTA7sqVkWZd6b7bntsqH8e9QQpj7Rg60bGvDq090BGDQshd9XN6SstHpn7RENHUQ2KSUp3r29bVkVTPNzShhwcR4/fua+8+PHzyKOXgMfeEkuf24IxOmA4iPCrs0BND/n9JX8v1I8PC2JpD1+fDXbuLspoPaWbcLuQG4cNIA7hvXjjmH9yEj15f6re5Kd4XOaWCeJP3kvSXv9+XrusazRup8jGH5VGgDDr0pj7U81vxunXH3Zr50Jzrq7KryQCjQUkQZAAXAZsERELEAzpdQKEVkN3AAEATl/W0lPw+UU3pwUw+QF+7BYYdknERzYbWzL48ffOkC3gQWERjj4MHYH86dFs9SLxoCPTY+nW/88QsIdzF+zifnTm7Ls8ygemrqPtxdvxVEmTJvQGpCalXfGXroNzHfHX7eFD1+P4dO3GvPEW/GMuD6dtEO+/OeeNjUuf20xY9l26pXHsCvT2B8XwMyvNwHuWy+HjEyndccClILUQ37MfOYcIybhOOdffJgv5tXsdtVxLx5i6r0tcJQJjZqX8sjriSgX/GdsS5Z80oCGMe7bMQGan1PivpVvWAfEorjkxixadiiu9jg79ytk+LXZ7Nvhx1vL4wCYO6UxG34OqdE0VFSby9ab22or6tw7n+FXpbN/VwCzvt3ijj+tBZ/9N4Ynpu9mxLVppB3yZfID7bweV23s12qkjmQJjCTqLLu4JCIFSqmgE4YNAcYrpS7zNHgsUEq96vluO3CZUirB01DyAeAQsA9IAP4DrABCcR+1PlRKvXSy8YdIhOpvGW70ZB1zhi9PMx+rrZzmPs7c9Mdqm7xsrSHeH9xORYKNa2hXmR826Mdqn4zZy1Y5nabGN/Ox2uvVT+SprJqdcZyGf6Nmqu3NDxsac/u0hzcqpfoYGrSazrqMw4mVBs+wlcBKz/tnT/iuS4X3M4AZlYQdbGQZNU3TNK2uOusqDpqmaZpWa87sJHClzsbGkZqmaZqm1ZDOOGiapmmaGepQp01G0hUHTdM0TTOBUNP7vOo2falC0zRN07Qq0xkHTdM0TTNLPbxUoTMOmqZpmqZVmc44/B3O4E6axGbuKuMqqUk3v3XEGbxcAZz5NXvWRJXl5Zka3vQOmiwmPqzKZW4HSk6T570tpnoPIasuMzuAMltdeb6EkXTFQdM0TdPMUg8rDvpShaZpmqZpVaYzDpqmaZpmFp1x0DRN0zTtbKYzDpqmaZpmBqUbR2qapmmaVh264qDVlqgmpUyYnkhYlAMULPqwAd+8G2VY/D5D8hj7QjJWi2LxxxF8NivasNgAV92ZyiX/ykApSNjlz7TxLSkrMfbKmMWimLl4N5kpdp6+rbWhsc2Mb/ayNTs+nLnz5uHXEuk/PJ+cDBtjhrY3LG5F89Zup6jQgsspOB3CfSM7GBbb7O3WrPljsSje+GANmWl+PPdwH0Bx6917GDzsMC6X8MOXzfnu05Zej8fs+aO5ndEVBxFxAttwT8dO4Dal1BED468ExiulYo2KWVVOhzD7+SbEbwvAP9DJrCW72fRrMIl7/LyObbEoxk0+xMQbWpNx2M7MRXtYtzTUkNgADaJLGXVHGqOHdaa0xMITb+1jyOVZLP8i0pD45a68K52kPb4EBLsMjWt2fDOXbW3EhzN33iz7NIJv50YyYXqSIfFO5tFr25GXbezu1eztFsybP1fckEDS/iACAh0ADL/8EJHRRYy59nyUEkLDve+/pTbmT03Ux0sVZ3rjyCKlVA+lVBegFBj7dxfIKFlpduK3BQBQVGglKd6PyMZlhsRu3/MIyQk+pCT64iizsHJhGANH5BoSu5zVpvDxc2GxKnz9XWSm+hgaP7JxKf2G5bH44waGxq2N+GYu29qIfybPm+3rg8g3+IBeW2pjuzVj/jRoWETfweksXdjs6LB//DORj+e0RSn3I6Bys329Hk9tzB/N7UyvOFS0CmgrIhEi8o2IbBWRdSLSDUBEnhWR+SKyVkT2iMj/eYYPEZHvy4OIyCwRuf3E4CJSUOH9NSLyvuf9tSKyXUT+EJFfzZiw6KaltOlSxK5NAYbEa9CojPTkYwfyjMN2Q3fOmak+fDE7mvnrtrEgdiuFeVY2rQoxLD7A2OcOMefFJihzkg2mxy9n9LKtjfj1Zd6YRsHkBXuYtWgnl96UYVhYs7dbs4x+eCdzZ7Q/bn1pHHOE8y86zBvz1vDc9A00aeZ9z5B1dv4og191QL2oOIiIDbgU92WL54DNSqluwBPABxV+2g0YCgwEnhYRI/pJfRoYoZTqDlxhQLzj+AU4eWpOAu883YQjBSZ2eWugoFAHAy/K5fZBXbipbzf8ApwMvSrTsPj9h+eSk2E7emZqNLPjlzN72ZoRv77MGzM9fHU77r20I5NuacsVt6XTpb/JXXnXYX0Hp5Gb7Uv8rtDjhtt9XJSWWnnwtkEs/aYZDzy17W8qYf0jIs1EZIWI7BCRP0XkAc/wCBFZ7jlxXi4i4Z7hIiIzRCTec8Ld63TjODNzdsf4i8gWz/tVwLvAeuCfAEqpn0WkgYiUn+4uVEoVAUUisgLoB+R4WYY1wPsi8hnwVWU/EJHRwGgAP6q+w7XaFE/NSeDnr8JZszjMy2Iek5liJ6pJ6dHPkY3LyDhsNyx+z8H5pCb5kJvljrlmSTgdexfy89fGpLY79SlkwMV59B36Jz6+ioBgJ4/OOMDL97c4I+KDecvW7Pj1Yd6YLTPFfdabm2lnzZJQOvQ4wvb1wQbENXe7NUOn7tn0Py+VPuem4+PrxD/Qwfjn/yAjzY/fVrgbLv62IpoHn/a+4lBX58/f0MbBATyilNokIsHARhFZDtwO/KSUeklEHgceBx7DfdJ9jufVH3jb8/9JnekVhyKlVI+KA0TkVL8/cREq3DO5YublZC1pVGW/UUqNFZH+wEjcC6i3Uuq402ul1GxgNkCIRFRxNVI8PC2JpD1+fDXb2BbxcVsCiGlVSnSzEjJT7AwZlcNL44zb8acd8qFDr0J8/VyUFAs9BuWxZ2ugYfHnvtSEuS+5k0XdBuZzzdh0Qw9cZsc3c9maHf/Mnzfm8vV3YrG422f4+jvpfX4+H73RyJDYZm+3Zpj3Znvmvem+O6Nrr0yuvnk/rz7dndvvjaNb70yWJwfQtVcWhxK93z/UyfnzN1xeUEodBg573ueLyE4gBhgFDPH8bB6wEnfFYRTwgVJKAetEJExEGnviVOpMrzhUZhVwE/CCiAwBMpRSeZ4KxSgRmQIE4p6BjwNWoJOI+AL+wDBgdSVxU0WkIxAHXAXkA4hIG6XUemC9iFwKNAO8zst37lfI8Guz2bfDj7eWxwEwd0pjNvzsfVsBl1N4c1IMkxfsw2KFZZ9EcGC3cS2P47YEsmpROLMW7cDpFPb+GcDiBcbeUXEmM3PZ1kZ8M5ld9sffOkC3gQWERjj4MHYH86dFs9TARp7hUQ6embMPAKtVseKbcGJXhp7mr6rG7O0WzJ8/5T5/vzUTXviDK29MoOiIjRkvdvE6Zm3MnzoiUkQq3uk323Ny+hci0hLoiTsTH12hMpAClN+rGgNUvI3moGfYSSsOos7gRwGLSIFSKuiEYRHAe0Br4AgwWim1VUSe9Qw7B4gEXlb/3955h0dVpX/886ZACJCEkNARARUEFESk2BYVC5bFXpZdG66LXbGvvYuKa0H0h6CL2BVsKyBYEERREREQpfeeQAiEkvb+/rg3MAkBksy5yUx4P88zz9w5c+d7zpy5c+973/Oe96i+6n/mKTxjYDGwBfhUVf8bOh1TRM4HBgLrgWlAHVW9XERG+5oCfAXcrHvp1CRJ1W5ykrM+qGyCXlZbC4JdXjhQovi/BMDevXXhE+39E8XLagdN0Mtq569cFZj2j/oV2bohkIM/Mb25tj13gFPNX4cO+EVVu+xrPxGpA3wLPKaqo0UkS1VTQt7fqKr1/MkBT6rqd375V8Cde0tDENUeh5JGg1+2ATh7Dx+ZqaqXlvKZO4A7SinvGbL9IfBhKfucW/YWG4ZhGEawiEg8MAp4S1WLYu/WFg1BiEhjYJ1fvhLPU15EM79sj1SLWRWGYRiGEWkIXnCky8c+6/TG5YcDf6jqsyFvfQpc5m9fBnwSUn6pP7uiO7Bpb/ENEOUeh/Kgqg9WdRsMwzCM/YzKH6E7BvgHMCtk1uG/gSeB90WkH7AUuNB/bwxwOrAAb3j/in1VsN8YDoZhGIZR3fFjFfYUs7FbgJ0fk3ddeeoww8EwDMMwAkKiPSi4FCzGwTAMwzCMMmMeB8MwDMMIgghaX8IlZjhUNwKei6/5+YHqB9l+iQ12zQMtjPIzRJTnEggc6589EmSehWjHltU2DMMwDGO/xjwOhmEYhhEU5nEwDMMwDGN/xjwOhmEYhhEQFuNgGIZhGMZ+jXkcDMMwDCMoqqHHwQwHwzAMwwiCMi5MFW2Y4RDBdOmZTf9HVhEbo4x9J5X3Bzd0ph1fs5BBoxYQX7OQ2FiY/HkyIwc1dqYfZNuLiIlRXhw7j8w18dx/WSun2uf0W8tpl2SgCkv+rMWg2w4kb4e7kb0RP8xmW04MhQVCQb5wwxltnWkHrR/0b2v6eya9SS63P7+MlPR8UBjzZn0+Hp7uTD+a2w6Vc94xqqHhICIFwCy87/YHcJmqbq3aVpWfmBjlusdXcvfFrchYHc+LY+Yz9Ytkls1PcKKft0O448LWbN8aS2yc8uxH8/n5myT+nF47bO2g217E2VetZ/n8miTWLXSqW79hLn2uWMfVJ7Und0cM/x6yiJ5nbWDCh2lO67njgkPI3hjcXzAI/aB/W9PfOwX5wtCHm7BgViK1ahcweNw8pk+q60Q/mtsOlXfeKTfV0ONQHYMjt6lqJ1XtAOQC/UPfFJGoMJbaHLGVVUtqsGZZTfLzYpj4SQo9Tt3ksAZh+1Yvk2JcnBIbr7haiyX4tkNa41y6npTN2HfqO9UtIjZOqZFQSEysUrNWIZlrawRST7QR9G9r+ntnw7p4FsxKBGBbTizLFySQ1jjPiXY0tx0q57xjeFRHwyGUycBBItJTRCaLyKfAHBE5UERmF+0kIreJyIP+9kQRGSgiP4nIPBE5zi+/XEQGh3zmf75urIj8V0Rmi8gsEbnFRcPrN8pj/apdF6uM1fFO/2TgWehDxv/JezNn8+ukusz9NXxvA1RO2/s/tJJhjzZB3TobAMhcW4MPhzZk5NRZvD1tJjnZsUyfnOS2EoXH357P4DF/0LtvhlvtAPWD/m1Nv+w0bJZL6w7b+HN6ohO9aG47VG77y4rgxTi4fEQCUXH3XRF8z0JvYJxf1BnooKqLReTAfXw8TlW7isjpwANAr73s2wlo6ns4EJGUUtpyNXA1QALu/ijhUlgoXHtKW2on5fPA8CW0aLONpXNrVXWz9km3XpvIyohjwaxEDu+x2bl+neR8epy8icuP6cCW7DjueXkhJ56TydcfufNuDDj3EDLX1CC5fh5PvrOA5QtqMvvHulGjb1QtCYkF3DdsCa/c34StW4Jdg8U10dz2CmHLakcFtURkBjANWAYM98t/UtXFZdQY7T//Ahy4j30XAa1E5EUROQ3ILrmDqg5V1S6q2iWemmVqQOaaeNKb5O58ndY4j4zV8WX6bHnJyY7jtyl1OKqnm4tw0G1v1yWH7qdkM2Lq79w9ZCkdj9nMHS8sdaZ/xLGbWbu8Bps2xFOQL0wZV49Dj8xxpg+Quca7M9qUGc+Uccm07eQ2DCco/aB/W9PfN7Fxyn3DlvD16HpMGZviTDea2w6Ve87c36mOhkNRjEMnVb1BVYuOpNAzfz7Fv3vJ6Jkd/nMBu7wypX5GVTcCHYGJePEUw8L+BsDcGYk0bZlLw+Y7iIsvpGefLKaOT3YhDUByaj61k7yVLmskFNL5+M0sX1g2o2ZfBN32159swt+7tOey7u154toW/DalLk/d2MKZ/rqVNWjbOYeaCYWA0umYbJYvcBdgVbNWAbVqF+zcPvL4zSyZGx36Qf+2pr8vlAGDlrN8fgKjh7qdkRDNbYfKaH/FsKGK6sNaoIGI1Ae2AGeya0hjTywBrhWRGKAp0BVARNKAXFUdJSJzgTddNLCwQHjpnqY8/vYiYmJh/LupLJ3n7uKS2jCP255bRkyMEhMDkz5L4ccv3fzJgm570MydUZvJY+oxeMwcCgqEhb8nMvZtdzMq6qXn88CwRQDExirffFyPaRPdneCC1A/6tzX9vdO+aw69LtjIojkJDJkwF4DXn2jMz1+HH4MTzW2H6D/vRBOi1Wz8RUS2qGqdEmU9gdtU9cyQshuBm4CVeMMNS1T1QRGZ6O87zTcKpqnqgSIieEbBkXjTPOsBDwIbgdfZ5Y24W1XH7ql9SZKq3eQkF1+1dESC04bgx+sCbL/EBjueqoVR/l8qLKjqFhhGpfOjfkW2bgjkxFMntbkedurNTjWnvnvbL6raxaloOal2HoeSRoNfNhFvKCG07AXghVL27RmynYEf46CehdV3D9V2rmBzDcMwjGqMBDDzq6qpjjEOhmEYhmEERLXzOBiGYRhGxBDlI5ilYR4HwzAMwzDKjHkcDMMwDCMgImUKpUvM42AYhmEYRpkxj4NhGIZhBIFSLVNOm+FQ2YggNd1kaCwN3bFj3zvtp0hcsIe7bt8eqH7QBN0/MYnBrtMiqSmB6ucvWRaofpBsP6troPq1xs0IVF/zcve9U4RiQxWGYRiGYezXmMfBMAzDMILCPA6GYRiGYezPmMfBMAzDMAJAqJ4xDmY4GIZhGEYQqFbLWRU2VGEYhmEYRpkxj4NhGIZhBIQNVRiBcsvARXQ7MYuszHj6n3YYAFfdvYxuJ2WRnyesWprAs7e3JGdz+D9bl57Z9H9kFbExyth3Unl/cMOwNStLP75mIYNGLSC+ZiGxsTD582RGDmocluYtAxfR9YSNZGXGc03vw3eW//XSNZz5j7UUFgg/fZPCawMPCLf5pDfJ5fbnl5GSng8KY96sz8fD08PWLSLo37bPlWvpfUkGIjD2nTQ+Hh6eflqjHdw6cC716ueiKox7vxGfjGxK3+uXcuoFa9i0IR6AEf85kGmTUitUx2ujvmTb1jgKC4SCAuHmfscDcNb5iznjvMUUFgg/f9+Q14e0C+u7QLD970L7zku/pcdhy9i4uRZXPHx+sfcu7DWT6y74kb8O+AebchLodMgqHrt2PKsz6gIw+deWjPi8c5nruuXpxbvOaad0AKBOcj7/fmkhDZvtYO2Kmjx+bWu2ZLu5FAV97BsegRkOIlIAzPLr+AO4TFW3BlTXEqCLqmbs4f0U4G+qOiSI+l0xYVQan73RkNsGLdpZNv27ZF57qjmFBcKVdy7nomtX89rA5mHVExOjXPf4Su6+uBUZq+N5ccx8pn6RzLL5CeF+hUrRz9sh3HFha7ZvjSU2Tnn2o/n8/E0Sf06vXWHNCR+m8ekbDbntmYU7yw7vvonuJ2/kujMOIy83huT6eS6aT0G+MPThJiyYlUit2gUMHjeP6ZPqOumfoPu+xSHb6H1JBjeddSh5ecJjI+fz45fJrF5acf2CAmHYwFYsnFOHWrXzeWHUDKZ/nwLAxyOaMvq1Zk7afvf1PcjetCv52uGdM+h+3Bquv/Qv5OfFklwv/ORpQfa/K+2xPxzC6G/a8+8rJhYrT6+3haParWBNZp1i5TPnN+Lul06rUJsnfJDGZyMacNuzi3eWXXTtamZMSeL9lxtz4TWrufDa1bz2ZHjnNAj+2K8w1dDjEGSMwzZV7aSqHYBcoH+Ade2LFODaKqy/TMz+KYnNWcVtuemTkyksEAD+/LU2aY3Cz6DW5oitrFpSgzXLapKfF8PET1LoceqmsHUrSx+E7VtjAYiLU2LjNez4o9k/7973Z/Rdx/uvNCEv1/ubbMqMD68Snw3r4lkwy8uiuC0nluULEkhr7MYoCbrvDzh4O3N/rc2O7TEUFgizptblmN5ZYWluXF+DhXO8i9W2nDiWLaxFWsPgMwWefs4SPhh5EPl53rG0aWP4GV2D7H9X2jPnN2bz1t2/6/UXTOWV0d2cxvLN/qnubv+rHidn8eWo+gB8Oao+R5+S5aSu4M87FUPU7SMSqKzgyMnAQSLSWEQmicgMEZktIscBiMgpIvKDiEwXkQ9EpI5fvkRE0vztLiIy0d+uLyLjReR3ERmGN+sF/70BvvZsEbnZL34SaO3X+/Re2rElROd8Efmvv91aRKaKyCwRebRoPxGpIyJf+e2eJSJ9guzEUy7MYNq3yWHr1G+Ux/pVNXa+zlgd7+zCVRn64N1dDBn/J+/NnM2vk+oy99eKexv2RNOW2+lw1Gb+M3o2T70zh0MO37LvD5WThs1yad1hG39Od5OOOei+XzI3gfZdt1A3JZ+aCYUcdcIm0hu7u8g3aLqd1ofm8Odvnmv8rL6reOmTX7j5sXnUSar491CFR56byvOvTeK0PksBaNo8h/YdN/Dsq5N58qUpHHxoVtjtD7L/g9Q+puMSMrISWbii/m7vtW+1juH3juKpG8ZyYOMNYdeVkpbHhnXe99iwLp6UtMjvH6M4gRsOIhIH9MYbtvgb8IWqdgI6AjN8w+BeoJeqdgamAQP2IfsA8J2qtgc+Ag7w6zoSuALoBnQH/ikiRwB3AQt9D8jtpbVjH/U9DzyvqocBK0LKtwPn+O0+ARgkIlLywyJytYhME5FpeVqx9Qwuvm4VBfnC1x/v/sfeHyksFK49pS19u7SjzRFbadFmm/M6YmOVusn53HJue4Y9cQB3v7gAl37HhMQC7hu2hFfub8LWLbHOdINk+YJafPByIx5/az6PjpzPwjm1KCzc7ZCvEAmJBdzzwh8MfaIV23Li+PydxvQ7+SiuP7szG9bX4Ko7F+9bZA/c0f8YbrriL9x/azfOOHcJ7TtlEhOn1E3KZcA/j+W1we2465FpVEu/8j6oGZ/P33vP4LVPu+z23rxlaVz070vo9+h5jPqmPY9dM8Fx7VK9e1yBQnX7iACCNBxqicgMPENgGTAc+Bm4QkQeBA5T1c14F/h2wBR//8uAFvvQPh54E0BVPwc2+uXHAh+pao6qbgFGA8eV8vnS2rE3egAf+Ntvh5QL8LiIzAS+BJoCu0XjqOpQVe2iql3ipfzjbSeft55uJ27kqZtbEeJcqTCZa+JJb7LrLjGtcR4Zq9244StDP5Sc7Dh+m1KHo3ru6ycsPxlrajDli3qAMG9mHbQQklPznWjHxin3DVvC16PrMWVsihNNqJy+/+K9NG4441Buv6ANWzbFsXJR+C7+2LhC7nlhDhM/S+f7CWkAZGXWoLBQvIDJDxpxyGEV/40zM2oB3nDED5Ma0ebQLDLXJfD9t40BYd4f9VAVklLC854E2f9BaTdNz6Zx/c0Mv28U7z72Dun1cnj13tGkJm1l6/YabNvh1fHj7AOIjS0kuXZ4i7llZcST2sD7HqkNctmUEdn9Y+xOZcQ4dFLVG1Q1V1Un4V30VwL/FZFL8a6EE0L2baeq/XyN/JA2Ootw2UM7oPjtRlnq6wukA0f63ou1LtsJcOTxWZz/r9U8+M9D2LHdzV3p3BmJNG2ZS8PmO4iLL6Rnnyymjg9/CKSy9JNT86md5F3AayQU0vn4zSxf6H7F0R8m1KNjd+9i1bTlNuLilU0bXMQTKwMGLWf5/ARGD3U3mwKC73tgZ5BoepNcjjltI998UrGZDrtQbn50PssXJvLRf3cFQtZL33UROLpXJkvnV2w4p2ZCPrUS83dud+66nqWL6vLDpEYc3tmLp27SfAtxcYVkZ9XYm9Q+CbL/g9JetCqVs2//BxffcwkX33MJ6zfW5p+PnsuG7ERSk7ZSdFpse+A6YmKUTTnh/demfplCr/MyAeh1XiY/TEgJ8xt4VMaxXyHU8SMCqNTpmCLSAlihqq+KSE2gM/AY8JKIHKSqC0SkNtBUVecBS4AjgbHAeSFSk/CGGx4Vkd5APb98Mp4h8CSeQXIO8A9gM1B3H+14A1grIocCc/3PFt3iTPXrfw+4OKQdycA6Vc0TkRPYt6dkr9z1/AIO776ZpHr5jPz+V958rhkXXbOK+BrK4yPnAl6A5Iv3tgynGgoLhJfuacrjby8iJhbGv5vK0nnu7J2g9VMb5nHbc8uIiVFiYmDSZyn8+GV4J4g7n1/A4d2yvb6fMp2Rzzdj/Afp3DJwES+PnUl+njDodjcen/Zdc+h1wUYWzUlgyATvd339icb8/HVS2NpB9z3Aff+3iLr18inIE1667wBywpxK165zNiedvY7FcxN58aPpgDf1sucZ62l16BZUYe3KBF584OAK6ddL3cE9T0wDIDa2kG8nNOWXHxsQF1fIzffM4KU3J5KfJzz76BGE+/sG2f+utO/v9zWd2qwiuc52PnjybV7/rDNjprQtdd+/dF5Mn7/MoaAghh15cTz06kmUp4/uemEhh/fwz2lTZ/Dmf5ry3pDG/HvIAk69aD3rVtbksWtbl/s7lEZlHPuGh2hA6TBFZIuq1ilRdhlwO5AHbAEuVdXFInIiMBAoMmXvVdVP/aDF4UA2MBFvymVPEakPvIM3NPA9cAreXX+GiAwArvR1hqnqc37dbwOH4xkhs/fQjvP9dqzHG2Kpo6qXi8jBeEMjtYBxQF9VberHZ3wG1PH37w70VtUle+qXpJj62r1m73L2ZtnRHeFPKatSdg8RcUZMTfdeiVAKt4fnwq1qJC7Y+4iYRDcBoHtCUlMC1c9fsixQ/SDZflbXQPVrjZsRqL7mBTfL5kf9imzdEMiJp25yMz3y6Budan477s5fVHX3gJRKJLAzRUmjwS8bAYwopfxr4KhSyicDh5RSnolnLJRW77PAs6WU/61EUWnt+BD4sBTZlUB3VVURuRho4++fgRf/YBiGYRi7Uw3XqrDMkWXjSGCwP2Mii10eDcMwDMPYrzDDoQz4no+OVd0OwzAMI7qIlKRNLrHVMQ3DMAzDKDPmcTAMwzCMIIigKZQuMcPBMAzDMAJAAKmGwZE2VGEYhmEYRpkxj0NloxporoXYlGAzpWluwIvGFBYGJi0JweZxiG11QKD6unRloPpBUxhwjhFZsy5Q/Wim1tjpger/4/eKryNSFt5oG+B/K2iHQHCntCrDPA6GYRiGYZQZ8zgYhmEYRkBUxxgHMxwMwzAMIwiq6awKG6owDMMwDKPMmMfBMAzDMAJBq+VaFeZxMAzDMAyjzJjHIUJJb5LL7c8vIyU9HxTGvFmfj4enV1gvrdF2bn1iLvXS8lCFce835pM3m9Kq7Rauf2A+8TULKcwXXnrkIObNSiq3/i1PLKDriRvJyoznmtM7AXDX8/No1nIbAHWSCtiSHcv1f63Ykh+3DFxE1xN8/d6HA9D3phWcdtE6Nm2IB2DEM835eWJKhfSD7p+mzTZz170/7HzduHEOI0e0p06dPE49fRGbsrypoiNeO4xpPzUuZ9t3cNvT83e2fex7DflkRBOOPS2Dv9+4nOatt3HzeYczf/ZuC9aGpV8nOY+7n59Hw6Y7WLuyJk/c2IYt2eU/pdwycBHdTswiKzOe/qcdVuy9c69azdX3LOfCzkeQvTG+Qu0P+tgJZcCzy+jWazNZGXH868Q2YeuVpEvPbPo/sorYGGXsO6m8P7ihU/1z+q3ltEsyUIUlf9Zi0G0Hkrej/PeXo05sTHztQiQGYmLhjNFrmTYwmRXf1CImXql7QD7HPLGBGknKqik1mT4ohcI8iImHI2/PonGP8k/dja9ZyKBRC4ivWUhsLEz+PJmRg8r3XwqC6rhWRVQaDiLSDHgJaAfEAmOAW1U12InilUhBvjD04SYsmJVIrdoFDB43j+mT6rJsfkKF9YY91YqFf9SlVmI+L3z4K9N/SOHKWxfx9pAWTJucSpfjN3DlrYu56/LyX9wnjG7Ap2824ranF+wse/KmXSuiX3X3ErZujq1Q2wEmfJjGp2805LZnFhYr//i1xowaFv7JIej+WbmiLjf091aCj4lR3nj3M374riknn7aEj0cdwugPKn6RKSgQXn3iQBbOqUOt2gW88NFv/DolhaXzE3nkurbc+MjCfYtUQL/XueuY8X0yHwxtxgVXr+DCf63gtacPLLf+hFFpfPZGQ24btKhYeVrjHRx53CbWrqwRVvuDPnZCGf9eKp++nsbtzy93qgvecXPd4yu5++JWZKyO58Ux85n6RXKFzwklqd8wlz5XrOPqk9qTuyOGfw9ZRM+zNjDhw7QK6Z0yYj0JqbuSGDQ5Zjudb91ETBz88nQys/4viSNv30TNeoWc+PJ6EhsWsnFePF/2S+OCyavLXV/eDuGOC1uzfWsssXHKsx/N5+dvkvhzeu0Ktd/YM1E3VOEvbT0a+FhVDwYOBmoBTznQrviVzTEb1sWzYFYiANtyYlm+IIG0xhVPvrQxoyYL/6jr6W2NY9miRNIa5KIqJNbOB6B2nXw2rKvYSXr2z0lsztqTHaocf3omEz+r2Alo3/rhE3T/hNLxiLWsWVWHdevcnNA2rq/BwjmeN2FbTizLF9aifsNcli9MZOXiWoHp9zhpA19+1ACALz9qQI9eGyqkP/un0n/bf923jGFPHhB2VHrQx06xun6sw+aNwdTV5oitrFpSgzXLapKfF8PET1Loceomp3XExik1EgqJiVVq1iokc234x3sRTY7dQYzfNemdctm6xjvd1m+XR2JDz8BIOTiPgh1CQW5FahC2b/U04+KU2HiNjPACVbePCCAaPQ4nAttV9XUAVS0QkVuApSIyH2irqtcDiMj/gGdUdaKInAI8BNQEFgJXqOoWEVkCvAecDDwlIlnA43iejAxVPUlEHgS2qOozvu5s4ExgPfA+0Mzf/xFVfc/1F27YLJfWHbbx5/REJ3oNmmyn9aFb+HNmXYY+2ZpHXp1Fv9sXITFwW99OTuoIpcNRm9mYEc+qpeFfxEpy1qVrOOnc9cyfVYdXHzugQq7ykgTdP385YTkTv9mVCe+sPgs46eSlzJ9Xj2GvdGTLloqfrBs03U7rdjnM/a1iwxLl0U9Jy2Pjeq+tG9fHk5LmLqto95M3krmmBov/cHPMl0YQx06Q1G+Ux/pVu46NjNXxtO281Zl+5toafDi0ISOnzmLH9himT0pi+uTyD8uBt0bDl/3SQeCQi7ZwyEU5xd5fMKo2B/beve3LvqhFars8Yiv4F4iJUQaPm0uTA3P57L9pzP21ir0NCmKZIyOC9sAvoQWqmg0sYQ+GkIikAfcCvVS1MzANGBCyS6Zf/hXwKnCeqnYELthHW04DVqlqR1XtAIwr/9fZOwmJBdw3bAmv3N+ErVvCd4gkJBZwz/NzGPpEa7blxHH6xat49clWXHZSd14d2JqbHpnnoNXF6XlmBt/+r+Lehj3x+VsNubJnJ6474zA2rIvnn/csC1sz6P6JiyukW49VfPdtM+87fNqafpeezvX/OpkNmQlc1f+3sNp+7+C5/N9jLdm6xf1FcO/64uxmqGZCARdfu4o3/tPUjWApBHHsRDt1kvPpcfImLj+mA32POpyExAJOPCezQlqnvbOOMz9ay0mvrmfuW3VY+/OudO8zX66LxCot/1rccMiaH8cvz6TQ4+GKea4ACguFa09pS98u7WhzxFZatNlWYa1oRkReE5F1/k1uUVmqiEwQkfn+cz2/XETkBRFZICIzRaTzvvSj0XCoCN3x4iGmiMgM4DKgRcj774XsN0lVFwOo6r6O4FnAySIyUESOU9VS/YYicrWITBORaXmUPQwjNk65b9gSvh5djyljU8r8uT3rFXLPc3OY+L8GfP+ldyHv1WctUyZ425PHpdHmsM1h1xNKTKxy9KkbmPR5fae6AFkZ8RQWCqrC2HcbcMjhW8LSq4z+6dJ1NQvn1yMryxuXzspK2Pkdxo1pxSFtKnbSjI0r5N7Bc/nm03S+H+++r0vTz8qIp16651Oul57LpsyKBS+WpHGLHTRqtoOXx8xmxOQZpDXKZfBnv1MvrUL+61JxfexUBplr4klvsqsP0hrnkbHaTZ8DHHHsZtYur8GmDfEU5AtTxtXj0CNz9v3BUkhsWABArfqFND95GxkzPRfCgtGJrJhYi+Oe2YDIrv1z1sTyzfVpHDswk7oHFIT9XXKy4/htSh2O6un2fFYhqmao4r94N7ah3AV85Q/xf+W/BuiNN+R/MHA18PK+xKPRcJgDHBlaICJJQCMgk+LfqShqSIAJqtrJf7RT1X4h++3r35Ffmq6qzgM64xkQj4rI/aV9WFWHqmoXVe0ST1kXWlIGDFrO8vkJjB5a8dkUoXo3PzKP5YsS+WhEs52lmetqcNhRnr3TsXsWKx0PJxxxTBYrFiWQscb9AlNFFy2Ao0/dwNJ54bS9cvrnLycs59tvmu98XS911x3R0ceuZOmSiixSptz8+EKWL6zFR683Cat95dGf+nUqvc7xFpbqdc46fvgq1UltS+YmcvFRnbnsuE5cdlwnMtbU4Pqz2rMxw914u9tjp3KYOyORpi1zadh8B3HxhfTsk8XU8e4WtVu3sgZtO+dQM6EQUDodk83yBeUPvMzbKuRtkZ3bq6ckkHJwHisnJfD7sCROfDmDuFq7LoC52cLXV6fR+dZNNDiy4sZhcmo+tZO8eKQaCYV0Pn4zyxcGu7BdpKKqk4CSdyF9gBH+9gjg7JDyN9RjKpAiInuNGo7sQb3S+Qp4UkQuVdU3/IDGQcBgYDFwjYjEAE2Brv5npgIvichBqrpARGoDTf0LfyhTgSEi0lJVF4tIqu91WIIX04DvxmnpbzcBNqjqm35sxFWuvmT7rjn0umAji+YkMGTCXABef6IxP39dsTHHdp2zOanPOhbPrc2Lo72RnhHPteSFBw7hX3cvJDZWycuN4cUHDq6Q/p3/mcfh3bJJqpfPyO9+YeTzzRj/QUP+ckZ4QZE79Z9fsEt/ynRGPt+Mw7tl06rdVlBYu6ImL9zTssL6QfcPQM2EfI44ci0vPrfL7u33z5m0OigLVWHtmsRi75WV9kduptc561n8ZyKDP53htX1QC+JrFHLN/YtJTs3joVf/YNEftbn3ynbO9N//v6b8+/l5nHrBOtatrMnjIbNoysNdzy/g8O6bvd/2+19587lmfPG+C2PZI+hjJ5S7hizl8B5bSE7N581pcxg5qCFfvOPGA1RYILx0T1Mef3sRMbEw/t1Uls5zM6MCYO6M2kweU4/BY+ZQUCAs/D2RsW+X/7+7PTOGidel7WxzyzNzaHr8dj46uREFucKEK7zfNr1jLt0f3sifb9Zl87I4Zr6UxMyXvPNbr9fWU6t++YIDUhvmcdtzy4iJUWJiYNJnKfz4ZbCrBZeJyIhnBGioqkXTVdYARXN5mwKh04BW+GV7nNoiGiFRmuVBRJrjTcc8FEgH3lPVf/kzLt7E80j8AdQDHvSDI08EBsLOW/57VfVTPziyi6pm+Nq98YIjY4B1qnqyiNQCPsHrzB+BHnjunTbA03gLp+YB16jqtL21PUlStZuc5KgndseW1d4zQS+rTRO3c+pLEu3Lamt+fqD6Eur7DoDC7dsD1Q8SiQv2HjGal9X+sfBLsnVDIAdPUp2m2v2w/k41J0y9fymQEVI0VFWHltxPRA4E/ufH3yEiWaqaEvL+RlWt508ieFJVv/PLvwLu3Nu1LBo9DqjqcuCvACJyNPCOiHRW1elA3z185mvgqFLKDyzxeiwwtkTZNuCUUmSXAF+U/xsYhmEYRoXIUNUuFfjcWhFprKqr/aGIdX75SqB5yH7N/LI9Eo0xDsVQ1e9VtYVvNBiGYRhG5BA5eRw+xZsYgP/8SUj5pf7siu7AppAhjVKJSo+DYRiGYRilIyLvAD2BNBFZATwAPAm8LyL9gKXAhf7uY4DTgQXAVuCKfemb4WAYhmEYQaB4EXCVXa3qJXt4a7cAO/UCHa8rj74ZDoZhGIYRAIIiUTgBYV9EfYyDYRiGYRiVh3kcDMMwDCMozONgGIZhGMb+jHkcqhkFWW6X2a1WBJ3AZ1N2sPoSsJ1fGP4aAVVJ9buvc0fQybfeOPTAQPXRKD42zeNgGIZhGMb+jHkcDMMwDCMIqmg6ZtCY4WAYhmEYAWHTMQ3DMAzD2K8xj4NhGIZhBIV5HAzDMAzD2J8xj0ME06VnNv0fWUVsjDL2nVTeH9zQmfaAZ5fRrddmsjLi+NeJbZzpVoZ+epNcbn9+GSnp+aAw5s36fDw83WkdQfY9QEyM8uLYeWSuief+y1o51QYY8cNstuXEUFggFOQLN5zR1pl20H0TpH60HzvR3PcAtZPyueXpZRzYZhuq8OytLfhjeh0n2kGf0ypG2CtaRiTmcQBEpL6IzPAfa0Rkpb+9RUSG+PtcLiKD/e3+InJpkG2KiVGue3wl9/ZtyT97tuGEPlkccLC7PATj30vlnr4tnelVpn5BvjD04SZc3bMtN515MGddnuG0b4Lue4Czr1rP8vk1nWqW5I4LDuHaUw91ajQE3TdB60fzsRPtfQ9wzUMrmDYxiat6tueaUw5l2YIEZ9pBn9MqhBJJy2o7wwwHQFUzVbWTqnYCXgH+47+uo6rXlrL/K6r6RpBtanPEVlYtqcGaZTXJz4th4icp9DjVXXKn2T/WYfPG4BxOQepvWBfPglmJAGzLiWX5ggTSGuc50w+679Ma59L1pGzGvlPfmWZlEXTfBK0fzcdOtPd9Yt0CDuu2hXH+cZ+fF0NOtrtzRNDnNGMXZjjsBRHpKSL/K6X8QRG5zd/+p4j8LCK/icgoEUl0UXf9RnmsX1Vj5+uM1fFOT3DVhYbNcmndYRt/TnfS7UDwfd//oZUMe7QJGuT8boXH357P4DF/0LtvhjPZoPumMo/7aDt2or3vGzXfwaYNcdz67FJeGvcHNz+9lJq1ojgjZFkpdPyIAMxwCJ/RqnqUqnYE/gD6VXWD9hcSEgu4b9gSXrm/CVu3xFZ1c8pEt16byMqI23nXGxQDzj2E63sfyj3/OIi/XraeDt02B1pftBGNx060ExunHNRhK/8bmc51px3K9q0xXHTd2qpullEBzHAInw4iMllEZgF9gfYldxCRq0VkmohMy2NHmUQz18ST3iR35+u0xnlkrI531eaoJzZOuW/YEr4eXY8pY1OcagfZ9+265ND9lGxGTP2du4cspeMxm7njhaVOtEPJXOPdOW7KjGfKuGTadtrqSDfY47IyjvtoPXaive8zVtdg/eoazP21NgDffV6Pgw5zc1xGMqLq9BEJmOEQPv8FrlfVw4CHgN2ifVR1qKp2UdUu8ZQtIG7ujESatsylYfMdxMUX0rNPFlPHJzttePSiDBi0nOXzExg91G1EPATb968/2YS/d2nPZd3b88S1LfhtSl2eurGFE+0iatYqoFbtgp3bRx6/mSVz3QShBX1cBn/cR++xE+19v3F9PBmr4mnWygu47HRsNsvmuwuOjFiqYXCkRZKET11gtYjE43kcVroQLSwQXrqnKY+/vYiYWBj/bipL57n7k901ZCmH99hCcmo+b06bw8hBDfnCYbBekPrtu+bQ64KNLJqTwJAJcwF4/YnG/Px1khP9oPs+aOql5/PAsEUAxMYq33xcj2kT3VwAgu6boPWj+diJ9r4HeOm+5tz54hLiahSyZmlNBt3qzmgO+pxm7EI0QiyYSEFEHgS2qOozItITuE1VzxSRy4Euqnp9iX2uAe4A1gM/AnVV9fI96SdJqnaTk4L9EkbVIBKwvi2rbUQoMQHHiQR4bP6oX5GtGwL58yYnNNajW1zmVHPcvIG/qGoXp6LlxDwOJVDVB0O2JwIT/e3/4g1LlNznZeDlSmugYRiGYVQhZjgYhmEYRiBETlyCSyw40jAMwzCMMmMeB8MwDMMIimrocTDDwTAMwzCCohoaDjZUYRiGYRhGmTGPg2EYhmEEgQKF5nEwDMMwDGM/xjwOlcxmNmZ8qR+WZ3GCNMDd8oamH5x2+W8sorlvol0/mtseefrlz88USb+t25zvxVCCXQa3ajDDoZJR1XIlyBeRaUFmCTP9qtE2/arVj+a2m37VaVcIC440DMMwDGN/xjwOhmEYhhEEFhxpVBFDTb/K9KO57aZfddqmX7X6Qbd9v8dWxzQMwzCMAEiu0VCPbnixU81xK16w1TENwzAMo9pSDW/ObajCMAzDMIwyYx4HwzAMwwiE6rmsthkO+xEiEgvcqKr/qaT66gHNVXVmANodgHZAQlGZqr7huh7XiEhrYIWq7hCRnsDhwBuqmhUN+nuos4aq5galHwQiEgPUUdXsgPQDO/aDRkQaUPx/tcyB5gXAOFXdLCL3Ap2BR1V1ugPtBKAf0J7i7b4yXG2jdGyoIsIQkZtEJEk8hovIdBE5xYW2qhYAl7jQ2hMiMtFvfyowHXhVRJ51XMcDwIv+4wTgKeCvYWoO2NvDQbOLGAUUiMhBeNHfzYG3o0Xf/30PDHndFfjZlb6veYaI3CEi9xc9HOm+7R+btYHZwBwRud2Ftq8f6LEf5LnB1/+riMwHFgPfAkuAsY7k7/ONhmOBXsBw4GVH2iOBRsCpeO1uBmx2pB0eChQWun1EAGY4RB5X+ndBpwD1gH8ATzrUnyIig0XkOBHpXPRwqJ/st/9cvDvdbngnCpecD5wErFHVK4COQHKYmnX9RxfgGqCp/+iPd3fkikJVzQfOAV5U1duBxlGk/wQwTkSuFZHHgFeAK1yJi8grwEXADYAAF+AuJXA7/9g8G++C2BLv/+WKoI/9oM8NjwDdgXmq2hLvPzbVkXZRUuozgKGq+jlQw5H2Qap6H5CjqiP8Oro50g4fVbePCMCGKiIP8Z9PB0aq6u8iInv7QDnp5D8/HFKmwImO9ONEpDFwIXCPI82SbFPVQhHJF5EkYB3enXWFUdWHAERkEtBZVTf7rx8EPg+zvaHkicglwGXAWX5ZfLToq+oXItIfmIC3HsARqrrGlT5wtKoeLiIzVfUhERmEu7veeBGJxzMcBqtqnoi4PBMHfewHfW7IU9VMEYkRkRhV/UZEnnOkvVJE/g84GRgoIjVxd+Oa5z9n+UOYa4AGjrSNUjDDIfL4RUTG490N3S0idQFn/ilVPcGV1h54GPgC+E5VfxaRVsB8x3VME5EU4FXgF2AL8IMj7YZA6Hh9rl/miivwvBiPqepiEWmJ52qNCn0RuQ/vwng8XvzERBG51b+DdME2/3mriDQBMnHnMfk/PPf7b8AkEWkBuIxxCPrYD/TcgHfhrQNMAt4SkXVAjiPtC4HTgGdUNcs3sFwNEw31Y0ruAz4F6gBOhrecECFeApdYAqgIww/a6gQs8v9g9YGmroKsRKQh8DjQRFV7i0g7oIeqDnehX9n44+1JDvvnHryT3Ed+0dnA+6r6uAv9aMe/A71bVbf5r1sAw1T1ZEf69+HFrpwEvITnDRvmu6KdIyJx/tBOxFMJ54bawHY8z0ZfvOG/t1Q104H2AaWVuwi8jGSS4xvo0annOdUct+6VKk8AZYZDhOG7HvsCrVT1Yf8P10hVf3KkPxZ4HbhHVTuKSBzwq6oe5kj/dUpZYNpFhPO+YjFcRGiH1HOc/3KSqv7qQtfXXkzp/dMqGvQrE9+dnaCqmxzplXoXqqoPl1ZeAf3Ajn1f//jSylV1kgv9IBGRWXh9I3gzH1oCc1W1fRiaf1fVN/cUvKyqToOyK0JyfLoeneLYcMj4vyo3HGyoIvIYgud+PBHP9bkZL1L+KEf6aar6vojcDaCq+SJSsK8PlYP/hWwn4AXprXKkPWgv77mM00gEslX1dRFJF5GWqrrYkXboHz4BL/gv1ZF2YPoi8pyq3iwin1H6xTGsWS0h9SQCtwIHqOo/ReQAETlOVf+3r8+WgVC3ewJwJvCHA90igjz2obhrPwHoijdU5+S4F5HN7P7bbgKmAbeq6qKKape8MfGN82srqudT23+uG6aOUU7M4xBhiMh0Ve0sIr+q6hF+2W+q2tGR/kTgPGCCX093YKCq/sWFfin1xeCN+R4dhL5rxJvq2QVoo6qH+OPsH6jqMQHW+YuqHhnJ+iJypKr+IiKlHieq+m04+iH1vId3MbxUVTv4hsT3qtrJhX6JumoCX6hqT9favn6gx76INAeeU1Unt7Qi8giwAm/6rgAXA63xppZe47qfRGSWK09npJIcl649Us5xqvlF5qvmcTB2I0+8RE0KICLpuA2AGoAXQNRaRKYA6XjTG4PiYBxHOIvIpaWVO0oAdQ5wBN7JElVd5QehOaHEcEsMnpHi7H8YlL5vNMQCV6tq33D19kJrVb3InxmCqm51PHMglES8Of9B4fzYL8EK4FCHen8tcYMyVERmqOqdIvLvcIRLDCfE4E1xDssbIyIv7O19Vb0xHH1nVMNltc1wiDxewAvMa+DPkz8fuNeVuKpO9+8a2+DdVcxV1bx9fKzMhLg7xX9eA9zpSt8ndNgmAS+QbjrgwnDIVVUtmqbnB4y5JHS4JR8vyv/CaNBX1QIRaSHBZorMFZFa7DKcWwM7XAiHjLMDxOIZzU7iG3z9QI99EXmRXe0vCpR0Etfjs1VELgQ+9F+fjxcsCaUMT5WTUOM7H2+K86gwNX8J8/NGBTHDIcJQ1bdE5Be8i6EAZ6uqs3FYEbkOL1L6d/91PRG5RFWHuNBX1cDHG1X1htDX/tTMdx3Jv+/PN08RkX8CV+JN+3RC0NNhK2G67SK8JGKfEhIz4DAQ7QFgHNBcRN4CjgEud6R9Zsh2PrDW5YyKSjj2p4Vs5wPvqOoUh/p9gefx4qwUL/nT331D7vpwhIvypLjET/YU+VTDcACLcYgQRCRJVbPFS1e7G6q6wVE9M0qOF4fGUziqoyletr+dhmmQkd/iJfWZraptwtQRPNd1W7zsfII3Bj4h/FburKO0CPBNwC+qOsOBfk28GJYDKd7/rmYOPFBaucsLgz/NsDte/09V1QyH2rF4eTlC+yasKYGVNdsnmhGRCcAF6q+Z4uddeFdVTw1Dc7CqXh90wG44JMela4+6fZxqfpE13GIcjJ28jXdH9Au73J1FKOBqOl2siIj6FqN/InWV+hURGYiXMngOu9LMKl5SGVd1hJ4oYvAWu/ogXF1/iGKMH7DlzFgoQRf/8Zn/+kxgJtBfRD5Q1afC1P8E3xDBkYs/lCDuHEMRkXOAr4sSSolIioicraofO9C+Ac+jsZZdcUOKl8gqHCplto+IHAM8yC6jXPAOW1dTeYOcTpquIQutqepG8RbTCodL8Twhz4SpExyqEbO+hEvMcIgQVPVM/7llwFWNA97z3fEA/8JdSl/wEia1UVXnF60QQk8U+cBSVV3hSHu6iBylqk4XbgqhGV5K6y2w8w7+c7xMjL/gLdgVlr6qnhamxh7xg3XvYPeVCF1NhX1AVYuSb6FeoqMHgI8daN+Ed2yGndAolEoYHipiOHAL3nHicgp1EUFOJy0QkQOKvDviJQ4L1929ENzN6AmMaujVN8MhAhGRc4Fj8f5Yk13cbYVwJ3A1Xlpi8O52GznUX4S3NkKQhsPpqlos6ExEBpYsqyDdgL4ishRvDL/ori7cu9IiGlC8b/KAhqq6TURc9Nn3InKYqs5yoFUabwHv4XlK+uOtibHeoX5p6xe4Ok8tx/PGBELAs30ANqmqSyO/GKpaLFhRRN4BvnMk/2/gOxH5Fu8/dRzeeSgc0veU/AkiIwFUdcUMhwhDRIYABwHv+EX9ReRkVb3Ohb56i0P9iDc/+0IgjfCjm0PZCswQka8IuUA6nhp1MrtHq/cupawiVHjMtYy8BfwoIp/4r88C3vZnb8xxoH8scLl4GSR34N7wqa+qw0XkJv9O71sRcemdmSbeUtQv+a+vw130/CK8tTU+p/ix6eoCE+RsH4BvRORpYDTF2x9UDIWT6aR+PotkvCmY3f3imx3ErsTirUsR1HRdJ6gNVRiVwInAoSExCCOA38MVFZFDgEv8RwbeXWMQbtZP/YdzROQavGxzrUQkND9/XcBJdLmqLvXrakCIK94VqvqIiIwDipIC9VfVomh5F/kRejvQ2BtFU3dXi8gZeK5sl5kvb8BbrOg9//UEPOPBBcv8Rw0cxvUUEfBsH9i1VHRoYJzLGIqSmSOdTCf1b1buUNX3KT4cEi6rXQX9GuXDDIfIYwFwALDUf93cLwuXP4HJwJmqugBARG5xoFuMgKdIvY0Xj/EEcFdI+WaHs07+ihfs1gRvue4WeGmJK5xTvyTqrZy4FN8wCR37rShFs3LwUpQHyaMikoyXFvpFIAm42ZW4quZQ/Ld1RtCBnaWQg7cmgxMqYSpvkNNJvxSR2/AMwtBpvOH8byPa0+ChFuNgBEfITIG6wB8iUrSo1VGAC1fwuXgpZL/x73jfxeEfr0RynZKoOkiZrd5iR5uAS0TkWOBg9daTSBN360k8gudO/VJVjxCRE4C/O9AFSjVMDsAz6sI1TCplVo7uWjNiE3ACgIjcHK6uBLgWRpDaJeoJ1Y/Fy+r4vgttXz/oRbqOAWaoao6I/B1vaOH5Ii9cmFzkP4d6j8I9Lk8K47OVg2KZI41AKW1KUVEQ0cXhivsBlh/7Y+l98O4SG4jIy8BHqjo+zCrOLKVM8Dwmd4epXVw0ZD0JvJU+awBv4iULCpc8Vc0UkRgRiVHVb8RbStoVgRgmlTgrpzQGAM+FqTHSfw5ial2Q2qEEOdsHgl+k62Wgo4h0xPMoDcOLzwh7HZsgjktXXkaj/JjhECGETikSkSOAv+GtbLgYeMVhPTl4d6dv+0lYLsAbxwzLcAi9Kyml/S6DLyHY9SSyRKQO3rDOWyKyjuIn7HAJ1DARkX6qOjzkdSxwb8Bueheeq999z8VBwCxguLrL6hik9k5U9VsRaYS3aqXiTxd0qF8sX4SIPAN84bCKfD+XSR9gsB8E28+FsHiLlQ3AW/X0ahE5GG9qrMuYh8hEq19wZGlTn4wqQEQOEZEHRORPvLHjZXiZPU9Q1ReDqFNVN6rqUFUN2+W3j/YPDruxxcn1g0eDWE+iD97MkJvxcl4sxJv54Ioiw2QSnmHyPG4Nk5NEZIyINBaRDnhpg4NOhezCFzsCz4s0Cy/Ac29JlSJJeycichXwE96w4PnAVBFxkTxpT7hepGuziNyN5wH73J8NEe9I+3Ugl11BwSuBRx1pG5WMeRwih0oJXgyQymx/YOtJ+OO7LfDiJ0b4d0qx4eqGBED2AbbhJfLpizdNzVlkuKr+TUQuwrtI5gB/UwfrGZQScb/zLaBWuPpAOz9jJyIyHO8C7IogtUO5HTiiKMGUeKmzvwdecyEuAS/ShReH8Degn6quEZEDgKcdaVfmqqcRgwJqMQ5GgAQavFgJVFr7VfUZETkZyMaLc7hfHa0n4RsiV+NNMWwNNMUbKgrXK/MxXsbIHBEZparn4d0JO8V3Ad+ENzx0KPAP8dYi2RqObsAR97Brmieqmu/4mhKkdiiZFJ/Vstkvc0Wgi3QBt4QmUVPVZSLiajZRYKueGpWPLXIVYYQEL16CNz/7DdwEL1YKld1+EUkDMtXRgSwiM/DGqH9Uf+EvEZlVdMcahu6vIXo7t13jDxVdp6pf+Xd0A4ArVdXZdNIgEJECdg3ZFHkxtrIrgVVSJGqXqOcN4DC89UIU738w03+ElWjKj1X5XVXbOmjqnuqYrqqdS5TNdJE8zDf078VbV2Y8/qqnqjoxXO1IJklStXvcKU41J+S/Z4tcGcUJKnixsgiy/SLSHXgS2IA3O2EkXubLGBG5VFXHhVsHsENVc4vuSkUkDjdj+LqHbdd09fM54BtTg/xpghGNqoY9HFQV2iVYSPGAyKLsoGF7a1S1QETmusj5URKpnMRqE0RkOrtWPb1JHa56GslUxVCFiJyGt0R6LDBMVZ90qm8eByNaEJFpeDnvk4GhQG9VnSoibYF3XNzFi8hTQBbeyns34J1Q56jqPWHqFt31ht7xguO7Xr+uDnh3dqGLULlKe2zsAz/4FfUXMnOoOwlvNtFPFE+iFFYeCvESetUj2MRqx5dWrqrOVs2NRJIkVbvFnOxU88vC9/fqcfC9U/PwUvOvwMsDdImqukhpD5jHwYgu4oqGPETkYVWdCqCqfzoct74L6IcXXPgvYAzefPawqKy7Xj/HRU88w2EM3iyC73C3XoKxB3yDbSR+Cm4RyQAuVdWwU8b73OdIpxhaPLFaR7zcMeAFO7vKlXB7yHYC3nDgLzhKlx3RVP50zK7AAlVdBCAi7+INm5nhYOyXhP4Dt5V4z4nrTFUL8WZoOJmlUQWcD3QEflXVK0SkIV5yLCN4hgIDVPUbABHpiXccHb2Xz+wTEUnAW4k00DwUInIjXmDwaL/oTREZ6mI6uKoWm9IsIs0JP2mYUTpN8VaCLWIFu9Y5cYIZDkY00VFEsvHd/f42/uuwFqSSvafMxkWAWCWxTb1FhfJFJAkvrXXzqm7UfkLtIqMBQFUnOsoxMgJvZshkPA9SO7yZM665CujmxykhIgOBH/DysrhmBd6sn2rNZjZ+8aV+mOZYNsEfti1iqKoOdVzHXjHDwYgaAnb3nws0pLilDt5Fd02A9bpmmnirMr6K5wregnfyN4JnkYjcx64U13/HW8o7XCorD4UABSGvC3A0pVpEXmSXYR5DSObX6oyqnlYF1a6k+M1CM7/MGWY4GIbHf4C7tcSCPv5d+39wmz0yEPzpl0+oahbwip9PI0lVZ+79k4YjrgQewnP1K56HwEXmyMrKQ/E68KOIfOS/PhsYvufdy8Wf7EqklokXzOxkxoaxGz8DB4tISzyD4WK8xF7OsFkVhgGIyM+qetQe3gs7j0NlEU1trS6UEoPwmqrm7f1T5dKvlDwUfl2dgWP9l5NV9dcw9eLxsk9eCizxixsCL6rqkyLSSVVnhFOHsTsicjpeDEks3vH4mFN9MxwMA0RkvqoevIf3FqjqQZXdpoogIiPwFihysRS7UQZE5D2KxyAsUdWbq7RR5UBEUvf2fjhTMkXkBbw1NW5R1c1+WRLeSqIFwGlaNSu6GmFghoNhACLyDvC1qr5aovwq4GRVvahqWlY+/MyRB+Pd3RXljdAoCu6MOkK9PH7CsJ9KZmCMZERkMd7QigAHABv97RRgWTgXdhFZgLfui5YojwUy8HOxVFTfqBosxsEwPG4GPhKRvnhBheCtqFgDbxnviCYko+CpVd2W/ZDKikEIhCLDQERexUsPP8Z/3RsvziEcCktLB+9nwlxvRkN0Yh4HwwhBRE4AOvgvf1fVr6uyPWUldJ2BkEW0jEqgMmMQgqS0+JhwY2ZE5GNgdMnMpSLyd+ACVe1TUW2j6jDDwTCqAZW1iJZRfRGRL/DiNIoShvUFjlfVCnuxRKQp3iyTbRT35NUCzlFVp9MEjcrBhioMo3pQWYtoGdWXS4AHgKLpmJP8sgrjGwbdROREoGiF1jGq+lU4ukbVYh4Hw6gGVOYiWoZh7N+Y4WAYhmEgIt9QirdKVav/QlRGubChCsMwDAPgtpDtBOA8wPliWkb0Yx4HwzAMo1RE5CdV7VrV7TAiC/M4GIZhGCUzSMYARwLJVdQcI4Ixw8EwDMMAb7pkUQbJfGAx0K9KW2REJDZUYRiGYRhGmYmp6gYYhmEYVY+IJIrIvSIy1H99sIicWdXtMiIPMxwMwzAMgNeBXOBo//VK4NGqa44RqZjhYBiGYQC0VtWn8BftUtWi9TYMoxhmOBiGYRgAuSJSCz8JlIi0BnZUbZOMSMRmVRiGYRjgrVMxDmguIm8BxwCXV2mLjIjEZlUYhmEYAIhIfaA73hDFVFXNqOImGRGIDVUYhmEYiMgxwHZV/RxIAf4tIi2qtlVGJGKGg2EYhgHwMrBVRDoCA4CFwBtV2yQjEjHDwTAMwwDIV2/sug/wkqq+BNSt4jYZEYgFRxqGYRgAm0XkbuDvwPEiEgPEV3GbjAjEPA6GYRgGwEV40y/7qeoaoBnwdNU2yYhEbFaFYRjGfoyIJAD9gYOAWcBwVc2v2lYZkYwZDoZhGPsxIvIeXrbIyUBvYKmq3lS1rTIiGTMcDMMw9mNEZJaqHuZvxwE/qWrnKm6WEcFYjINhGMb+TV7Rhg1RGGXBPA6GYRj7MSJSAOQUvQRqAUULXKmqJlVV24zIxAwHwzAMwzDKjA1VGIZhGIZRZsxwMAzDMAyjzJjhYBhRgogUiMgMEZktIh+ISGIYWv8VkfP97WEi0m4v+/YUkaMrUMcSEUkra3mJfbaUs64HReS28rbRMIzyY4aDYUQP21S1k6p2AHLxkvbsxJ9KV25U9SpVnbOXXXoC5TYcDMOonpjhYBjRyWTgIN8bMFlEPgXmiEisiDwtIj+LyEwR+ReAeAwWkbki8iXQoEhIRCaKSBd/+zQRmS4iv4nIVyJyIJ6Bcovv7ThORNJFZJRfx8/+csyISH0RGS8iv4vIMLyo/L0iIh+LyC/+Z64u8d5//PKvRCTdL2stIuP8z0wWkbZOetMwjDJji1wZRpThexZ6A+P8os5AB1Vd7F98N6nqUSJSE5giIuOBI4A2QDugITAHeK2EbjrwKnC8r5WqqhtE5BVgi6o+4+/3NvAfVf1ORA4AvgAOBR4AvlPVh0XkDKBfGb7OlX4dtYCfRWSUqmYCtYFpqnqLiNzva18PDAX6q+p8EekGDAFOrEA3GoZRQcxwMIzooZaIzPC3JwPD8YYQflLVxX75KcDhRfELQDJwMHA88I6qFgCrROTrUvS7A5OKtFR1wx7a0QtoJ7LToZAkInX8Os71P/u5iGwsw3e6UUTO8beb+23NBAqB9/zyN4HRfh1HAx+E1F2zDHUYhuEQMxwMI3rYpqqdQgv8C2hOaBFwg6p+UWK/0x22IwborqrbS2lLmRGRnnhGSA9V3SoiE4GEPeyufr1ZJfvAMIzKxWIcDKN68QVwjYjEA4jIISJSG5gEXOTHQDQGTijls1OB40Wkpf/ZVL98M1A3ZL/xwA1FL0Skk785CfibX9YbqLePtiYDG32joS2ex6OIGKDIa/I3vCGQbGCxiFzg1yEi0nEfdRiG4RgzHAyjejEML35huojMBv4Pz7P4ETDff+8N4IeSH1TV9cDVeMMCv7FrqOAz4Jyi4EjgRqCLH3w5h12zOx7CMzx+xxuyWLaPto4D4kTkD+BJPMOliBygq/8dTgQe9sv7Av389v0O9ClDnxiG4RBLOW0YhmEYRpkxj4NhGIZhGGXGDAfDMAzDMMqMGQ6GYRiGYZQZMxwMwzAMwygzZjgYhmEYhlFmzHAwDMMwDKPMmOFgGIZhGEaZMcPBMAzDMIwy8/9NZcDz10f+zAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred=clf.predict(X_test)\n",
