notebooks/09_Combinatorial_Method_Usage_with_FingerPrint_Transformers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example: Using Multiple Different Fingerprint Transformer\n",
    "\n",
    "In this notebook we will explore how to evaluate the performance of machine learning models depending on different fingerprint transformers (Featurization techniques). This is an example, that you easily could adapt for many different combinations of featurizers, optimizaiton and other modelling techniques.\n",
    "\n",
    "Following steps will happen:\n",
    "* Data Parsing\n",
    "* Pipeline Building\n",
    "* Training Phase\n",
    "* Analysis\n",
    "\n",
    "Authors: @VincentAlexanderScholz, @RiesBen \n",
    "\n",
    "## Imports:\n",
    "First we will import all the stuff that we will need for our work.\n"
   ]
  },
  {
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   "execution_count": 1,
   "metadata": {
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   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from time import time\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from rdkit.Chem import PandasTools\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.pipeline import Pipeline, make_pipeline\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from scikit_mol import fingerprints"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Data:\n",
    "In this step we will check if the SLC6A4 data set is already present or needs to be downloaded.\n",
    "\n",
    "\n",
    "**WARNING:** The Dataset is a simple and very well selected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2024-04-12T12:10:34.880411Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 out of 200 SMILES failed in conversion\n"
     ]
    }
   ],
   "source": [
    "full_set = False\n",
    "\n",
    "# if not present download example data\n",
    "if full_set:\n",
    "    csv_file = \"SLC6A4_active_excape_export.csv\"\n",
    "    if not os.path.exists(csv_file):\n",
    "        import urllib.request\n",
    "        url = \"https://ndownloader.figshare.com/files/25747817\"\n",
    "        urllib.request.urlretrieve(url, csv_file)\n",
    "else:\n",
    "    csv_file = '../tests/data/SLC6A4_active_excapedb_subset.csv'\n",
    "\n",
    "#Parse Database\n",
    "data = pd.read_csv(csv_file)\n",
    "\n",
    "PandasTools.AddMoleculeColumnToFrame(data, smilesCol=\"SMILES\")\n",
    "print(f\"{data.ROMol.isna().sum()} out of {len(data)} SMILES failed in conversion\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build Pipeline:\n",
    "In this stage we will build the Pipeline consisting of the featurization part (finger print transformers) and the model part (Ridge Regression).\n",
    "\n",
    "Note that the featurization in this section is an hyperparameter, living in `param_grid`, and the `\"fp_transformer\"` string is just a placeholder, being replaced during pipeline execution. \n",
    "\n",
    "This way we can define multiple different scenarios in `param_grid`, that allow us to rapidly explore different combinations of settings and methodologies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-22T11:29:15.949644Z",
     "start_time": "2023-09-22T11:29:15.461010Z"
    },
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'fp_transformer': [MorganFingerprintTransformer(),\n",
       "   AvalonFingerprintTransformer()],\n",
       "  'fp_transformer__nBits': [256, 512, 1024, 2048, 4096],\n",
       "  'regressor__alpha': array([0.1  , 0.325, 0.55 , 0.775, 1.   ])},\n",
       " {'fp_transformer': [RDKitFingerprintTransformer(),\n",
       "   AtomPairFingerprintTransformer(),\n",
       "   MACCSKeysFingerprintTransformer()],\n",
       "  'regressor__alpha': array([0.1  , 0.325, 0.55 , 0.775, 1.   ])}]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "regressor = Ridge()\n",
    "optimization_pipe = Pipeline([(\"fp_transformer\", \"fp_transformer\"), # this is a placeholder for different transformers\n",
    "                              (\"regressor\", regressor)])\n",
    "\n",
    "param_grid = [ # Here pass different Options and Approaches\n",
    "    {\n",
    "        \"fp_transformer\": [fingerprints.MorganFingerprintTransformer(),\n",
    "                          fingerprints.AvalonFingerprintTransformer()],\n",
    "        \"fp_transformer__nBits\": [2**x for x in range(8,13)],\n",