    "\n",
    "accuracy=accuracy_score(y_pred, y_test)\n",
    "print('LightGBM Model accuracy score: {0:0.4f}'.format(accuracy_score(y_test, y_pred)))\n",
    "print(classification_report(y_test, y_pred, target_names=classes, zero_division=0))\n",
    "# cm = confusion_matrix(y_test, y_pred)\n",
    "# print(cm)\n",
    "fig, ax = plt.subplots(figsize=(8,8))\n",
    "ConfusionMatrixDisplay.from_predictions(y_test, y_pred, display_labels=classes, xticks_rotation='vertical', ax=ax)\n",
    "plt.show()\n",
    "fig, ax = plt.subplots(figsize=(8,8))\n",
    "ConfusionMatrixDisplay.from_predictions(y_test, y_pred, display_labels=classes, xticks_rotation='vertical', normalize='true', include_values=False, ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "809f60bd",
   "metadata": {},
   "source": [
    "### param tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "17a2744b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:39:01.541187Z",
     "start_time": "2022-07-04T19:39:01.539437Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# automl = AutoML()\n",
    "# automl.fit(\n",
    "#     X_train,\n",
    "#     y_train,\n",
    "#     task=\"classification\",\n",
    "#     metric='macro_f1', # 'accuracy', 'micro_f1'\n",
    "#     time_budget=10*60,\n",
    "#     estimator_list=['lgbm'],\n",
    "#     verbose=1,\n",
    "#     n_jobs=min(32, joblib.cpu_count())\n",
    "# )\n",
    "# y_pred=automl.predict(X_test)\n",
    "\n",
    "# print(classification_report(y_test, y_pred, target_names=classes, zero_division=0))\n",
    "# cm = confusion_matrix(y_test, y_pred)\n",
    "# print(cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "124eea01",
   "metadata": {},
   "source": [
    "## simple multilabel lightgbm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "93106a4b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:39:01.544262Z",
     "start_time": "2022-07-04T19:39:01.542272Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train = s2_train.reshape(len(labels_train), -1)\n",
    "y_train = multilabels_train\n",
    "X_test = s2_test.reshape(len(labels_test), -1)\n",
    "y_test = multilabels_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f849e99c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:39:15.756387Z",
     "start_time": "2022-07-04T19:39:01.545311Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OneVsRestClassifier(estimator=LGBMClassifier(n_jobs=10), n_jobs=15)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = ltb.LGBMClassifier(\n",
    "    n_jobs=min(32, joblib.cpu_count()//len(classes)),  # seems more than 32 actually slows things down\n",
    ")  \n",
    "clf = OneVsRestClassifier(clf, n_jobs=len(classes))\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9b2be19d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:39:17.239491Z",
     "start_time": "2022-07-04T19:39:15.757727Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LightGBM Model accuracy score: 0.2522\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       Abies       0.66      0.19      0.29       156\n",
      "        Acer       0.43      0.04      0.07       390\n",
      "       Alnus       0.36      0.01      0.02       418\n",
      "      Betula       0.64      0.02      0.03       430\n",
      "     Cleared       0.75      0.42      0.54       715\n",
      "       Fagus       0.71      0.50      0.58      1434\n",
      "    Fraxinus       0.45      0.04      0.08       338\n",
      "       Larix       0.78      0.09      0.17       637\n",
      "       Picea       0.79      0.59      0.68      1409\n",
      "       Pinus       0.76      0.62      0.68      1355\n",
      "     Populus       0.50      0.03      0.06        96\n",
      "      Prunus       0.33      0.02      0.04        47\n",
      " Pseudotsuga       0.69      0.23      0.35       575\n",
      "     Quercus       0.71      0.42      0.53      1443\n",
      "       Tilia       0.00      0.00      0.00        29\n",
      "\n",
      "   micro avg       0.74      0.37      0.50      9472\n",
      "   macro avg       0.57      0.21      0.27      9472\n",
      "weighted avg       0.69      0.37      0.45      9472\n",
      " samples avg       0.61      0.45      0.49      9472\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>mAP</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Abies</th>\n",
       "      <td>0.6591</td>\n",
       "      <td>0.1859</td>\n",
       "      <td>0.2900</td>\n",
       "      <td>0.3879</td>\n",
       "      <td>156.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Acer</th>\n",
       "      <td>0.4286</td>\n",
       "      <td>0.0385</td>\n",
       "      <td>0.0706</td>\n",
       "      <td>0.2771</td>\n",
       "      <td>390.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alnus</th>\n",
       "      <td>0.3571</td>\n",
       "      <td>0.0120</td>\n",
       "      <td>0.0231</td>\n",
       "      <td>0.1964</td>\n",
       "      <td>418.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Betula</th>\n",
       "      <td>0.6364</td>\n",
       "      <td>0.0163</td>\n",
       "      <td>0.0317</td>\n",
       "      <td>0.2365</td>\n",
       "      <td>430.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cleared</th>\n",
       "      <td>0.7500</td>\n",
       "      <td>0.4238</td>\n",
       "      <td>0.5416</td>\n",
       "      <td>0.6342</td>\n",
       "      <td>715.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fagus</th>\n",
       "      <td>0.7072</td>\n",
       "      <td>0.4951</td>\n",
       "      <td>0.5824</td>\n",
       "      <td>0.6995</td>\n",
       "      <td>1434.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fraxinus</th>\n",
       "      <td>0.4516</td>\n",
       "      <td>0.0414</td>\n",
       "      <td>0.0759</td>\n",
       "      <td>0.2729</td>\n",
       "      <td>338.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Larix</th>\n",
       "      <td>0.7792</td>\n",
       "      <td>0.0942</td>\n",
       "      <td>0.1681</td>\n",
       "      <td>0.3639</td>\n",
       "      <td>637.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Picea</th>\n",
       "      <td>0.7947</td>\n",
       "      <td>0.5905</td>\n",
       "      <td>0.6775</td>\n",
       "      <td>0.7835</td>\n",
       "      <td>1409.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pinus</th>\n",
       "      <td>0.7647</td>\n",
       "      <td>0.6162</td>\n",
       "      <td>0.6825</td>\n",
       "      <td>0.7889</td>\n",
       "      <td>1355.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Populus</th>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.0312</td>\n",
       "      <td>0.0588</td>\n",
       "      <td>0.1485</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prunus</th>\n",
       "      <td>0.3333</td>\n",
       "      <td>0.0213</td>\n",
       "      <td>0.0400</td>\n",
       "      <td>0.0569</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pseudotsuga</th>\n",
       "      <td>0.6856</td>\n",
       "      <td>0.2313</td>\n",
       "      <td>0.3459</td>\n",
       "      <td>0.4806</td>\n",
       "      <td>575.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quercus</th>\n",
       "      <td>0.7111</td>\n",
       "      <td>0.4179</td>\n",
       "      <td>0.5264</td>\n",
       "      <td>0.6710</td>\n",
       "      <td>1443.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tilia</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0463</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>micro avg</th>\n",
       "      <td>0.7376</td>\n",
       "      <td>0.3748</td>\n",
       "      <td>0.4970</td>\n",
       "      <td>0.6142</td>\n",
       "      <td>9472.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macro avg</th>\n",
       "      <td>0.5706</td>\n",
       "      <td>0.2144</td>\n",
       "      <td>0.2743</td>\n",
       "      <td>0.4030</td>\n",
       "      <td>9472.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weighted avg</th>\n",
       "      <td>0.6896</td>\n",
       "      <td>0.3748</td>\n",
       "      <td>0.4536</td>\n",
       "      <td>0.5879</td>\n",
       "      <td>9472.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>samples avg</th>\n",
       "      <td>0.6136</td>\n",
       "      <td>0.4463</td>\n",
       "      <td>0.4920</td>\n",
       "      <td>0.7313</td>\n",
       "      <td>9472.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              precision  recall  f1-score     mAP  support\n",
       "Abies            0.6591  0.1859    0.2900  0.3879    156.0\n",
       "Acer             0.4286  0.0385    0.0706  0.2771    390.0\n",
       "Alnus            0.3571  0.0120    0.0231  0.1964    418.0\n",
       "Betula           0.6364  0.0163    0.0317  0.2365    430.0\n",
       "Cleared          0.7500  0.4238    0.5416  0.6342    715.0\n",
       "Fagus            0.7072  0.4951    0.5824  0.6995   1434.0\n",
       "Fraxinus         0.4516  0.0414    0.0759  0.2729    338.0\n",
       "Larix            0.7792  0.0942    0.1681  0.3639    637.0\n",
       "Picea            0.7947  0.5905    0.6775  0.7835   1409.0\n",
       "Pinus            0.7647  0.6162    0.6825  0.7889   1355.0\n",
       "Populus          0.5000  0.0312    0.0588  0.1485     96.0\n",
       "Prunus           0.3333  0.0213    0.0400  0.0569     47.0\n",
       "Pseudotsuga      0.6856  0.2313    0.3459  0.4806    575.0\n",
       "Quercus          0.7111  0.4179    0.5264  0.6710   1443.0\n",
       "Tilia            0.0000  0.0000    0.0000  0.0463     29.0\n",
       "micro avg        0.7376  0.3748    0.4970  0.6142   9472.0\n",
       "macro avg        0.5706  0.2144    0.2743  0.4030   9472.0\n",
       "weighted avg     0.6896  0.3748    0.4536  0.5879   9472.0\n",
       "samples avg      0.6136  0.4463    0.4920  0.7313   9472.0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = clf.predict(X_test)\n",
    "y_score = clf.predict_proba(X_test)\n",
    "\n",
    "accuracy=accuracy_score(y_pred, y_test)\n",
    "print('LightGBM Model accuracy score: {0:0.4f}'.format(accuracy_score(y_test, y_pred)))\n",
    "print(classification_report(y_test, y_pred, target_names=classes, zero_division=0))\n",
    "cr = classification_report(y_test, y_pred, target_names=classes, zero_division=0, output_dict=True)\n",
    "df = pd.DataFrame(cr).transpose()\n",
    "df.insert(3, 'mAP', list(average_precision_score(y_test, y_score, average=None)) + [\n",
    "    average_precision_score(y_test, y_score, average=n) for n in ['micro', 'macro', 'weighted', 'samples']\n",
    "])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "369777b4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:39:17.244260Z",
     "start_time": "2022-07-04T19:39:17.240530Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n",
       "       [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e4d6f5a1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:39:17.248575Z",
     "start_time": "2022-07-04T19:39:17.245157Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0. , 0. , 0. , 0. , 0.1, 0.1, 0. , 0.1, 0.5, 0.2, 0. , 0. , 0.1,\n",
       "        0.1, 0. ],\n",
       "       [0. , 0. , 0. , 0.1, 0.1, 0. , 0. , 0.3, 0.1, 0.7, 0. , 0. , 0.1,\n",
       "        0.2, 0. ],\n",
       "       [0.5, 0. , 0. , 0. , 0. , 0.1, 0. , 0.1, 0.2, 0.2, 0. , 0. , 0.8,\n",
       "        0.1, 0. ],\n",
       "       [0. , 0.1, 0.1, 0.1, 0.1, 0.5, 0. , 0.1, 0.5, 0.1, 0. , 0. , 0.1,\n",
       "        0.1, 0. ],\n",
       "       [0.9, 0. , 0. , 0. , 0. , 0. , 0. , 0.2, 0. , 0.1, 0. , 0. , 0.6,\n",
       "        0.1, 0. ],\n",
       "       [0.2, 0. , 0. , 0.1, 0.1, 0.1, 0.1, 0.2, 0.2, 0.2, 0. , 0. , 0.2,\n",
       "        0.4, 0. ],\n",
       "       [0.6, 0.1, 0. , 0.2, 0.1, 0.2, 0. , 0.2, 0.4, 0.3, 0. , 0. , 0.5,\n",
       "        0.4, 0. ],\n",
       "       [0.7, 0. , 0. , 0. , 0. , 0.1, 0. , 0.1, 0.2, 0.2, 0. , 0. , 0.8,\n",
       "        0.1, 0. ],\n",
       "       [0.1, 0. , 0.1, 0.1, 0. , 0.1, 0. , 0.1, 0.1, 0.4, 0. , 0. , 0.2,\n",
       "        0.4, 0. ],\n",
       "       [0.8, 0. , 0. , 0.1, 0. , 0.2, 0. , 0.1, 0.1, 0.2, 0. , 0. , 0.8,\n",
       "        0.3, 0. ]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_score[:10].round(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4020a430",
   "metadata": {},
   "source": [
    "### param tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c748b6b1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:39:17.251214Z",
     "start_time": "2022-07-04T19:39:17.249415Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# automl = AutoML(\n",
    "#     time_budget=30,\n",
    "#     estimator_list=['lgbm'],\n",
    "#     n_jobs=min(32, joblib.cpu_count() // len(classes)),\n",
    "# )\n",
    "# automl = OneVsRestClassifier(automl, n_jobs=len(classes))\n",
    "# automl.fit(\n",
    "#     X_train,\n",
    "#     y_train,\n",
    "# )\n",
    "\n",
    "# y_pred = automl.predict(X_test)\n",
    "# y_score = automl.predict_proba(X_test)\n",
    "\n",