    "    },\n",
    "    {\n",
    "       \"fp_transformer\": [fingerprints.RDKitFingerprintTransformer(),\n",
    "                         fingerprints.AtomPairFingerprintTransformer(),\n",
    "                         fingerprints.MACCSKeysFingerprintTransformer()], \n",
    "    },\n",
    "]\n",
    "\n",
    "global_options = {\n",
    "    \"regressor__alpha\": np.linspace(0.1,1,5),\n",
    "}\n",
    "\n",
    "[params.update(global_options) for params in param_grid]\n",
    "\n",
    "param_grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Model\n",
    "In this section, the combinatorial approaches are trained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-22T11:29:15.960939Z",
     "start_time": "2023-09-22T11:29:15.461078Z"
    },
    "execution": {
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     "shell.execute_reply": "2024-04-12T12:11:08.385791Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Runtime: 33.48\n"
     ]
    }
   ],
   "source": [
    "# Split Data\n",
    "mol_list_train, mol_list_test, y_train, y_test = train_test_split(data.ROMol, data.pXC50, random_state=0)\n",
    "\n",
    "# Define Search Process\n",
    "grid = GridSearchCV(optimization_pipe, n_jobs=1,\n",
    "                    param_grid=param_grid)\n",
    "\n",
    "# Train\n",
    "t0 = time()\n",
    "grid.fit(mol_list_train, y_train.values)\n",
    "t1 = time()\n",
    "\n",
    "print(f'Runtime: {t1-t0:0.2F}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis\n",
    "\n",
    "Now let's investigate our results from the training stage. Which one is the best finger print method for this data set? Which parameters are optimal?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2024-04-12T12:11:08.443334Z"
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   "outputs": [
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       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
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       "      <th>param_fp_transformer__nBits</th>\n",
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       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
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       "      <th>rank_test_score</th>\n",
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       "      <td>0.025447</td>\n",
       "      <td>0.002689</td>\n",
       "      <td>0.003600</td>\n",
       "      <td>0.000743</td>\n",
       "      <td>MorganFingerprintTransformer()</td>\n",
       "      <td>256</td>\n",
       "      <td>0.1</td>\n",
       "      <td>{'fp_transformer': MorganFingerprintTransforme...</td>\n",
       "      <td>0.017975</td>\n",
       "      <td>0.394682</td>\n",
       "      <td>0.524598</td>\n",
       "      <td>0.542116</td>\n",
       "      <td>0.310238</td>\n",
       "      <td>0.357922</td>\n",
       "      <td>0.190209</td>\n",
       "      <td>25</td>\n",
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       "      <th>1</th>\n",
       "      <td>0.025515</td>\n",
       "      <td>0.001279</td>\n",
       "      <td>0.003084</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>MorganFingerprintTransformer()</td>\n",
       "      <td>256</td>\n",
       "      <td>0.325</td>\n",
       "      <td>{'fp_transformer': MorganFingerprintTransforme...</td>\n",
       "      <td>0.078758</td>\n",
       "      <td>0.449548</td>\n",
       "      <td>0.554241</td>\n",
       "      <td>0.572363</td>\n",
       "      <td>0.330543</td>\n",
       "      <td>0.397090</td>\n",
       "      <td>0.181071</td>\n",
       "      <td>24</td>\n",
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       "      <th>2</th>\n",
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       "      <td>0.000330</td>\n",
       "      <td>0.003101</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>MorganFingerprintTransformer()</td>\n",
       "      <td>256</td>\n",
       "      <td>0.55</td>\n",
       "      <td>{'fp_transformer': MorganFingerprintTransforme...</td>\n",
       "      <td>0.128221</td>\n",
       "      <td>0.490253</td>\n",
       "      <td>0.575230</td>\n",
       "      <td>0.593237</td>\n",
       "      <td>0.344076</td>\n",
       "      <td>0.426203</td>\n",
       "      <td>0.173061</td>\n",
       "      <td>23</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.026571</td>\n",
       "      <td>0.001018</td>\n",
       "      <td>0.003208</td>\n",
       "      <td>0.000214</td>\n",
       "      <td>MorganFingerprintTransformer()</td>\n",
       "      <td>256</td>\n",
       "      <td>0.775</td>\n",
       "      <td>{'fp_transformer': MorganFingerprintTransforme...</td>\n",
       "      <td>0.169585</td>\n",
       "      <td>0.521723</td>\n",
       "      <td>0.590890</td>\n",
       "      <td>0.608380</td>\n",
       "      <td>0.353866</td>\n",
       "      <td>0.448889</td>\n",
       "      <td>0.166100</td>\n",
       "      <td>22</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.027070</td>\n",
       "      <td>0.001007</td>\n",
       "      <td>0.003137</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>MorganFingerprintTransformer()</td>\n",
       "      <td>256</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{'fp_transformer': MorganFingerprintTransforme...</td>\n",
       "      <td>0.204831</td>\n",
       "      <td>0.546774</td>\n",
       "      <td>0.603010</td>\n",
       "      <td>0.619752</td>\n",
       "      <td>0.361324</td>\n",
       "      <td>0.467138</td>\n",
       "      <td>0.160060</td>\n",
       "      <td>21</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>60</th>\n",
       "      <td>0.113114</td>\n",
       "      <td>0.018944</td>\n",
       "      <td>0.029888</td>\n",
       "      <td>0.008465</td>\n",
       "      <td>MACCSKeysFingerprintTransformer()</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1</td>\n",
       "      <td>{'fp_transformer': MACCSKeysFingerprintTransfo...</td>\n",
       "      <td>-1.649022</td>\n",
       "      <td>-1.943461</td>\n",
       "      <td>-0.602509</td>\n",
       "      <td>-0.418328</td>\n",
       "      <td>-0.752525</td>\n",
       "      <td>-1.073169</td>\n",
       "      <td>0.606987</td>\n",
       "      <td>65</td>\n",
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       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>0.102846</td>\n",
       "      <td>0.001870</td>\n",
       "      <td>0.025450</td>\n",
       "      <td>0.001682</td>\n",
       "      <td>MACCSKeysFingerprintTransformer()</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.325</td>\n",
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       "      <td>-0.969593</td>\n",
       "      <td>-0.813087</td>\n",
       "      <td>-0.188690</td>\n",
       "      <td>0.003831</td>\n",
       "      <td>-0.314764</td>\n",
       "      <td>-0.456461</td>\n",
       "      <td>0.372595</td>\n",
       "      <td>64</td>\n",
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       "      <td>0.103216</td>\n",
       "      <td>0.001285</td>\n",
       "      <td>0.025450</td>\n",
       "      <td>0.001661</td>\n",
       "      <td>MACCSKeysFingerprintTransformer()</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.55</td>\n",
       "      <td>{'fp_transformer': MACCSKeysFingerprintTransfo...</td>\n",
       "      <td>-0.657588</td>\n",
       "      <td>-0.505782</td>\n",
       "      <td>-0.045940</td>\n",
       "      <td>0.124510</td>\n",
       "      <td>-0.171340</td>\n",
       "      <td>-0.251228</td>\n",
       "      <td>0.289700</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>0.103230</td>\n",
       "      <td>0.002304</td>\n",
       "      <td>0.025570</td>\n",
       "      <td>0.001561</td>\n",
       "      <td>MACCSKeysFingerprintTransformer()</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.775</td>\n",
       "      <td>{'fp_transformer': MACCSKeysFingerprintTransfo...</td>\n",
       "      <td>-0.468371</td>\n",
       "      <td>-0.356825</td>\n",
       "      <td>0.036642</td>\n",
       "      <td>0.182939</td>\n",
       "      <td>-0.087318</td>\n",
       "      <td>-0.138587</td>\n",
       "      <td>0.242115</td>\n",
       "      <td>59</td>\n",
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       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>0.104568</td>\n",
       "      <td>0.002589</td>\n",
       "      <td>0.025515</td>\n",
       "      <td>0.001571</td>\n",
       "      <td>MACCSKeysFingerprintTransformer()</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{'fp_transformer': MACCSKeysFingerprintTransfo...</td>\n",
       "      <td>-0.339715</td>\n",
       "      <td>-0.266652</td>\n",
       "      <td>0.092180</td>\n",