    "# accuracy=accuracy_score(y_pred, y_test)\n",
    "# print('LightGBM Model accuracy score: {0:0.4f}'.format(accuracy_score(y_test, y_pred)))\n",
    "# print(classification_report(y_test, y_pred, target_names=classes, zero_division=0))\n",
    "# cr = classification_report(y_test, y_pred, target_names=classes, zero_division=0, output_dict=True)\n",
    "# df = pd.DataFrame(cr).transpose()\n",
    "# df.insert(3, 'mAP', list(average_precision_score(y_test, y_score, average=None)) + [\n",
    "#     average_precision_score(y_test, y_score, average=n) for n in ['micro', 'macro', 'weighted', 'samples']\n",
    "# ])\n",
    "# df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6e43236",
   "metadata": {},
   "source": [
    "## training helpers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "16dcd528",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T19:39:17.256977Z",
     "start_time": "2022-07-04T19:39:17.252198Z"
    }
   },
   "outputs": [],
   "source": [
    "def train_and_eval(name, X_train, y_train, X_test, y_test, time_budget=TIME_BUDGET, verbose=0):\n",
    "    automl = AutoML(\n",
    "        time_budget=time_budget,\n",
    "        verbose=verbose,\n",
    "        estimator_list=['lgbm'],\n",
    "        n_jobs=min(32, joblib.cpu_count() // len(classes)),  # gets slower if more than 32...\n",
    "    )\n",
    "    automl = OneVsRestClassifier(automl, n_jobs=len(classes))\n",
    "    automl.fit(\n",
    "        X_train,\n",
    "        y_train,\n",
    "    )\n",
    "    with shelve.open(\"lightgbm_bestconfigs.shelve\") as all_res:\n",
    "        all_res[name] = [clf.best_config for clf in automl.estimators_]\n",
    "        \n",
    "    y_pred = automl.predict(X_test)\n",
    "    y_score = automl.predict_proba(X_test)\n",
    "\n",
    "    accuracy=accuracy_score(y_pred, y_test)\n",
    "    #print('LightGBM Model accuracy score: {0:0.4f}'.format(accuracy_score(y_test, y_pred)))\n",
    "    #print(classification_report(y_test, y_pred, target_names=classes, zero_division=0))\n",
    "    cr = classification_report(y_test, y_pred, target_names=classes, zero_division=0, output_dict=True)\n",
    "    df = pd.DataFrame(cr).transpose()\n",
    "    df.insert(3, 'mAP', list(average_precision_score(y_test, y_score, average=None)) + [\n",
    "        average_precision_score(y_test, y_score, average=n) for n in ['micro', 'macro', 'weighted', 'samples']\n",
    "    ])\n",
    "    with shelve.open(\"lightgbm_res.shelve\") as all_res:\n",
    "        all_res[name] = df\n",
    "    display(df.iloc[:-4])\n",
    "    display(df.iloc[-4:])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9678a5e",
   "metadata": {},
   "source": [
    "## individual"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94ebca5b",
   "metadata": {},
   "source": [
    "### S1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "7a79b996",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T20:11:32.632163Z",
     "start_time": "2022-07-04T19:39:17.257797Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>mAP</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Abies</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0648</td>\n",
       "      <td>156.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Acer</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1108</td>\n",
       "      <td>390.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alnus</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1058</td>\n",
       "      <td>418.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Betula</th>\n",
       "      <td>0.0662</td>\n",
       "      <td>0.0233</td>\n",
       "      <td>0.0344</td>\n",
       "      <td>0.0914</td>\n",
       "      <td>430.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cleared</th>\n",
       "      <td>0.6364</td>\n",
       "      <td>0.1762</td>\n",
       "      <td>0.2760</td>\n",
       "      <td>0.4061</td>\n",
       "      <td>715.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fagus</th>\n",
       "      <td>0.5714</td>\n",
       "      <td>0.0446</td>\n",
       "      <td>0.0828</td>\n",
       "      <td>0.3921</td>\n",
       "      <td>1434.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fraxinus</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1152</td>\n",
       "      <td>338.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Larix</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1589</td>\n",
       "      <td>637.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Picea</th>\n",
       "      <td>0.5472</td>\n",
       "      <td>0.0617</td>\n",
       "      <td>0.1110</td>\n",
       "      <td>0.4181</td>\n",
       "      <td>1409.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pinus</th>\n",
       "      <td>0.6410</td>\n",
       "      <td>0.3373</td>\n",
       "      <td>0.4420</td>\n",
       "      <td>0.5659</td>\n",
       "      <td>1355.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Populus</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0302</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prunus</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0106</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pseudotsuga</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1750</td>\n",
       "      <td>575.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quercus</th>\n",
       "      <td>0.4524</td>\n",
       "      <td>0.0132</td>\n",
       "      <td>0.0256</td>\n",
       "      <td>0.3574</td>\n",
       "      <td>1443.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tilia</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0075</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             precision  recall  f1-score     mAP  support\n",
       "Abies           0.0000  0.0000    0.0000  0.0648    156.0\n",
       "Acer            0.0000  0.0000    0.0000  0.1108    390.0\n",
       "Alnus           0.0000  0.0000    0.0000  0.1058    418.0\n",
       "Betula          0.0662  0.0233    0.0344  0.0914    430.0\n",
       "Cleared         0.6364  0.1762    0.2760  0.4061    715.0\n",
       "Fagus           0.5714  0.0446    0.0828  0.3921   1434.0\n",
       "Fraxinus        0.0000  0.0000    0.0000  0.1152    338.0\n",
       "Larix           0.0000  0.0000    0.0000  0.1589    637.0\n",
       "Picea           0.5472  0.0617    0.1110  0.4181   1409.0\n",
       "Pinus           0.6410  0.3373    0.4420  0.5659   1355.0\n",
       "Populus         0.0000  0.0000    0.0000  0.0302     96.0\n",
       "Prunus          0.0000  0.0000    0.0000  0.0106     47.0\n",
       "Pseudotsuga     0.0000  0.0000    0.0000  0.1750    575.0\n",
       "Quercus         0.4524  0.0132    0.0256  0.3574   1443.0\n",
       "Tilia           0.0000  0.0000    0.0000  0.0075     29.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>mAP</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>micro avg</th>\n",
       "      <td>0.5549</td>\n",
       "      <td>0.0806</td>\n",
       "      <td>0.1407</td>\n",
       "      <td>0.3511</td>\n",
       "      <td>9472.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macro avg</th>\n",
       "      <td>0.1943</td>\n",
       "      <td>0.0438</td>\n",
       "      <td>0.0648</td>\n",
       "      <td>0.2007</td>\n",
       "      <td>9472.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weighted avg</th>\n",
       "      <td>0.3796</td>\n",
       "      <td>0.0806</td>\n",
       "      <td>0.1186</td>\n",
       "      <td>0.3279</td>\n",
       "      <td>9472.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>samples avg</th>\n",
       "      <td>0.1409</td>\n",
       "      <td>0.0994</td>\n",
       "      <td>0.1108</td>\n",
       "      <td>0.5474</td>\n",
       "      <td>9472.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              precision  recall  f1-score     mAP  support\n",
       "micro avg        0.5549  0.0806    0.1407  0.3511   9472.0\n",
       "macro avg        0.1943  0.0438    0.0648  0.2007   9472.0\n",
       "weighted avg     0.3796  0.0806    0.1186  0.3279   9472.0\n",
       "samples avg      0.1409  0.0994    0.1108  0.5474   9472.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train = s1_train.reshape(len(labels_train), -1)\n",
    "y_train = multilabels_train\n",
    "X_test = s1_test.reshape(len(labels_test), -1)\n",
    "y_test = multilabels_test\n",
    "train_and_eval('S1', X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0d32ea5",
   "metadata": {},
   "source": [
    "### S2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "06525a01",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T20:47:04.573588Z",
     "start_time": "2022-07-04T20:11:32.633580Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>mAP</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Abies</th>\n",
       "      <td>0.7941</td>\n",
       "      <td>0.1731</td>\n",
       "      <td>0.2842</td>\n",
       "      <td>0.4571</td>\n",
       "      <td>156.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Acer</th>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.0359</td>\n",
       "      <td>0.0670</td>\n",
       "      <td>0.3052</td>\n",
       "      <td>390.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alnus</th>\n",
       "      <td>0.5600</td>\n",
       "      <td>0.0335</td>\n",
       "      <td>0.0632</td>\n",
       "      <td>0.2284</td>\n",
       "      <td>418.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Betula</th>\n",
       "      <td>0.7179</td>\n",
       "      <td>0.0651</td>\n",
       "      <td>0.1194</td>\n",
       "      <td>0.3181</td>\n",
       "      <td>430.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cleared</th>\n",
       "      <td>0.7790</td>\n",
       "      <td>0.4042</td>\n",
       "      <td>0.5322</td>\n",
       "      <td>0.6446</td>\n",
       "      <td>715.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fagus</th>\n",
       "      <td>0.7238</td>\n",
       "      <td>0.5537</td>\n",
       "      <td>0.6274</td>\n",
       "      <td>0.7304</td>\n",
       "      <td>1434.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fraxinus</th>\n",
       "      <td>0.5238</td>\n",
       "      <td>0.0651</td>\n",
       "      <td>0.1158</td>\n",
       "      <td>0.2956</td>\n",
       "      <td>338.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Larix</th>\n",
       "      <td>0.9103</td>\n",
       "      <td>0.1115</td>\n",
       "      <td>0.1986</td>\n",
       "      <td>0.4240</td>\n",
       "      <td>637.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Picea</th>\n",
       "      <td>0.8114</td>\n",
       "      <td>0.6288</td>\n",
       "      <td>0.7085</td>\n",
       "      <td>0.8089</td>\n",
       "      <td>1409.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pinus</th>\n",
       "      <td>0.7885</td>\n",
       "      <td>0.6384</td>\n",
       "      <td>0.7055</td>\n",
       "      <td>0.8145</td>\n",
       "      <td>1355.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Populus</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0104</td>\n",
       "      <td>0.0206</td>\n",
       "      <td>0.2967</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prunus</th>\n",
       "      <td>0.3750</td>\n",
       "      <td>0.0638</td>\n",
       "      <td>0.1091</td>\n",
       "      <td>0.1061</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pseudotsuga</th>\n",
       "      <td>0.7865</td>\n",
       "      <td>0.2435</td>\n",
       "      <td>0.3718</td>\n",
       "      <td>0.5332</td>\n",
       "      <td>575.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quercus</th>\n",
       "      <td>0.7237</td>\n",
       "      <td>0.4428</td>\n",
       "      <td>0.5494</td>\n",
       "      <td>0.6817</td>\n",
       "      <td>1443.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tilia</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1296</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             precision  recall  f1-score     mAP  support\n",
       "Abies           0.7941  0.1731    0.2842  0.4571    156.0\n",
       "Acer            0.5000  0.0359    0.0670  0.3052    390.0\n",
       "Alnus           0.5600  0.0335    0.0632  0.2284    418.0\n",
       "Betula          0.7179  0.0651    0.1194  0.3181    430.0\n",
       "Cleared         0.7790  0.4042    0.5322  0.6446    715.0\n",
       "Fagus           0.7238  0.5537    0.6274  0.7304   1434.0\n",
       "Fraxinus        0.5238  0.0651    0.1158  0.2956    338.0\n",
       "Larix           0.9103  0.1115    0.1986  0.4240    637.0\n",
       "Picea           0.8114  0.6288    0.7085  0.8089   1409.0\n",
       "Pinus           0.7885  0.6384    0.7055  0.8145   1355.0\n",
       "Populus         1.0000  0.0104    0.0206  0.2967     96.0\n",
       "Prunus          0.3750  0.0638    0.1091  0.1061     47.0\n",
       "Pseudotsuga     0.7865  0.2435    0.3718  0.5332    575.0\n",
       "Quercus         0.7237  0.4428    0.5494  0.6817   1443.0\n",
       "Tilia           0.0000  0.0000    0.0000  0.1296     29.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>mAP</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>micro avg</th>\n",
       "      <td>0.7627</td>\n",
       "      <td>0.4004</td>\n",
       "      <td>0.5252</td>\n",
       "      <td>0.6165</td>\n",
       "      <td>9472.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macro avg</th>\n",
       "      <td>0.6663</td>\n",
       "      <td>0.2313</td>\n",
       "      <td>0.2982</td>\n",
       "      <td>0.4516</td>\n",
       "      <td>9472.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weighted avg</th>\n",
       "      <td>0.7427</td>\n",
       "      <td>0.4004</td>\n",
       "      <td>0.4817</td>\n",
       "      <td>0.6199</td>\n",
       "      <td>9472.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>samples avg</th>\n",
       "      <td>0.6490</td>\n",
       "      <td>0.4739</td>\n",
       "      <td>0.5220</td>\n",
       "      <td>0.7256</td>\n",
       "      <td>9472.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              precision  recall  f1-score     mAP  support\n",
       "micro avg        0.7627  0.4004    0.5252  0.6165   9472.0\n",
       "macro avg        0.6663  0.2313    0.2982  0.4516   9472.0\n",
       "weighted avg     0.7427  0.4004    0.4817  0.6199   9472.0\n",
       "samples avg      0.6490  0.4739    0.5220  0.7256   9472.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train = s2_train.reshape(len(labels_train), -1)\n",
    "y_train = multilabels_train\n",
    "X_test = s2_test.reshape(len(labels_test), -1)\n",
    "y_test = multilabels_test\n",
    "train_and_eval('S2', X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c9e4c4c",
   "metadata": {},
   "source": [
    "### Aerial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "e9c69670",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T21:26:38.412029Z",
     "start_time": "2022-07-04T20:47:04.574905Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>mAP</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Abies</th>\n",
       "      <td>0.9375</td>\n",
       "      <td>0.1923</td>\n",
       "      <td>0.3191</td>\n",
       "      <td>0.4558</td>\n",
       "      <td>156.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Acer</th>\n",
       "      <td>0.5294</td>\n",
       "      <td>0.0231</td>\n",
       "      <td>0.0442</td>\n",
       "      <td>0.3108</td>\n",
       "      <td>390.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alnus</th>\n",
       "      <td>0.7188</td>\n",
       "      <td>0.0550</td>\n",
       "      <td>0.1022</td>\n",
       "      <td>0.3207</td>\n",
       "      <td>418.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Betula</th>\n",
       "      <td>0.7917</td>\n",
       "      <td>0.0884</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>0.3991</td>\n",
       "      <td>430.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cleared</th>\n",
       "      <td>0.8541</td>\n",
       "      <td>0.4503</td>\n",
       "      <td>0.5897</td>\n",
       "      <td>0.6806</td>\n",
       "      <td>715.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fagus</th>\n",
       "      <td>0.7364</td>\n",
       "      <td>0.4247</td>\n",
       "      <td>0.5387</td>\n",
       "      <td>0.6820</td>\n",
       "      <td>1434.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fraxinus</th>\n",
       "      <td>0.5263</td>\n",
       "      <td>0.0296</td>\n",
       "      <td>0.0560</td>\n",
       "      <td>0.3084</td>\n",
       "      <td>338.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Larix</th>\n",
       "      <td>0.7667</td>\n",
       "      <td>0.0722</td>\n",
       "      <td>0.1320</td>\n",
       "      <td>0.3845</td>\n",
       "      <td>637.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Picea</th>\n",
       "      <td>0.8163</td>\n",
       "      <td>0.5202</td>\n",
       "      <td>0.6355</td>\n",
       "      <td>0.7493</td>\n",
       "      <td>1409.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pinus</th>\n",
       "      <td>0.8298</td>\n",
       "      <td>0.5469</td>\n",
       "      <td>0.6593</td>\n",
       "      <td>0.7880</td>\n",
       "      <td>1355.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Populus</th>\n",
       "      <td>0.3333</td>\n",
       "      <td>0.0104</td>\n",
       "      <td>0.0202</td>\n",
       "      <td>0.2717</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prunus</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0426</td>\n",
       "      <td>0.0816</td>\n",
       "      <td>0.1253</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pseudotsuga</th>\n",
       "      <td>0.8582</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>0.3244</td>\n",
       "      <td>0.4968</td>\n",
       "      <td>575.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quercus</th>\n",
       "      <td>0.7181</td>\n",
       "      <td>0.3742</td>\n",
       "      <td>0.4920</td>\n",
       "      <td>0.6438</td>\n",
       "      <td>1443.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tilia</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1791</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             precision  recall  f1-score     mAP  support\n",
       "Abies           0.9375  0.1923    0.3191  0.4558    156.0\n",
       "Acer            0.5294  0.0231    0.0442  0.3108    390.0\n",
       "Alnus           0.7188  0.0550    0.1022  0.3207    418.0\n",
       "Betula          0.7917  0.0884    0.1590  0.3991    430.0\n",
       "Cleared         0.8541  0.4503    0.5897  0.6806    715.0\n",
       "Fagus           0.7364  0.4247    0.5387  0.6820   1434.0\n",
       "Fraxinus        0.5263  0.0296    0.0560  0.3084    338.0\n",
       "Larix           0.7667  0.0722    0.1320  0.3845    637.0\n",
       "Picea           0.8163  0.5202    0.6355  0.7493   1409.0\n",
       "Pinus           0.8298  0.5469    0.6593  0.7880   1355.0\n",
       "Populus         0.3333  0.0104    0.0202  0.2717     96.0\n",
       "Prunus          1.0000  0.0426    0.0816  0.1253     47.0\n",
       "Pseudotsuga     0.8582  0.2000    0.3244  0.4968    575.0\n",
       "Quercus         0.7181  0.3742    0.4920  0.6438   1443.0\n",
       "Tilia           0.0000  0.0000    0.0000  0.1791     29.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>mAP</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>micro avg</th>\n",
       "      <td>0.7863</td>\n",
       "      <td>0.3398</td>\n",
       "      <td>0.4746</td>\n",
       "      <td>0.5798</td>\n",
       "      <td>9472.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macro avg</th>\n",
       "      <td>0.6944</td>\n",
       "      <td>0.2020</td>\n",
       "      <td>0.2769</td>\n",
       "      <td>0.4531</td>\n",
       "      <td>9472.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weighted avg</th>\n",
       "      <td>0.7612</td>\n",
       "      <td>0.3398</td>\n",
       "      <td>0.4398</td>\n",
       "      <td>0.6004</td>\n",
       "      <td>9472.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>samples avg</th>\n",
       "      <td>0.5858</td>\n",
       "      <td>0.4144</td>\n",
       "      <td>0.4627</td>\n",
       "      <td>0.7018</td>\n",
       "      <td>9472.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              precision  recall  f1-score     mAP  support\n",
       "micro avg        0.7863  0.3398    0.4746  0.5798   9472.0\n",
       "macro avg        0.6944  0.2020    0.2769  0.4531   9472.0\n",
       "weighted avg     0.7612  0.3398    0.4398  0.6004   9472.0\n",
       "samples avg      0.5858  0.4144    0.4627  0.7018   9472.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train = feats_aerial_train\n",
    "y_train = multilabels_train\n",
    "X_test = feats_aerial_test\n",
    "y_test = multilabels_test\n",
    "train_and_eval('A', X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0334f00",
   "metadata": {},
   "source": [
    "## combined"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95931370",
   "metadata": {},
   "source": [
    "### S1 + S2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "61f33395",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T22:09:40.427670Z",
     "start_time": "2022-07-04T21:26:38.413518Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>mAP</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Abies</th>\n",
       "      <td>0.9167</td>\n",
       "      <td>0.1410</td>\n",
       "      <td>0.2444</td>\n",
       "      <td>0.4512</td>\n",
       "      <td>156.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Acer</th>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.0282</td>\n",
       "      <td>0.0534</td>\n",
       "      <td>0.2768</td>\n",
       "      <td>390.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alnus</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0120</td>\n",
       "      <td>0.0236</td>\n",
       "      <td>0.2948</td>\n",
       "      <td>418.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Betula</th>\n",
       "      <td>0.6061</td>\n",
       "      <td>0.0465</td>\n",
       "      <td>0.0864</td>\n",
       "      <td>0.2481</td>\n",
       "      <td>430.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cleared</th>\n",
       "      <td>0.7912</td>\n",
       "      <td>0.4028</td>\n",
       "      <td>0.5338</td>\n",
       "      <td>0.6527</td>\n",
       "      <td>715.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fagus</th>\n",
       "      <td>0.7353</td>\n",
       "      <td>0.5404</td>\n",
       "      <td>0.6230</td>\n",
       "      <td>0.7369</td>\n",
       "      <td>1434.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fraxinus</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2536</td>\n",
       "      <td>338.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Larix</th>\n",
       "      <td>0.8947</td>\n",
       "      <td>0.1068</td>\n",
       "      <td>0.1907</td>\n",
       "      <td>0.4286</td>\n",
       "      <td>637.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Picea</th>\n",
       "      <td>0.8196</td>\n",
       "      <td>0.6125</td>\n",
       "      <td>0.7011</td>\n",
       "      <td>0.8027</td>\n",
       "      <td>1409.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pinus</th>\n",
       "      <td>0.7982</td>\n",
       "      <td>0.6568</td>\n",
       "      <td>0.7206</td>\n",
       "      <td>0.8210</td>\n",
       "      <td>1355.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Populus</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0104</td>\n",
       "      <td>0.0206</td>\n",
       "      <td>0.3147</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prunus</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0385</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pseudotsuga</th>\n",
       "      <td>0.7772</td>\n",
       "      <td>0.2609</td>\n",
       "      <td>0.3906</td>\n",
       "      <td>0.5438</td>\n",
       "      <td>575.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quercus</th>\n",
       "      <td>0.7485</td>\n",
       "      <td>0.4373</td>\n",
       "      <td>0.5521</td>\n",
       "      <td>0.6919</td>\n",
       "      <td>1443.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tilia</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0612</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             precision  recall  f1-score     mAP  support\n",
       "Abies           0.9167  0.1410    0.2444  0.4512    156.0\n",
       "Acer            0.5000  0.0282    0.0534  0.2768    390.0\n",
       "Alnus           1.0000  0.0120    0.0236  0.2948    418.0\n",
       "Betula          0.6061  0.0465    0.0864  0.2481    430.0\n",
       "Cleared         0.7912  0.4028    0.5338  0.6527    715.0\n",
       "Fagus           0.7353  0.5404    0.6230  0.7369   1434.0\n",
       "Fraxinus        0.0000  0.0000    0.0000  0.2536    338.0\n",
       "Larix           0.8947  0.1068    0.1907  0.4286    637.0\n",
       "Picea           0.8196  0.6125    0.7011  0.8027   1409.0\n",
       "Pinus           0.7982  0.6568    0.7206  0.8210   1355.0\n",
       "Populus         1.0000  0.0104    0.0206  0.3147     96.0\n",
       "Prunus          0.0000  0.0000    0.0000  0.0385     47.0\n",
       "Pseudotsuga     0.7772  0.2609    0.3906  0.5438    575.0\n",
       "Quercus         0.7485  0.4373    0.5521  0.6919   1443.0\n",
       "Tilia           0.0000  0.0000    0.0000  0.0612     29.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>mAP</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>micro avg</th>\n",
       "      <td>0.7758</td>\n",
       "      <td>0.3932</td>\n",
       "      <td>0.5219</td>\n",
       "      <td>0.6010</td>\n",
       "      <td>9472.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macro avg</th>\n",
       "      <td>0.6392</td>\n",
       "      <td>0.2170</td>\n",
       "      <td>0.2760</td>\n",
       "      <td>0.4411</td>\n",
       "      <td>9472.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weighted avg</th>\n",
       "      <td>0.7460</td>\n",
       "      <td>0.3932</td>\n",
       "      <td>0.4740</td>\n",
       "      <td>0.6206</td>\n",
       "      <td>9472.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>samples avg</th>\n",
       "      <td>0.6469</td>\n",
       "      <td>0.4653</td>\n",
       "      <td>0.5160</td>\n",
       "      <td>0.7129</td>\n",