       "      <td>0.218357</td>\n",
       "      <td>-0.028878</td>\n",
       "      <td>-0.064942</td>\n",
       "      <td>0.210919</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0        0.025447      0.002689         0.003600        0.000743   \n",
       "1        0.025515      0.001279         0.003084        0.000064   \n",
       "2        0.026243      0.000330         0.003101        0.000081   \n",
       "3        0.026571      0.001018         0.003208        0.000214   \n",
       "4        0.027070      0.001007         0.003137        0.000176   \n",
       "..            ...           ...              ...             ...   \n",
       "60       0.113114      0.018944         0.029888        0.008465   \n",
       "61       0.102846      0.001870         0.025450        0.001682   \n",
       "62       0.103216      0.001285         0.025450        0.001661   \n",
       "63       0.103230      0.002304         0.025570        0.001561   \n",
       "64       0.104568      0.002589         0.025515        0.001571   \n",
       "\n",
       "                 param_fp_transformer param_fp_transformer__nBits  \\\n",
       "0      MorganFingerprintTransformer()                         256   \n",
       "1      MorganFingerprintTransformer()                         256   \n",
       "2      MorganFingerprintTransformer()                         256   \n",
       "3      MorganFingerprintTransformer()                         256   \n",
       "4      MorganFingerprintTransformer()                         256   \n",
       "..                                ...                         ...   \n",
       "60  MACCSKeysFingerprintTransformer()                         NaN   \n",
       "61  MACCSKeysFingerprintTransformer()                         NaN   \n",
       "62  MACCSKeysFingerprintTransformer()                         NaN   \n",
       "63  MACCSKeysFingerprintTransformer()                         NaN   \n",
       "64  MACCSKeysFingerprintTransformer()                         NaN   \n",
       "\n",
       "   param_regressor__alpha                                             params  \\\n",
       "0                     0.1  {'fp_transformer': MorganFingerprintTransforme...   \n",
       "1                   0.325  {'fp_transformer': MorganFingerprintTransforme...   \n",
       "2                    0.55  {'fp_transformer': MorganFingerprintTransforme...   \n",
       "3                   0.775  {'fp_transformer': MorganFingerprintTransforme...   \n",
       "4                     1.0  {'fp_transformer': MorganFingerprintTransforme...   \n",
       "..                    ...                                                ...   \n",
       "60                    0.1  {'fp_transformer': MACCSKeysFingerprintTransfo...   \n",
       "61                  0.325  {'fp_transformer': MACCSKeysFingerprintTransfo...   \n",
       "62                   0.55  {'fp_transformer': MACCSKeysFingerprintTransfo...   \n",
       "63                  0.775  {'fp_transformer': MACCSKeysFingerprintTransfo...   \n",
       "64                    1.0  {'fp_transformer': MACCSKeysFingerprintTransfo...   \n",
       "\n",
       "    split0_test_score  split1_test_score  split2_test_score  \\\n",
       "0            0.017975           0.394682           0.524598   \n",
       "1            0.078758           0.449548           0.554241   \n",
       "2            0.128221           0.490253           0.575230   \n",
       "3            0.169585           0.521723           0.590890   \n",
       "4            0.204831           0.546774           0.603010   \n",
       "..                ...                ...                ...   \n",
       "60          -1.649022          -1.943461          -0.602509   \n",
       "61          -0.969593          -0.813087          -0.188690   \n",
       "62          -0.657588          -0.505782          -0.045940   \n",
       "63          -0.468371          -0.356825           0.036642   \n",
       "64          -0.339715          -0.266652           0.092180   \n",
       "\n",
       "    split3_test_score  split4_test_score  mean_test_score  std_test_score  \\\n",
       "0            0.542116           0.310238         0.357922        0.190209   \n",
       "1            0.572363           0.330543         0.397090        0.181071   \n",