       "      <td>9472.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              precision  recall  f1-score     mAP  support\n",
       "micro avg        0.7758  0.3932    0.5219  0.6010   9472.0\n",
       "macro avg        0.6392  0.2170    0.2760  0.4411   9472.0\n",
       "weighted avg     0.7460  0.3932    0.4740  0.6206   9472.0\n",
       "samples avg      0.6469  0.4653    0.5160  0.7129   9472.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train = np.hstack((s1_train.reshape(len(labels_train), -1), s2_train.reshape(len(labels_train), -1)))\n",
    "y_train = multilabels_train\n",
    "X_test = np.hstack((s1_test.reshape(len(labels_test), -1), s2_test.reshape(len(labels_test), -1)))\n",
    "y_test = multilabels_test\n",
    "train_and_eval('S1 + S2', X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ff8661a",
   "metadata": {},
   "source": [
    "### Aerial + S1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "7ad3d3a9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T22:47:43.270519Z",
     "start_time": "2022-07-04T22:09:40.429091Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>mAP</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Abies</th>\n",
       "      <td>0.8333</td>\n",
       "      <td>0.2564</td>\n",
       "      <td>0.3922</td>\n",
       "      <td>0.4383</td>\n",
       "      <td>156.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Acer</th>\n",
       "      <td>0.5152</td>\n",
       "      <td>0.0436</td>\n",
       "      <td>0.0804</td>\n",
       "      <td>0.2777</td>\n",
       "      <td>390.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alnus</th>\n",
       "      <td>0.8667</td>\n",
       "      <td>0.0311</td>\n",
       "      <td>0.0600</td>\n",
       "      <td>0.2473</td>\n",
       "      <td>418.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Betula</th>\n",
       "      <td>0.7917</td>\n",
       "      <td>0.1326</td>\n",
       "      <td>0.2271</td>\n",
       "      <td>0.3885</td>\n",
       "      <td>430.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cleared</th>\n",
       "      <td>0.8380</td>\n",
       "      <td>0.4196</td>\n",
       "      <td>0.5592</td>\n",
       "      <td>0.6495</td>\n",
       "      <td>715.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fagus</th>\n",
       "      <td>0.7476</td>\n",
       "      <td>0.4421</td>\n",
       "      <td>0.5557</td>\n",
       "      <td>0.6865</td>\n",
       "      <td>1434.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fraxinus</th>\n",
       "      <td>0.4490</td>\n",
       "      <td>0.0651</td>\n",
       "      <td>0.1137</td>\n",
       "      <td>0.2633</td>\n",
       "      <td>338.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Larix</th>\n",
       "      <td>0.8125</td>\n",
       "      <td>0.1020</td>\n",
       "      <td>0.1813</td>\n",
       "      <td>0.4040</td>\n",
       "      <td>637.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Picea</th>\n",
       "      <td>0.7972</td>\n",
       "      <td>0.4493</td>\n",
       "      <td>0.5747</td>\n",
       "      <td>0.7059</td>\n",
       "      <td>1409.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pinus</th>\n",
       "      <td>0.8365</td>\n",
       "      <td>0.5852</td>\n",
       "      <td>0.6887</td>\n",
       "      <td>0.8084</td>\n",
       "      <td>1355.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Populus</th>\n",
       "      <td>0.8333</td>\n",
       "      <td>0.0521</td>\n",
       "      <td>0.0980</td>\n",
       "      <td>0.2810</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prunus</th>\n",
       "      <td>0.2000</td>\n",
       "      <td>0.0213</td>\n",
       "      <td>0.0385</td>\n",
       "      <td>0.0816</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pseudotsuga</th>\n",
       "      <td>0.7385</td>\n",
       "      <td>0.1670</td>\n",
       "      <td>0.2723</td>\n",
       "      <td>0.4304</td>\n",
       "      <td>575.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quercus</th>\n",
       "      <td>0.6885</td>\n",
       "      <td>0.4075</td>\n",
       "      <td>0.5120</td>\n",
       "      <td>0.6243</td>\n",
       "      <td>1443.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tilia</th>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.0345</td>\n",
       "      <td>0.0645</td>\n",
       "      <td>0.1031</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             precision  recall  f1-score     mAP  support\n",
       "Abies           0.8333  0.2564    0.3922  0.4383    156.0\n",
       "Acer            0.5152  0.0436    0.0804  0.2777    390.0\n",
       "Alnus           0.8667  0.0311    0.0600  0.2473    418.0\n",
       "Betula          0.7917  0.1326    0.2271  0.3885    430.0\n",
       "Cleared         0.8380  0.4196    0.5592  0.6495    715.0\n",
       "Fagus           0.7476  0.4421    0.5557  0.6865   1434.0\n",
       "Fraxinus        0.4490  0.0651    0.1137  0.2633    338.0\n",
       "Larix           0.8125  0.1020    0.1813  0.4040    637.0\n",
       "Picea           0.7972  0.4493    0.5747  0.7059   1409.0\n",
       "Pinus           0.8365  0.5852    0.6887  0.8084   1355.0\n",
       "Populus         0.8333  0.0521    0.0980  0.2810     96.0\n",
       "Prunus          0.2000  0.0213    0.0385  0.0816     47.0\n",
       "Pseudotsuga     0.7385  0.1670    0.2723  0.4304    575.0\n",
       "Quercus         0.6885  0.4075    0.5120  0.6243   1443.0\n",
       "Tilia           0.5000  0.0345    0.0645  0.1031     29.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>mAP</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>micro avg</th>\n",
       "      <td>0.7697</td>\n",
       "      <td>0.3447</td>\n",
       "      <td>0.4762</td>\n",
       "      <td>0.5711</td>\n",
       "      <td>9472.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macro avg</th>\n",
       "      <td>0.6965</td>\n",
       "      <td>0.2140</td>\n",
       "      <td>0.2945</td>\n",
       "      <td>0.4260</td>\n",
       "      <td>9472.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weighted avg</th>\n",
       "      <td>0.7552</td>\n",
       "      <td>0.3447</td>\n",
       "      <td>0.4452</td>\n",
       "      <td>0.5822</td>\n",
       "      <td>9472.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>samples avg</th>\n",
       "      <td>0.5836</td>\n",
       "      <td>0.4223</td>\n",
       "      <td>0.4660</td>\n",
       "      <td>0.7030</td>\n",
       "      <td>9472.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              precision  recall  f1-score     mAP  support\n",
       "micro avg        0.7697  0.3447    0.4762  0.5711   9472.0\n",
       "macro avg        0.6965  0.2140    0.2945  0.4260   9472.0\n",
       "weighted avg     0.7552  0.3447    0.4452  0.5822   9472.0\n",
       "samples avg      0.5836  0.4223    0.4660  0.7030   9472.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train = np.hstack((feats_aerial_train, s1_train.reshape(len(labels_train), -1)))\n",
    "y_train = multilabels_train\n",
    "X_test = np.hstack((feats_aerial_test, s1_test.reshape(len(labels_test), -1)))\n",
    "y_test = multilabels_test\n",
    "train_and_eval('A + S1', X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5438037e",
   "metadata": {},
   "source": [
    "### Aerial + S2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "d078246a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T23:47:21.543736Z",
     "start_time": "2022-07-04T23:04:51.436480Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>mAP</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Abies</th>\n",
       "      <td>0.9000</td>\n",
       "      <td>0.2885</td>\n",
       "      <td>0.4369</td>\n",
       "      <td>0.5812</td>\n",
       "      <td>156.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Acer</th>\n",
       "      <td>0.6486</td>\n",
       "      <td>0.0615</td>\n",
       "      <td>0.1124</td>\n",
       "      <td>0.3716</td>\n",
       "      <td>390.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alnus</th>\n",
       "      <td>0.8222</td>\n",
       "      <td>0.0885</td>\n",
       "      <td>0.1598</td>\n",
       "      <td>0.4301</td>\n",
       "      <td>418.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Betula</th>\n",
       "      <td>0.8194</td>\n",
       "      <td>0.1372</td>\n",
       "      <td>0.2351</td>\n",
       "      <td>0.4570</td>\n",
       "      <td>430.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cleared</th>\n",
       "      <td>0.8651</td>\n",
       "      <td>0.4126</td>\n",
       "      <td>0.5587</td>\n",
       "      <td>0.7015</td>\n",
       "      <td>715.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fagus</th>\n",
       "      <td>0.7649</td>\n",
       "      <td>0.5990</td>\n",
       "      <td>0.6719</td>\n",
       "      <td>0.7787</td>\n",
       "      <td>1434.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fraxinus</th>\n",
       "      <td>0.5893</td>\n",
       "      <td>0.0976</td>\n",
       "      <td>0.1675</td>\n",
       "      <td>0.3790</td>\n",
       "      <td>338.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Larix</th>\n",
       "      <td>0.8735</td>\n",
       "      <td>0.2276</td>\n",
       "      <td>0.3611</td>\n",
       "      <td>0.5298</td>\n",
       "      <td>637.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Picea</th>\n",
       "      <td>0.8610</td>\n",
       "      <td>0.6416</td>\n",
       "      <td>0.7353</td>\n",
       "      <td>0.8368</td>\n",
       "      <td>1409.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pinus</th>\n",
       "      <td>0.8434</td>\n",
       "      <td>0.6716</td>\n",
       "      <td>0.7477</td>\n",
       "      <td>0.8594</td>\n",
       "      <td>1355.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Populus</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0312</td>\n",
       "      <td>0.0606</td>\n",
       "      <td>0.4923</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prunus</th>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.0426</td>\n",
       "      <td>0.0800</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pseudotsuga</th>\n",
       "      <td>0.8217</td>\n",
       "      <td>0.3287</td>\n",
       "      <td>0.4696</td>\n",
       "      <td>0.6174</td>\n",
       "      <td>575.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quercus</th>\n",
       "      <td>0.7740</td>\n",
       "      <td>0.5149</td>\n",
       "      <td>0.6184</td>\n",
       "      <td>0.7504</td>\n",
       "      <td>1443.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tilia</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0345</td>\n",
       "      <td>0.0667</td>\n",
       "      <td>0.3360</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             precision  recall  f1-score     mAP  support\n",
       "Abies           0.9000  0.2885    0.4369  0.5812    156.0\n",
       "Acer            0.6486  0.0615    0.1124  0.3716    390.0\n",
       "Alnus           0.8222  0.0885    0.1598  0.4301    418.0\n",
       "Betula          0.8194  0.1372    0.2351  0.4570    430.0\n",
       "Cleared         0.8651  0.4126    0.5587  0.7015    715.0\n",
       "Fagus           0.7649  0.5990    0.6719  0.7787   1434.0\n",
       "Fraxinus        0.5893  0.0976    0.1675  0.3790    338.0\n",
       "Larix           0.8735  0.2276    0.3611  0.5298    637.0\n",
       "Picea           0.8610  0.6416    0.7353  0.8368   1409.0\n",
       "Pinus           0.8434  0.6716    0.7477  0.8594   1355.0\n",
       "Populus         1.0000  0.0312    0.0606  0.4923     96.0\n",
       "Prunus          0.6667  0.0426    0.0800  0.1974     47.0\n",
       "Pseudotsuga     0.8217  0.3287    0.4696  0.6174    575.0\n",
       "Quercus         0.7740  0.5149    0.6184  0.7504   1443.0\n",
       "Tilia           1.0000  0.0345    0.0667  0.3360     29.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>mAP</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>micro avg</th>\n",
       "      <td>0.8146</td>\n",
       "      <td>0.4486</td>\n",
       "      <td>0.5786</td>\n",
       "      <td>0.6941</td>\n",
       "      <td>9472.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macro avg</th>\n",
       "      <td>0.8167</td>\n",
       "      <td>0.2785</td>\n",
       "      <td>0.3654</td>\n",
       "      <td>0.5546</td>\n",
       "      <td>9472.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weighted avg</th>\n",
       "      <td>0.8089</td>\n",
       "      <td>0.4486</td>\n",
       "      <td>0.5440</td>\n",
       "      <td>0.6908</td>\n",
       "      <td>9472.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>samples avg</th>\n",
       "      <td>0.7205</td>\n",
       "      <td>0.5306</td>\n",
       "      <td>0.5832</td>\n",
       "      <td>0.7853</td>\n",
       "      <td>9472.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              precision  recall  f1-score     mAP  support\n",
       "micro avg        0.8146  0.4486    0.5786  0.6941   9472.0\n",
       "macro avg        0.8167  0.2785    0.3654  0.5546   9472.0\n",
       "weighted avg     0.8089  0.4486    0.5440  0.6908   9472.0\n",
       "samples avg      0.7205  0.5306    0.5832  0.7853   9472.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train = np.hstack((feats_aerial_train, s2_train.reshape(len(labels_train), -1)))\n",
    "y_train = multilabels_train\n",
    "X_test = np.hstack((feats_aerial_test, s2_test.reshape(len(labels_test), -1)))\n",
    "y_test = multilabels_test\n",
    "train_and_eval('A + S2', X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b096443",
   "metadata": {},
   "source": [
    "### Aerial + S1 + S2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "15448c6b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-05T00:25:26.057164Z",
     "start_time": "2022-07-04T23:47:21.545878Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>mAP</th>\n",
       "      <th>support</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Abies</th>\n",
       "      <td>0.6364</td>\n",
       "      <td>0.2692</td>\n",
       "      <td>0.3784</td>\n",
       "      <td>0.4114</td>\n",
       "      <td>156.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Acer</th>\n",
       "      <td>0.6842</td>\n",
       "      <td>0.0667</td>\n",
       "      <td>0.1215</td>\n",
       "      <td>0.3714</td>\n",
       "      <td>390.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alnus</th>\n",
       "      <td>0.7778</td>\n",
       "      <td>0.1172</td>\n",
       "      <td>0.2037</td>\n",
       "      <td>0.4075</td>\n",
       "      <td>418.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Betula</th>\n",
       "      <td>0.8022</td>\n",
       "      <td>0.1698</td>\n",
       "      <td>0.2802</td>\n",
       "      <td>0.4574</td>\n",
       "      <td>430.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cleared</th>\n",
       "      <td>0.8830</td>\n",
       "      <td>0.4434</td>\n",
       "      <td>0.5903</td>\n",
       "      <td>0.7214</td>\n",
       "      <td>715.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fagus</th>\n",
       "      <td>0.7617</td>\n",
       "      <td>0.5886</td>\n",
       "      <td>0.6640</td>\n",
       "      <td>0.7713</td>\n",
       "      <td>1434.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fraxinus</th>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.1183</td>\n",
       "      <td>0.2010</td>\n",
       "      <td>0.4266</td>\n",
       "      <td>338.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Larix</th>\n",
       "      <td>0.8675</td>\n",
       "      <td>0.2057</td>\n",
       "      <td>0.3325</td>\n",
       "      <td>0.5195</td>\n",
       "      <td>637.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Picea</th>\n",
       "      <td>0.8560</td>\n",
       "      <td>0.6118</td>\n",
       "      <td>0.7136</td>\n",
       "      <td>0.8314</td>\n",
       "      <td>1409.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pinus</th>\n",
       "      <td>0.8590</td>\n",
       "      <td>0.6790</td>\n",
       "      <td>0.7585</td>\n",
       "      <td>0.8679</td>\n",
       "      <td>1355.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Populus</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.5112</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prunus</th>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.0213</td>\n",
       "      <td>0.0408</td>\n",
       "      <td>0.2392</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pseudotsuga</th>\n",
       "      <td>0.8579</td>\n",
       "      <td>0.2730</td>\n",
       "      <td>0.4142</td>\n",
       "      <td>0.5960</td>\n",
       "      <td>575.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quercus</th>\n",
       "      <td>0.7848</td>\n",
       "      <td>0.5156</td>\n",
       "      <td>0.6223</td>\n",
       "      <td>0.7542</td>\n",
       "      <td>1443.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tilia</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0345</td>\n",
       "      <td>0.0667</td>\n",
       "      <td>0.3540</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             precision  recall  f1-score     mAP  support\n",
       "Abies           0.6364  0.2692    0.3784  0.4114    156.0\n",
       "Acer            0.6842  0.0667    0.1215  0.3714    390.0\n",
       "Alnus           0.7778  0.1172    0.2037  0.4075    418.0\n",
       "Betula          0.8022  0.1698    0.2802  0.4574    430.0\n",
       "Cleared         0.8830  0.4434    0.5903  0.7214    715.0\n",
       "Fagus           0.7617  0.5886    0.6640  0.7713   1434.0\n",
       "Fraxinus        0.6667  0.1183    0.2010  0.4266    338.0\n",
       "Larix           0.8675  0.2057    0.3325  0.5195    637.0\n",
       "Picea           0.8560  0.6118    0.7136  0.8314   1409.0\n",
       "Pinus           0.8590  0.6790    0.7585  0.8679   1355.0\n",
       "Populus         0.0000  0.0000    0.0000  0.5112     96.0\n",
       "Prunus          0.5000  0.0213    0.0408  0.2392     47.0\n",
       "Pseudotsuga     0.8579  0.2730    0.4142  0.5960    575.0\n",
       "Quercus         0.7848  0.5156    0.6223  0.7542   1443.0\n",
       "Tilia           1.0000  0.0345    0.0667  0.3540     29.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>mAP</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>micro avg</th>\n",
       "      <td>0.8172</td>\n",
       "      <td>0.4442</td>\n",
       "      <td>0.5755</td>\n",
       "      <td>0.6962</td>\n",
       "      <td>9472.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macro avg</th>\n",
       "      <td>0.7292</td>\n",
       "      <td>0.2743</td>\n",
       "      <td>0.3592</td>\n",
       "      <td>0.5494</td>\n",
       "      <td>9472.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weighted avg</th>\n",
       "      <td>0.8009</td>\n",
       "      <td>0.4442</td>\n",
       "      <td>0.5426</td>\n",
       "      <td>0.6885</td>\n",
       "      <td>9472.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>samples avg</th>\n",
       "      <td>0.7239</td>\n",
       "      <td>0.5263</td>\n",
       "      <td>0.5814</td>\n",
       "      <td>0.7882</td>\n",
       "      <td>9472.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              precision  recall  f1-score     mAP  support\n",
       "micro avg        0.8172  0.4442    0.5755  0.6962   9472.0\n",
       "macro avg        0.7292  0.2743    0.3592  0.5494   9472.0\n",
       "weighted avg     0.8009  0.4442    0.5426  0.6885   9472.0\n",
       "samples avg      0.7239  0.5263    0.5814  0.7882   9472.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train = np.hstack((feats_aerial_train, s1_train.reshape(len(labels_train), -1), s2_train.reshape(len(labels_train), -1)))\n",
    "y_train = multilabels_train\n",
    "X_test = np.hstack((feats_aerial_test, s1_test.reshape(len(labels_test), -1), s2_test.reshape(len(labels_test), -1)))\n",
    "y_test = multilabels_test\n",
    "train_and_eval('A + S1 + S2', X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ad391ec",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "940a93ec",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-05T00:25:26.090172Z",
     "start_time": "2022-07-05T00:25:26.059090Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S1\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>mAP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>micro avg</th>\n",
       "      <td>0.5549</td>\n",
       "      <td>0.0806</td>\n",
       "      <td>0.1407</td>\n",
       "      <td>0.3511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macro avg</th>\n",
       "      <td>0.1943</td>\n",
       "      <td>0.0438</td>\n",
       "      <td>0.0648</td>\n",
       "      <td>0.2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weighted avg</th>\n",
       "      <td>0.3796</td>\n",
       "      <td>0.0806</td>\n",
       "      <td>0.1186</td>\n",
       "      <td>0.3279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>samples avg</th>\n",
       "      <td>0.1409</td>\n",
       "      <td>0.0994</td>\n",
       "      <td>0.1108</td>\n",
       "      <td>0.5474</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              precision  recall  f1-score     mAP\n",
       "micro avg        0.5549  0.0806    0.1407  0.3511\n",
       "macro avg        0.1943  0.0438    0.0648  0.2007\n",
       "weighted avg     0.3796  0.0806    0.1186  0.3279\n",
       "samples avg      0.1409  0.0994    0.1108  0.5474"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S2\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>mAP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>micro avg</th>\n",
       "      <td>0.7627</td>\n",
       "      <td>0.4004</td>\n",
       "      <td>0.5252</td>\n",
       "      <td>0.6165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macro avg</th>\n",
       "      <td>0.6663</td>\n",
       "      <td>0.2313</td>\n",
       "      <td>0.2982</td>\n",
       "      <td>0.4516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weighted avg</th>\n",
       "      <td>0.7427</td>\n",
       "      <td>0.4004</td>\n",
       "      <td>0.4817</td>\n",
       "      <td>0.6199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>samples avg</th>\n",
       "      <td>0.6490</td>\n",
       "      <td>0.4739</td>\n",
       "      <td>0.5220</td>\n",
       "      <td>0.7256</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              precision  recall  f1-score     mAP\n",
       "micro avg        0.7627  0.4004    0.5252  0.6165\n",
       "macro avg        0.6663  0.2313    0.2982  0.4516\n",
       "weighted avg     0.7427  0.4004    0.4817  0.6199\n",
       "samples avg      0.6490  0.4739    0.5220  0.7256"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>mAP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>micro avg</th>\n",
       "      <td>0.7863</td>\n",
       "      <td>0.3398</td>\n",
       "      <td>0.4746</td>\n",
       "      <td>0.5798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macro avg</th>\n",
       "      <td>0.6944</td>\n",
       "      <td>0.2020</td>\n",
       "      <td>0.2769</td>\n",
       "      <td>0.4531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weighted avg</th>\n",
       "      <td>0.7612</td>\n",
       "      <td>0.3398</td>\n",
       "      <td>0.4398</td>\n",
       "      <td>0.6004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>samples avg</th>\n",
       "      <td>0.5858</td>\n",
       "      <td>0.4144</td>\n",
       "      <td>0.4627</td>\n",
       "      <td>0.7018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              precision  recall  f1-score     mAP\n",
       "micro avg        0.7863  0.3398    0.4746  0.5798\n",
       "macro avg        0.6944  0.2020    0.2769  0.4531\n",
       "weighted avg     0.7612  0.3398    0.4398  0.6004\n",
       "samples avg      0.5858  0.4144    0.4627  0.7018"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S1 + S2\n"
     ]
    },
    {
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       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>mAP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>micro avg</th>\n",
       "      <td>0.7758</td>\n",
       "      <td>0.3932</td>\n",
       "      <td>0.5219</td>\n",
       "      <td>0.6010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macro avg</th>\n",
       "      <td>0.6392</td>\n",
       "      <td>0.2170</td>\n",
       "      <td>0.2760</td>\n",
       "      <td>0.4411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weighted avg</th>\n",
       "      <td>0.7460</td>\n",
       "      <td>0.3932</td>\n",
       "      <td>0.4740</td>\n",
       "      <td>0.6206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>samples avg</th>\n",
       "      <td>0.6469</td>\n",
       "      <td>0.4653</td>\n",
       "      <td>0.5160</td>\n",
       "      <td>0.7129</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              precision  recall  f1-score     mAP\n",
       "micro avg        0.7758  0.3932    0.5219  0.6010\n",
       "macro avg        0.6392  0.2170    0.2760  0.4411\n",
       "weighted avg     0.7460  0.3932    0.4740  0.6206\n",
       "samples avg      0.6469  0.4653    0.5160  0.7129"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A + S1\n"
     ]
    },
    {
     "data": {
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       "      <th>mAP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>micro avg</th>\n",
       "      <td>0.7697</td>\n",
       "      <td>0.3447</td>\n",
       "      <td>0.4762</td>\n",
       "      <td>0.5711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macro avg</th>\n",
       "      <td>0.6965</td>\n",
       "      <td>0.2140</td>\n",
       "      <td>0.2945</td>\n",
       "      <td>0.4260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weighted avg</th>\n",
       "      <td>0.7552</td>\n",
       "      <td>0.3447</td>\n",
       "      <td>0.4452</td>\n",
       "      <td>0.5822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>samples avg</th>\n",
       "      <td>0.5836</td>\n",
       "      <td>0.4223</td>\n",
       "      <td>0.4660</td>\n",
       "      <td>0.7030</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              precision  recall  f1-score     mAP\n",
       "micro avg        0.7697  0.3447    0.4762  0.5711\n",
       "macro avg        0.6965  0.2140    0.2945  0.4260\n",
       "weighted avg     0.7552  0.3447    0.4452  0.5822\n",
       "samples avg      0.5836  0.4223    0.4660  0.7030"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A + S2\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
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       "      <th>mAP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>micro avg</th>\n",
       "      <td>0.8146</td>\n",
       "      <td>0.4486</td>\n",
       "      <td>0.5786</td>\n",
       "      <td>0.6941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macro avg</th>\n",
       "      <td>0.8167</td>\n",
       "      <td>0.2785</td>\n",
       "      <td>0.3654</td>\n",
       "      <td>0.5546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weighted avg</th>\n",
       "      <td>0.8089</td>\n",
       "      <td>0.4486</td>\n",
       "      <td>0.5440</td>\n",
       "      <td>0.6908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>samples avg</th>\n",
       "      <td>0.7205</td>\n",
       "      <td>0.5306</td>\n",
       "      <td>0.5832</td>\n",
       "      <td>0.7853</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              precision  recall  f1-score     mAP\n",
       "micro avg        0.8146  0.4486    0.5786  0.6941\n",
       "macro avg        0.8167  0.2785    0.3654  0.5546\n",
       "weighted avg     0.8089  0.4486    0.5440  0.6908\n",
       "samples avg      0.7205  0.5306    0.5832  0.7853"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A + S1 + S2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>mAP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>micro avg</th>\n",
       "      <td>0.8172</td>\n",
       "      <td>0.4442</td>\n",
       "      <td>0.5755</td>\n",
       "      <td>0.6962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macro avg</th>\n",
       "      <td>0.7292</td>\n",
       "      <td>0.2743</td>\n",
       "      <td>0.3592</td>\n",
       "      <td>0.5494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weighted avg</th>\n",
       "      <td>0.8009</td>\n",
       "      <td>0.4442</td>\n",
       "      <td>0.5426</td>\n",
       "      <td>0.6885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>samples avg</th>\n",
       "      <td>0.7239</td>\n",
       "      <td>0.5263</td>\n",
       "      <td>0.5814</td>\n",
       "      <td>0.7882</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              precision  recall  f1-score     mAP\n",
       "micro avg        0.8172  0.4442    0.5755  0.6962\n",
       "macro avg        0.7292  0.2743    0.3592  0.5494\n",
       "weighted avg     0.8009  0.4442    0.5426  0.6885\n",
       "samples avg      0.7239  0.5263    0.5814  0.7882"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with shelve.open(\"lightgbm_res.shelve\") as all_res:\n",
    "    for name in 'S1, S2, A, S1 + S2, A + S1, A + S2, A + S1 + S2'.split(', '):\n",
    "        df = all_res[name].iloc[-4:, :-1]\n",
    "        print(name)\n",
    "        display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f3d60153",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-05T15:29:42.958824Z",
     "start_time": "2022-07-05T15:29:42.929109Z"
    }
   },
   "outputs": [],
   "source": [
    "with shelve.open(\"lightgbm_res.shelve\") as all_res:\n",
    "    df = pd.DataFrame.from_dict({\n",
    "        name:all_res[name].iloc[-4:, :-1].stack() for name in 'S1, S2, A, S1 + S2, A + S1, A + S2, A + S1 + S2'.split(', ')\n",
    "    }, orient='index')[['weighted avg', 'micro avg']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a48c609c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-05T15:29:43.342943Z",
     "start_time": "2022-07-05T15:29:43.241566Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table id=\"T_031b0\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_031b0_level0_col0\" class=\"col_heading level0 col0\" colspan=\"4\">weighted avg</th>\n",
       "      <th id=\"T_031b0_level0_col4\" class=\"col_heading level0 col4\" colspan=\"4\">micro avg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"blank level1\" >&nbsp;</th>\n",
       "      <th id=\"T_031b0_level1_col0\" class=\"col_heading level1 col0\" >precision</th>\n",
       "      <th id=\"T_031b0_level1_col1\" class=\"col_heading level1 col1\" >recall</th>\n",
       "      <th id=\"T_031b0_level1_col2\" class=\"col_heading level1 col2\" >f1-score</th>\n",
       "      <th id=\"T_031b0_level1_col3\" class=\"col_heading level1 col3\" >mAP</th>\n",
       "      <th id=\"T_031b0_level1_col4\" class=\"col_heading level1 col4\" >precision</th>\n",
       "      <th id=\"T_031b0_level1_col5\" class=\"col_heading level1 col5\" >recall</th>\n",
       "      <th id=\"T_031b0_level1_col6\" class=\"col_heading level1 col6\" >f1-score</th>\n",
       "      <th id=\"T_031b0_level1_col7\" class=\"col_heading level1 col7\" >mAP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_031b0_level0_row0\" class=\"row_heading level0 row0\" >S1</th>\n",
       "      <td id=\"T_031b0_row0_col0\" class=\"data row0 col0\" >0.379555</td>\n",
       "      <td id=\"T_031b0_row0_col1\" class=\"data row0 col1\" >0.080553</td>\n",
       "      <td id=\"T_031b0_row0_col2\" class=\"data row0 col2\" >0.118563</td>\n",
       "      <td id=\"T_031b0_row0_col3\" class=\"data row0 col3\" >0.327856</td>\n",
       "      <td id=\"T_031b0_row0_col4\" class=\"data row0 col4\" >0.554909</td>\n",
       "      <td id=\"T_031b0_row0_col5\" class=\"data row0 col5\" >0.080553</td>\n",
       "      <td id=\"T_031b0_row0_col6\" class=\"data row0 col6\" >0.140684</td>\n",
       "      <td id=\"T_031b0_row0_col7\" class=\"data row0 col7\" >0.351051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_031b0_level0_row1\" class=\"row_heading level0 row1\" >S2</th>\n",
       "      <td id=\"T_031b0_row1_col0\" class=\"data row1 col0\" >0.742738</td>\n",
       "      <td id=\"T_031b0_row1_col1\" class=\"data row1 col1\" >0.400443</td>\n",
       "      <td id=\"T_031b0_row1_col2\" class=\"data row1 col2\" >0.481652</td>\n",
       "      <td id=\"T_031b0_row1_col3\" class=\"data row1 col3\" >0.619903</td>\n",
       "      <td id=\"T_031b0_row1_col4\" class=\"data row1 col4\" >0.762719</td>\n",
       "      <td id=\"T_031b0_row1_col5\" class=\"data row1 col5\" >0.400443</td>\n",
       "      <td id=\"T_031b0_row1_col6\" class=\"data row1 col6\" >0.525164</td>\n",
       "      <td id=\"T_031b0_row1_col7\" class=\"data row1 col7\" >0.616548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_031b0_level0_row2\" class=\"row_heading level0 row2\" >A</th>\n",
       "      <td id=\"T_031b0_row2_col0\" class=\"data row2 col0\" >0.761154</td>\n",
       "      <td id=\"T_031b0_row2_col1\" class=\"data row2 col1\" >0.339844</td>\n",
       "      <td id=\"T_031b0_row2_col2\" class=\"data row2 col2\" >0.439850</td>\n",
       "      <td id=\"T_031b0_row2_col3\" class=\"data row2 col3\" >0.600413</td>\n",
       "      <td id=\"T_031b0_row2_col4\" class=\"data row2 col4\" >0.786273</td>\n",
       "      <td id=\"T_031b0_row2_col5\" class=\"data row2 col5\" >0.339844</td>\n",
       "      <td id=\"T_031b0_row2_col6\" class=\"data row2 col6\" >0.474569</td>\n",
       "      <td id=\"T_031b0_row2_col7\" class=\"data row2 col7\" >0.579782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_031b0_level0_row3\" class=\"row_heading level0 row3\" >S1 + S2</th>\n",
       "      <td id=\"T_031b0_row3_col0\" class=\"data row3 col0\" >0.745990</td>\n",
       "      <td id=\"T_031b0_row3_col1\" class=\"data row3 col1\" >0.393159</td>\n",
       "      <td id=\"T_031b0_row3_col2\" class=\"data row3 col2\" >0.474031</td>\n",
       "      <td id=\"T_031b0_row3_col3\" class=\"data row3 col3\" >0.620630</td>\n",
       "      <td id=\"T_031b0_row3_col4\" class=\"data row3 col4\" >0.775833</td>\n",
       "      <td id=\"T_031b0_row3_col5\" class=\"data row3 col5\" >0.393159</td>\n",
       "      <td id=\"T_031b0_row3_col6\" class=\"data row3 col6\" >0.521861</td>\n",
       "      <td id=\"T_031b0_row3_col7\" class=\"data row3 col7\" >0.601025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_031b0_level0_row4\" class=\"row_heading level0 row4\" >A + S1</th>\n",
       "      <td id=\"T_031b0_row4_col0\" class=\"data row4 col0\" >0.755173</td>\n",
       "      <td id=\"T_031b0_row4_col1\" class=\"data row4 col1\" >0.344700</td>\n",
       "      <td id=\"T_031b0_row4_col2\" class=\"data row4 col2\" >0.445221</td>\n",
       "      <td id=\"T_031b0_row4_col3\" class=\"data row4 col3\" >0.582184</td>\n",
       "      <td id=\"T_031b0_row4_col4\" class=\"data row4 col4\" >0.769684</td>\n",
       "      <td id=\"T_031b0_row4_col5\" class=\"data row4 col5\" >0.344700</td>\n",
       "      <td id=\"T_031b0_row4_col6\" class=\"data row4 col6\" >0.476156</td>\n",
       "      <td id=\"T_031b0_row4_col7\" class=\"data row4 col7\" >0.571078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_031b0_level0_row5\" class=\"row_heading level0 row5\" >A + S2</th>\n",
       "      <td id=\"T_031b0_row5_col0\" class=\"data row5 col0\" >0.808907</td>\n",
       "      <td id=\"T_031b0_row5_col1\" class=\"data row5 col1\" >0.448585</td>\n",
       "      <td id=\"T_031b0_row5_col2\" class=\"data row5 col2\" >0.543974</td>\n",
       "      <td id=\"T_031b0_row5_col3\" class=\"data row5 col3\" >0.690809</td>\n",
       "      <td id=\"T_031b0_row5_col4\" class=\"data row5 col4\" >0.814609</td>\n",
       "      <td id=\"T_031b0_row5_col5\" class=\"data row5 col5\" >0.448585</td>\n",
       "      <td id=\"T_031b0_row5_col6\" class=\"data row5 col6\" >0.578568</td>\n",
       "      <td id=\"T_031b0_row5_col7\" class=\"data row5 col7\" >0.694129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_031b0_level0_row6\" class=\"row_heading level0 row6\" >A + S1 + S2</th>\n",
       "      <td id=\"T_031b0_row6_col0\" class=\"data row6 col0\" >0.800905</td>\n",
       "      <td id=\"T_031b0_row6_col1\" class=\"data row6 col1\" >0.444151</td>\n",
       "      <td id=\"T_031b0_row6_col2\" class=\"data row6 col2\" >0.542581</td>\n",
       "      <td id=\"T_031b0_row6_col3\" class=\"data row6 col3\" >0.688548</td>\n",
       "      <td id=\"T_031b0_row6_col4\" class=\"data row6 col4\" >0.817211</td>\n",
       "      <td id=\"T_031b0_row6_col5\" class=\"data row6 col5\" >0.444151</td>\n",
       "      <td id=\"T_031b0_row6_col6\" class=\"data row6 col6\" >0.575513</td>\n",
       "      <td id=\"T_031b0_row6_col7\" class=\"data row6 col7\" >0.696188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f2214368550>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.style.background_gradient(cmap='RdYlGn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "00f9d06c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-05T15:29:43.969433Z",
     "start_time": "2022-07-05T15:29:43.715245Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrrrrrrr}\n",
      "\\toprule\n",
      "{} & \\multicolumn{4}{l}{weighted avg} & \\multicolumn{4}{l}{micro avg} \\\\\n",
      "{} &           precision &              recall &            f1-score &                 mAP &           precision &              recall &            f1-score &                 mAP \\\\\n",
      "\\midrule\n",
      "S1          & \\percentage{0.3796} & \\percentage{0.0806} & \\percentage{0.1186} & \\percentage{0.3279} & \\percentage{0.5549} & \\percentage{0.0806} & \\percentage{0.1407} & \\percentage{0.3511} \\\\\n",
      "S2          & \\percentage{0.7427} & \\percentage{0.4004} & \\percentage{0.4817} & \\percentage{0.6199} & \\percentage{0.7627} & \\percentage{0.4004} & \\percentage{0.5252} & \\percentage{0.6165} \\\\\n",
      "A           & \\percentage{0.7612} & \\percentage{0.3398} & \\percentage{0.4398} & \\percentage{0.6004} & \\percentage{0.7863} & \\percentage{0.3398} & \\percentage{0.4746} & \\percentage{0.5798} \\\\\n",
      "S1 + S2     & \\percentage{0.7460} & \\percentage{0.3932} & \\percentage{0.4740} & \\percentage{0.6206} & \\percentage{0.7758} & \\percentage{0.3932} & \\percentage{0.5219} & \\percentage{0.6010} \\\\\n",
      "A + S1      & \\percentage{0.7552} & \\percentage{0.3447} & \\percentage{0.4452} & \\percentage{0.5822} & \\percentage{0.7697} & \\percentage{0.3447} & \\percentage{0.4762} & \\percentage{0.5711} \\\\\n",
      "A + S2      & \\percentage{0.8089} & \\percentage{0.4486} & \\percentage{0.5440} & \\percentage{0.6908} & \\percentage{0.8146} & \\percentage{0.4486} & \\percentage{0.5786} & \\percentage{0.6941} \\\\\n",
      "A + S1 + S2 & \\percentage{0.8009} & \\percentage{0.4442} & \\percentage{0.5426} & \\percentage{0.6885} & \\percentage{0.8172} & \\percentage{0.4442} & \\percentage{0.5755} & \\percentage{0.6962} \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2756018/3326367905.py:1: FutureWarning: In future versions `DataFrame.to_latex` is expected to utilise the base implementation of `Styler.to_latex` for formatting and rendering. The arguments signature may therefore change. It is recommended instead to use `DataFrame.style.to_latex` which also contains additional functionality.\n",
      "  print(df.to_latex(float_format=\"\\\\percentage{%.4f}\", escape=False))\n"
     ]
    }
   ],
   "source": [
    "print(df.to_latex(float_format=\"\\\\percentage{%.4f}\", escape=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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