       "2            0.593237           0.344076         0.426203        0.173061   \n",
       "3            0.608380           0.353866         0.448889        0.166100   \n",
       "4            0.619752           0.361324         0.467138        0.160060   \n",
       "..                ...                ...              ...             ...   \n",
       "60          -0.418328          -0.752525        -1.073169        0.606987   \n",
       "61           0.003831          -0.314764        -0.456461        0.372595   \n",
       "62           0.124510          -0.171340        -0.251228        0.289700   \n",
       "63           0.182939          -0.087318        -0.138587        0.242115   \n",
       "64           0.218357          -0.028878        -0.064942        0.210919   \n",
       "\n",
       "    rank_test_score  \n",
       "0                25  \n",
       "1                24  \n",
       "2                23  \n",
       "3                22  \n",
       "4                21  \n",
       "..              ...  \n",
       "60               65  \n",
       "61               64  \n",
       "62               62  \n",
       "63               59  \n",
       "64               57  \n",
       "\n",
       "[65 rows x 16 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_training_stats = pd.DataFrame(grid.cv_results_)\n",
    "df_training_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-12T12:11:08.447990Z",
     "iopub.status.busy": "2024-04-12T12:11:08.446959Z",
     "iopub.status.idle": "2024-04-12T12:11:08.691729Z",
     "shell.execute_reply": "2024-04-12T12:11:08.691123Z"
    },
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Best Fingerprint Method / Performance\n",
    "res_dict = {}\n",
    "for i, row in df_training_stats.iterrows():\n",
    "    fp_name = row['param_fp_transformer'] \n",
    "    if(fp_name in res_dict and row['mean_test_score'] > res_dict[fp_name][\"mean_test_score\"]):\n",
    "        res_dict[fp_name] = row.to_dict()\n",
    "    elif(not fp_name in res_dict):\n",
    "        res_dict[fp_name] = row.to_dict()\n",
    "        \n",
    "df = pd.DataFrame(list(res_dict.values()))\n",
    "df =df.sort_values(by=\"mean_test_score\")\n",
    "\n",
    "#plot test score vs. approach\n",
    "plt.figure(figsize=[14,5])\n",
    "plt.bar(range(len(df)), df.mean_test_score, yerr=df.std_test_score)\n",
    "plt.xticks(range(len(df)), df.param_fp_transformer, rotation=90, fontsize=14)\n",
    "plt.ylabel(\"mean score\", fontsize=14)\n",
    "plt.title(\"Best Model of Fingerprint Transformer Type\", fontsize=18)\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-12T12:11:08.694324Z",
     "iopub.status.busy": "2024-04-12T12:11:08.694077Z",
     "iopub.status.idle": "2024-04-12T12:11:09.027663Z",
     "shell.execute_reply": "2024-04-12T12:11:09.026932Z"
    },
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tm4KCgtxWQ17xxLqXE57suwUxRL2SN4WABLwArhkGuMKOHTuMzWYzdrvdrfP99NNPTcmSJY3dbjd2u92UL1/efPTRR07bjho1yq31zZkzx/zyyy8Ow1JTU83zzz9v/Pz8rJrtdrupWbOm2bRpk9tqM8ZY75efn5+59957zddff21SU1PdWkNe8NS6l130jfToG+7h7X0jISHBzJ4928yePdst81uxYoWx2WymePHi5oknnjB9+/Y1ZcqUMXa73XTs2NGcO3fOob27+4XNZjOjR492GPbII48Ym81matasaV5++WXz/vvvm6ioKOPj42MKFy5s/vrrL7fW16xZM3PjjTda61WRIkVMjx49zJo1a9xWR144c+aMWb16tVm9erWnS3HKU333tddeM/7+/tb2NzAw0Lz88stOt3/u7h854e7lN3r06HR9ICkpydx3333WskyrJzQ01CxbtswtdaVJm7fdbjf169c37733nklISHBrDXnp0KFDJioqyjz66KNun+/ChQvN0qVLzfHjxzNst3r16nTbck/ZunWreeedd8zEiRPN/PnzTWJiottrqFatmunbt6/ZunWr2+ed1/bu3WtatWplWrdu7elSvBIhEdI5evSoGTlypBk1apTb5rlx40bj4+Nj/Pz8TLt27cydd95pAgICjN1uN08++WS69t5wwD9w4EBjs9lM6dKlTe/evc3QoUNNq1atjM1mMyVLljQHDhxwa3033HCDCQ0NtQ4gKlasaEaOHGliYmLcVkduufuDZk7RN9Kjb7iHt/cNd+vQoYMpUqSI2blzpzUsMTHRPPjgg8Zms5m2bduas2fPWuM83S/+/vtvY7fbTdOmTc2ZM2cc2i5cuNDYbDbzxBNPeKS+jRs3mt69e5ugoCCrj9SoUcNMmjTJxMfHu62mq5Un+q63h6g54e4QkIDXvTwRonpzgEpI6T7e/uWbpxESwSvce++9plChQmbdunXWsNjYWHPrrbcau91uevTo4bDx9vQB/4EDB0yhQoVM7dq1TVxcnEPbKVOmGJvNZp577jm315ecnGzmz59v2rZta3x8fIzNZjM+Pj7m9ttvN59++qlJTk52W03IG/SNvKmPvnF1CQ0NNd27d3c6bsiQIcZms5nbbrvNCoo83S9mzJhh7Ha7+e6775y2b968ualWrZq7ynP6QTgpKcnMmjXLNGvWzOEMvLvvvtt8+eWXBfIMvGuVt4eo3oyA1704C9URIaX7ePtZqJ5GSASvUKZMGXP//fenG37hwgXTrVs3Y7PZzMMPP2wdpHp6o/2///3P2O128/nnnzttHxkZaWrXru2u8pzuVGJjY83IkSNNxYoVHb51GDBggNm+fbvbakPu0Ddyh76Rd1JTU82ePXvMtm3bzLZt28yePXtMSkqKR2rx9/c3w4YNy3D8Sy+9ZGw2m2ndurU5c+aMx/vF+PHjjd1uNydPnnTavn///qZw4cLuKs9pv7jcX3/9ZQYNGmTKlClj9ZEKFSq4rT7kjreHqN6MgPfq5u0BKiElvIXd09dEguekpqYqISFBCQkJSk1N9Wgtx48fV/Xq1dMN9/X11Ycffqju3bvro48+0sMPP+zxWiVp//79kqSmTZs6Hd+kSRPFxsa6s6R0KlasqFGjRikmJkbLly/Xvffeq1OnTmnKlCm66aabdPPNN3u0Pm9G33AdfePqM3/+fLVp00aBgYGqWrWqIiMjFRkZqapVq6pIkSJq27atPv30U7fWFB4ergMHDmQ4fuzYsXrxxRe1atUq3XHHHTp9+rQbq0svODhY0qULRjvj4+OT4ThPuO666zRp0iTt379fixYt0h133KFDhw55uqwMDRo0SFWrVvXY/I0xiomJ0S+//KJffvlFMTExHt0enzp1KsNbYk+YMEEvvviivvvuO3Xs2FFnz551c3UFS3x8vCQpMjLS6fjIyMhMt0XuEBgYqJ49e2rt2rXasWOHnnvuOZUoUUJLlixRp06dVKlSJY/W501+/vln3XvvvQ7HVcWKFdPHH3+swYMH69tvv9Wdd96pc+fOebDKf61evVqS9PLLL6tw4cIO4+655x41a9bMYxd0b9SokT744AMdOnRIM2fO1M0336ydO3dqyJAhqlChgu655x599dVXMtwjq0Dy9XQBcK+NGzdq+vTpWrNmjcNBjM1mU+XKldWyZUs99thjatKkiVvrKlOmjI4cOeJ0nM1m06xZs2SM0f/+9z+lpqaqWrVqbq3vSmkb6iJFijgdX6RIEa/4wC5dWn633367br/9dh0/flxz587VjBkztHHjRk+X5tTEiRO1YsUKfffdd26dL30jb9A38o+7+8bFixd1//336/PPP5cxRjVq1FD16tWtO2AlJiZq165dio6O1nfffadPPvlECxYskK9v/h9a3HDDDVkeGI8dO1bSpYPr9evX53tNV1qyZIliYmIkSQcPHpQk/f33307vSLR//36Fhoa6sbrs8fHxUZcuXdSlSxevDomOHj1qLWt3mj9/vqZPn64ffvhBycnJDuP8/PzUrFkz9e7dWw888IBb68pOiCpd6ht33HGHGjRo4K7SXDJo0CAtWrRIf//9t9vnXVAD3gkTJuiLL77QjBkztHz5co/WZIxRbGysEhISJF1aphUrVvTIcssqQPX19dXLL7+sjh07atmyZW6uLr3shJQffPCBO0tKJy2k7Nmzp3bu3Kn//ve/+t///qclS5Zo6dKlCg8P1969ez1aI3KOkOga0r9/f73zzjsyxqhIkSKqVauWw8H+nj17NGPGDM2cOVPPPPOMpkyZ4rbaatasaaXlzthsNs2ePVuS9L///c/ttxyV5FDfzp07JUkxMTGqVatWurb79+9XyZIl3VVatpUoUULPPvusnn32Wf3888+eLsepHTt2aM2aNW6dJ30jd+gb7uHuvjFp0iQtXbpU//nPf/TKK6+oYsWKTtvt3btXQ4YM0fz58/Xqq69q6NCh+V5bx44d9cUXX+jLL7/M9NbxY8eOlc1m07hx46xbvrvLtm3btG3bNodhS5YscRoSbdiwQTfeeKN7CnNR2bJlPV2C1/DmAFUqGCFqTrg7BCTgzRveGKIWtACVkDJ/eTKA9naERNeIadOm6e2331bz5s01duxYNWvWLN1GJTU1VWvXrtXw4cP19ttvq1atWnriiSfcUl+HDh00cOBArV27Vs2bN3faJu3DsM1m09y5c91+wL969ep0H9aXLVvm9IPwpk2bVKNGDTdV5pqGDRt6ugSvQN/IPfrG1Wnu3Llq2rSpPv7440zbVaxYUR9//LH27NmjOXPmuCUkuu+++6xQNytjxoxRlSpV3Pozxz179jgdHhgYmG7Ytm3bVLVqVd177735XZZl1apVioiIcNv8curRRx/NUft169blUyXOeXOAKhWMENWbEfDmjjeHqAUhQCWkdB9PnYVaEBASXSOmTZtmbRgz2gjb7Xa1aNFC0dHRioyM1Hvvvee2D8IPPPCA4uPjdezYsUzbpf28JiIiwq0H/KtWrXI6vFSpUumGbdmyRRcuXFC7du3yuyzLrFmznO48vMGYMWNy1P7KA7P8Rt/IHfqG67y9b8TGxuqee+7JdvuWLVu67Sy7EiVK5KgP9uzZM/+KcSIn1wCpW7duhv0ov7Ro0cKt88uptNA7J9eycGfI4c0BquT9Iao3h4AEvLnnzSFqQQhQCSnhDWyGq0ldEwIDA9W/f39NmDAhW+2HDBmit956S2fOnMnnynC1s9vtLh3sp6Sk5GNV/6JvwFO8vW+UK1dOzZo1y/ZFqe+//3798MMP1jefgKuKFy+u8PBwzZw5M1vtx44dq6+//tptfaNw4cIaMGCAxo8fn632Q4cO1ZQpU7hI9P/z9m0fcqdmzZoqWbKkfvjhh2y1b9q0qU6cOKEdO3bkc2WXbgayYMEC1ahRQy1btsyy/ezZsxUbG6uRI0fme22SMgxrAwMD0335tm3bNg0YMECPPPJIjoNXV61Zs0YRERFeezH0nC6HtWvX6p9//mHb4gRnEl0jihQpkqPT/Q4dOpStb6CArAQGBio8PDzbZ0289957Wrt2bT5X9S/6BjzF2/tGhw4dNGfOHE2fPl29e/fOtO3777+vxYsXKyoqyk3VXZKYmKjY2FhVqVLFoV+uWLFCq1evVqFChdShQ4cM77Z3rXr55ZfVrl07j19vIyORkZH6+eef1ahRo2x9i+/szMX8FBISot27d2e7/e7duxUSEpKPFRUsQUFBLoWAKBg4C9V1nIWaO95+FmpBQkh0jWjVqpXmz5+vbt266fbbb8+07fLlyzV//nx17tzZTdU5OnXqVLqL7/7xxx9au3atChUqpDZt2mR46uq16qOPPtJtt92mMmXKeLqUdOrWravff/9dXbt2zVb75cuXu/WDMH3j6kbfcN3LL7+slStX6sknn9SkSZPUtm1bVa9e3bqQZkJCgnbt2qWVK1fqn3/+Ufny5TVu3Di31TdhwgSNHj1aFy5cUGBgoN5++2317NlTzz77rN5++23rIPHll1/WoEGD9Morr7ittpyIi4vTsGHDZLPZNGPGDLfMc/jw4RoxYoRuuukmPfbYY3r44YdVvHhxt8w7OyIjI7VmzRrt2LHD6bXNPK0gBKiS94ao3h4CejNvD3glQlR4DgF0HjK4JuzcudMEBwcbu91u2rRpYyZOnGgWLVpkoqOjTXR0tFm0aJGZOHGiue2224zdbjchISFm586dbq3xww8/NGXKlDF2u91ERESY5cuXG2OMef31142vr6+x2+3Gbrcbf39/M3XqVLfWlhNHjx41o0ePNmPGjHHbPG02mylUqJDp0qWL+eKLL0xKSorb5p2VZ555xtjtdrN79+5ste/Zs6ex2+35XNW/6BvuQ99w5O19wxhj4uLiTPfu3U1AQICx2WzGZrNZ61va3/7+/qZHjx7m0KFDbqsrOjra2Gw2ExYWZu655x5Trlw54+fnZz766CNjt9vN008/bZYuXWreffddU6FCBWO3283KlSvdVl9O7Nixw1qu7mKz2UzRokWt+RYuXNg89NBDZtWqVW6rITPff/+96dmzp9m8eXO22q9bt87Mnj07n6v616FDh6z1qlq1aqZPnz5m8uTJZsaMGWbGjBlm8uTJpk+fPqZatWrGbrebihUrmri4OLfVZ4wx48ePN/7+/sZut5uiRYuaWbNmGWOM6d+/v0P/tdvtZvDgwW6t7bnnnjN2u9388ccf2WrviW1fdhw6dMhERUWZRx991G3zTHvP6tata95++21z4sQJt807ux599FHj4+NjPvjggyzbTps2zfj4+JhevXq5obJ/JSQkmF9//dWcPn3aYfjy5cvNkCFDzPDhw8369evdWlNBMG7cOPPzzz97uowMtWrVyhQtWtSkpqZmq723blu8ASHRNeT33383t956a7oD/SsP+Fu0aGF+//13t9a2adMm60A1MjLSFClSxBQtWtSsXLnS+Pj4mI4dO5o333zTvPDCCyY4ONj4+PiYn376ya01ZpenDvjT3kO73W7Cw8PNiy++aP7++2+31ZCRL774wrRs2dKsW7cuW+2XLFliRo0alc9VOaJvuAd9w1FB6BtpTp06Zb755hszdepUM378eDN+/HgzdepU880335jExES313PnnXea4sWLW8HU0aNHTalSpUxQUJB54YUXHNrGxMQYf39/c//997u9zuxISEgws2fPdmvIYbPZzOjRo83mzZtNnz59TPHixa0+UrVqVTN+/Hhz8OBBt9VTEHlrgGqM94eo3h4CZhcBr3PeHqJ6c4CaE4SU6V0tAbQ3ICS6Bu3cudN88MEHZtCgQebxxx83jz/+uBk0aJD54IMPzF9//eWRmrp27WoCAwOtD+C7d+82QUFBpmzZsiYqKsqh7datW42Pj4/p0aOHByrN2tGjR83IkSPd+mHOZrOZl156ySxcuNB06NDB+Pj4WBvy1q1bm48//ticP3/ebfUUVPSN/EXfuPb88ssvZs6cOXn+vBEREenW88cee8zY7Xaza9eudO27dOliKlSokOd1FFRpIVGas2fPmrlz55oWLVpYQUehQoXMXXfdZZYuXepVZ+BlV36te1fytgDVmKsrRPVmBLwZ89YQ1dsD1JwgpEzvagmgvQEhEbItNjbWrFmzJl+eu1q1aqZr164Owx5++GFjt9vNb7/9lq59+/btTZUqVfKlloLoygP+/fv3mzFjxpjKlStbG/ISJUqYfv36mW3btnmwUtfk57qXF+gb3ou+4VmjRo3KlwNYf39/M2zYMIdhw4cPN3a73Vy4cCFd++eee874+/vneR0F1ZX94nK7d+82Q4cONeHh4VYfKVu2rBk6dKibq8yd/Fr38kp+hljXQojqrhDQ2xS0gNfbQtSrKUAlpER+snv6mkgoOGbNmqVWrVrly3Pv379fVapUcRiWdoX/6tWrp2tfo0aNHN2R6loTHh6u4cOH659//tG3336rrl276uzZs3r77bcVGRmpRo0a6YMPPvB0mdmWn+teXqBvFBz0jatDUFCQEhMTHYb5+PhIknx909+TIzU1VX5+fm6pzRljjGJiYvTLL7/ol19+UUxMjFJTUz1WT2aqVq2q8ePHa+/evVq6dKk6deqko0ePauLEiZ4u7aqSnxezPnTokMLDwx2GlStXTpIUERGRrn3VqlV1+PDhfKklv3jqYuDeJiAgQI888ohWr16tnTt3avDgwSpdurS++OIL3X333SpfvryGDRvmsfqKFi2qtm3b6qmnntLQoUM1dOhQPfXUU2rbtm26G3Fc7tdff9XcuXPzvJ7t27erc+fO1s0sSpYsqbvuukunT59OdxH6SpUqqUOHDvrxxx/zvI68EBQUpB49eqhHjx5un3dkZKTeffddHTp0SHPmzFHz5s21Z88evfTSS6pUqZI6d+6szz//3Gv3c5nJr3WvoCEkglcIDAzUmTNnHIYVKlRIkuTv75+uvZ+fn+x2z66+qampSkhIUEJCgldvBFu3bq2PP/5YBw8e1Jtvvqkbb7xRmzZtUp8+fTxdGrKBvpF/6BsFV/ny5RUbG+swrFu3bvr888+dtt+7d6/CwsLcUZqD+fPnq02bNgoMDFTVqlUVGRmpyMhIVa1aVUWKFFHbtm316aefur2u7LDb7erUqZOWLFmi/fv3e+3d4ZBeQQtRvRkBr2fkVwh4LQSo7uTtIaUrCKAvSb+nADygXLly2r9/v8OwO+64Q6GhoU7b79271yO3RN24caOmT5+uNWvWOBwo2Gw2Va5cWS1bttRjjz2mJk2auL22rBQvXlzPPPOMnnnmGW3evDnbt4eEZ9E38h99o+CpX7++lixZ4jCsevXqTs+uky6tn7fccosbKrvk4sWLuv/++/X555/LGKMaNWqoevXqCgoKknTp1uS7du1SdHS0vvvuO33yySdasGCB0w/w3qB06dIaNGiQp8tANmUUomZ023RPhajebP78+Zo+fbp++OEHJScnO4zz8/NTs2bN1Lt3bz3wwAMeqjBjaQFvp06ddPjwYc2ZM8fTJXmNghagGmMUGxurhIQESVJwcLAqVqzo8S8DnUkLKceNG6cvv/xSM2bM0FdffaWJEydq/Pjxni4POeSdRyO45kRGRio6OtphWMOGDdWwYUOn7Tdv3qw6deq4ozRL//799c4778gYoyJFiqhWrVoOB/x79uzRjBkzNHPmTD3zzDOaMmWKW+vLifr166t+/fqeLgPZQN9wL/pGwTBo0CC1b99eFy5csM6sy8iWLVtUrVo1t36YmzRpkpYuXar//Oc/euWVV1SxYkWn7fbu3ashQ4Zo/vz5evXVVzV06FC31NejRw/VrVvXLfOC+3l7iOrNCHivbgUlQCWkhKd55xYN15ynn35aN910k5KTk7NM7Ddv3qyLFy/qzjvvdFN10rRp0/T222+refPmGjt2rJo1a5YuxU9NTdXatWs1fPhwvf3226pVq5aeeOIJt9TXokULp6fJouCjb+QOfePqdN111+m6667LVtvIyEitWrUqnytyNHfuXDVt2lQff/xxpu0qVqyojz/+WHv27NGcOXPcFhLNmjXLLfOBZ3h7iOrNCHivbt4eoBJSwlt45xqFa06jRo3UqFGjbLWtX7++9uzZk88VOZo2bZpuuOEGRUdHZ7ghttvtatGihaKjoxUZGan33nvPbR+E3f0BCO5D38gd+gY8ITY2Vvfcc0+227ds2dKrz7BDweLtIao3I+C9unl7gEpICW/hfT9oBLzQzp071bFjx2wl9YUKFVLHjh21c+dON1QGeBZ9A0gvJCREu3fvznb73bt3KyQkJB8rApAdsbGxatGiRbbbt2zZMt3Pl+C9rrvuOt17771ZBkTSvwFqTgL/3Lo8pMwoIJL+DSkbNWrk1p9zzZo1S3fddZfb5gfPISQCsqFIkSI5uq34oUOHVKRIkXysCPAO9A14u7i4OD366KN67LHH3DbPDh06aNGiRZo+fXqWbd9//30tXrxYHTt2dENlOeeJ5Qd4CgEvPImQEt6Cn5uhwDl27JimTp0qm82m4cOHu2WerVq10vz589WtWzfdfvvtmbZdvny55s+fr86dO7ultpzyxPKDe9A3coe+kfciIiJ06623erSGhIQEzZ49WzabTTNmzHDLPF9++WWtXLlSTz75pCZNmqS2bduqevXqCg4OtmratWuXVq5cqX/++Ufly5fXuHHj3FJbTnli+eUFb1j3CoK4uDgNGzaswL2/+aVDhw6aM2eOpk+frt69e2faNi3g9dbbZfPeFjyElPAWNmOM8XQRKBimTJmiN9980+3XPLnSX3/9pVq1aslmsyklJcUt89y1a5caNmyoU6dOqXXr1hke8H/zzTdatWqVgoODtXHjxgwvhOdJnlh+ueUt615GvKU++kbu0DdyJjExUWfPnlWpUqW88na8aRITE7V48WJJl66n4C7x8fF64YUX9Omnn+r8+fOSJJvNJunSbY2lS3epSbv2RJkyZdxWW054avllpqCse5mZM2eOZs+e7fHrARXE7Z6Uf8svLi5OjRo10oEDB1SlSpVsBbw//fSTR+6AlZWC+t5K0ujRozVmzBiP1u2JkO2xxx7TnDlz9N5772UrpHz66acVFRWVrbNW3a2ghpTesO55A0IiFDjHjh3T22+/LZvNppEjR7ptvn/88Yf69OmjtWvXSvr3YD9NWle69dZb9e6776p27dpuqy0nPLX8kP/oG7lD33B04MABxcbGqkmTJg4fxN9//329/vrr+vvvvyVd+snhPffco0mTJql06dKeKtdrnT59Whs2bNCuXbuUkJAgSQoODlb16tXVpEkTFStWzMMVep+rYd07c+aMChUqlK1rn3iSN4aAkmeXHwGv53lDiOqJkI2Q0vO8Yd3zBoRE14ikpCQlJCSoXLlyDsNXrFih119/XZs2bdLZs2cVERGhrl27avDgwSpcuLCHqvVuu3bt0urVq50e8Ldo0SLbdxS5VhSkde/48ePy9fW1bjXqzN69exUTE8PPGJygb7jOG9e9bt26acOGDQ5nKb3wwgt6/fXXZbPZVKVKFRUvXly7d+/WyZMnVblyZW3cuFGhoaFuqe9q9+uvv2rbtm3q3r27p0txu4Kw7v3xxx+aPHmyjhw5ojZt2ujpp5+W3W7XsmXL9Nxzz2n37t2y2+26+eabNXnyZNWvX99ttRUEBWH5EfDmn88//1wRERG66aabPF1KhjgLNXe8NaQsCOueVzC4JkRFRZkyZco4DHvjjTeM3W43NpvNBAQEmFKlShmbzWbsdrtp2LChSUpK8lC1V6fY2FizZs0aT5fhdgVh3Vu7dq254YYbjN1uN3a73TRp0sR8//33TtuOGjXK2O12t9Z3tbtW+4Yx3r3uVa5c2fTo0cP6e/fu3cbHx8fUrl3bbN++3Rp+4cIFM2bMGGOz2Uy/fv3cVt/lUlNTzZ49e8y2bdvMtm3bzJ49e0xKSopHaskr7ny/vW35efu6988//5jg4GBjs9msfdczzzxj1q9fbwoVKmQCAwNNZGSkqVChgrHZbKZo0aJmx44dbqvP213ty++XX34xc+bM8XQZXs1ms5nHH3/c02V4tVOnTplvvvnGTJ061YwfP96MHz/eTJ061XzzzTcmMTHR0+UVWKx72UNIdI247rrrTLdu3ay/Dxw4YPz9/U14eLj5+uuvTWpqqjHGmCNHjphevXoZm81mXnrpJU+Va4wxJiUlxZw8edKcPHmywB/sG+P+D3jesvy8fd3bsWOHCQwMNDabzdSoUcPccMMNxmazGV9fX/PKK6+ka+8NIZG3vLd55VrtG96+7gUEBJihQ4daf0+bNs3Y7Xazbt06p+1vu+02U6lSJTdVd8m8efPMbbfdZgICAqygLe0REBBg2rRpY+bPn+/WmvKKO95vb11+3r7uPfHEE8Zut5s33njj/9q787goq/0P4J/nGRiQNcQF4arAFbdAEa+Ky2VxxfQqUlpaApZrhjvghoD9UrFcKm1REcW6LWSlleVSqECJdU20kgQRxCUzDVlFgu/vDy5zHRkQlXnmDHzfr9e8Xs15jjPfvucwPPPlec6hrKwsWr9+PanVavLx8aE+ffrQb7/9pun75ptvkiRJWkUvpYlWBDS2/N2v5lzgJSJKT0+/50OSJAoMDNRqY42jORcpee41Hi4SNRMWFha0ePFizfPt27eTLMu0b98+nf379etHbm5uSoWncezYMXruueeoU6dOZGJiojlZValU1KlTJ5o6dSp99913isfVGJQ4aRAxf6LPveDgYJIkSeuLUHp6OnXt2pVkWa5VsDJUkUjEsW0szfVnQ/S5Z29vTy+88ILm+Zo1a0iW5Tqv9Fu0aBGZmZkpEltFRQUFBgZqrkjs2rUr/etf/6Knn36ann76afrXv/5FXbt21VylEBgYSBUVFYrE1lj0Od6i50/kuUdE1KVLFxo5cqRW24gRI0iWZUpLS6vVf8iQIdS+fXulwtMQtQhoLPl7UM25wEtEms+N+30YgohFtofVnIuUxjT3RGdi6NvdmDLUarXmvlag+n5XAHWubTFo0CBs2rRJkdhqzJ07F5s2bQIRwdLSEt26ddOsz1FYWIjz588jPj4e27dvR1hYGDZu3KhofKITNX+iz73k5GQ89thjmDBhgqatb9++SE9PR2BgIFatWoXKykqsWrVKsZjuJurYGgtR8yf63OvVqxf2798PIoIkSZod6TIzM+Hl5VWrf2ZmJuzt7RWJbe3atdizZ49mTYYOHTro7HfhwgUsXrwYH3zwAV5++WUsWbJEkfhEJ3r+RJ57AJCfn49x48ZptXl6euLgwYPw9PSs1d/Ly0uzsL8S/vrrL4wfPx579+4FEaFLly5wc3PT+tzLysrC119/jW+++QbvvfcekpKSYGKizNcC0fMnMtHHtoalpSUCAwOhUqlqHSMiJCYmws3NDQMGDFA0rhoffPABtm7dirS0NNy+fVvrmFqtxqBBgzBt2jSt38/sf0TOn+hzz2gYqjrFlDVo0CDy9PTUPH/nnXdIluU67/F+8sknqXXr1kqFp7mc2MfHh44cOaKzCl1ZWUmHDx+mf/7znyTLMr311luKxdcY9FnZFzl/os89tVqtdaXTncrKymjYsGEkyzJFRkYSkfJXc4g8to2luf5siD73Pv74Y5IkicLCwqiyspLKy8upS5cu5O3trXU7CBHRtm3bSJZlevbZZxWJrUuXLjRgwIAG9/f29qYuXbroMaLGp8/xFj1/Is89IqJ27drR888/r9U2a9YskmWZLl26VKv/tGnTyMrKSqnw6KWXXiJJkmjixImUl5dXZ7+8vDyaOHEiybJMq1atUiw+0fP3sPT5syv62BJVX/lnbm5O/fv3pzNnzujsI0kSTZs2TdG4iMS/irIxNOerUEWee8aGi0TNRHx8PEmSRC+//DIRVS+G5ujoSKNGjaKysjKtvgcPHiRTU1MaP368YvH17NmTPDw8GvRBcvv2bXJ3d6eePXvqP7BGpM8PbZHzJ/rcc3R0rHWyeqdbt25pvqyHh4cr/kVd5LFtLM31Z0P0uUdENH36dJIkidzc3GjRokUUERFBJiYmZGlpSb6+vjR27Fjq3LkzybJMDg4OdPHiRUXiunvNmntZvHgxmZub6zGixqfP8TaG/Ik694iI/Pz8yMnJiYqKioiIqLCwkBwdHcnGxobWrl2r1ffmzZvUtm1b6t27t2LxiV4EFD1/D6s5F3hrnD59mry8vKhFixYUFxenWX+yhqG+qBtDke1hNfcipahzz9hwkaiZqKqqotGjR5Msy+Tn50ebNm2iF198kUxMTMjJyYlCQkJo7ty5mi8kVlZW9MsvvygWX4sWLer8i7oukZGR1KJFCz1G1Pj0+aEtcv5En3s+Pj7Uo0ePevvceVWHo6Ojol/URR7bxtJcfzZEn3s1Nm7cSC1bttT8ZbBmR6I7HyNGjKBz584pFlO7du3uq5j8xBNPULt27fQYUePT58+FseRPxLlHRLR7926SJIlcXV3pmWeeIRcXF1KpVPThhx+SWq2mRYsW0eeff04JCQnk4eFBsixTXFycYvGJXgQUPX8Pq7kXeGtUVFRQdHQ0mZqaUr9+/bTO7Qz1Rd1YimwPg4uUYs49Y8NFomakvLyc5s2bR6amppqFuu7cfrTmv7t37674Su+tWrW6r50rgoODqVWrVvoLSA/0+aEtev5EnnsvvfQSybJMJ0+erLdfzZf1mpiVIvrYNobm+rMh+ty7O4Y9e/bQihUraObMmTR9+nQKDw+nbdu2Kf4FnYjo2WefJZVKRVu2bLln37feeotUKhVNnTpVgcgajz5/Lowpf6LNvRoLFiwglUpFkiSRmZkZbdy4kYiIYmNjtRZPlSSJfH196fbt24rFZgxFQJHz97C4wKvtP//5Dz366KNkbm5OL730Ev31118G+6JuTEW2B8VFyv8Rae4ZGy4SNUOXLl2ijRs3UnBwMAUEBNDw4cNp/PjxtHz5cvrmm28Msir9+PHjydzcnPbv33/Pvl9++SWZmZnRhAkTFIis8ejzQ9tY8ifi3MvKyqKnnnqqQduF3rp1i0JCQsjPz0+ByKoZy9g+jOb6syH63BPZlStXqH379iTLMnXq1IlmzZpF69evp/j4eIqPj6f169fTrFmzqFOnTiTLMnXo0KHWWjai0+fPRXPInxJ+++03OnbsGF2/fl2r/eDBg7Rw4UIKCwuj999/X/HfbcZSBBQ1fw+LC7y1lZeXU0REBKlUKvLy8iJZlg3yRd0Yi2z3i4uU2kSZe8ZGIiIy9OLZjGVlZaFPnz4oKirC4MGDMWzYMLi5ucHW1hYAcPPmTWRlZeHAgQNITk6Gra0t0tPTNTueGIPY2FisXLkSlZWVjf7azSF/zVVzGFv+2WAP4urVq4iIiMCHH36o2UFRkiQA1TuYANW7rNTs4OXg4GCwWB/Ezp07sWPHDiQnJ+vl9Zt6/pqz3377DX379sWlS5fg6upa5+fewYMHkZOTg7/97W84fvw42rZta+DImwZ9/k4z9rH97rvvEBISguzsbEydOhVbtmxR9P2fe+457Ny5E2+++SamTZtWb9+3334bs2fPxpQpU7B161aFInx4+px/xpw/Q889Y8NFIiaMX375BbNmzdJsc1pzslqjZqr6+PjgjTfeQPfu3RWP8WFs3LgRr776Ks6fP6+X12/q+WvOmvrY8s+G+HJzc5GcnIyzZ8+ioKAAsiyjTZs2+Mc//oFhw4ZBrVYbLLbi4mJ89913yMrKws2bNwEAtra2cHNzg7e3N6ytrQ0WW10KCwtRVlaG1q1bQ5Zlg8Yiev5Ennsi4yKg4XCBt36VlZUoLi6GmZkZzM3NFX1vYy+yNQQXKetmyLlnbLhIxABUnySWlpaiVatWBj9hzcrKwuHDh3WesPr6+qJz584GjU90xpY/keZeQ9y4cQPFxcXo0KGD4u9tbGMrGmPPnyHm3vnz5zF79mzs37+/1jEigiRJsLe3R3R0NGbPnq1YXI3h1KlTOHnyJIKDgxv9tS9duoS8vDx4e3trfa69/fbbWLduHc6dOwcAsLS0RFBQENauXYs2bdo0ehz6pM/8AcY/91q2bImQkBBs2LDBoHGIXgSsiyj506W0tBSmpqYwNTU1aBzGOraGZuxFtnvhIiVrFErf38YMIy8vj27evFmr/bPPPqOePXtqFgi0tramqVOn0o0bNwwQ5cPJy8ujI0eO6OW1i4uL6dKlS7Xav/rqKxo2bBjZ2dmRubk5de3alaKjo6m0tFQvceiTvvLX1OZeaGgoqVQqQ4dxX/T5s3Gn69ev6xxrQ8TSmESJWem5l5+fTw4ODiRJEvXq1YuCgoKoV69eJEkSeXp60iuvvEIhISFkb29PsizTzJkzFYutMehz3YaJEyeSs7OzVlt4eDjJskwqlYrc3NyoT58+ZGdnp9nl6dq1a3qJRV/0mb+mMPeMfXHUjIyMBq2Xpi+GzN/PP/9Mzz33HI0ZM4Zee+01zZpIn332GXXu3JlkWSYTExPy8fGhH374wSAxPgxDj60oioqK6MCBA7R582ZatWoVrVq1ijZv3kwHDhygwsJCQ4dXpz179lBGRoahwzDa/LGG4SJRMyHLMq1cuVKrLTExkVQqFcmyTG5ubtS/f3+ysbHRnITdunXLQNE+GH2esE6ZMoUcHBy02jZs2KDZecPc3Jxat26t2ZGjT58+VFJSopdY9EVf+Wtqcy80NNRgO0w9KH3+bBARpaSkkLu7u6bg5+3tTUePHjVILPogSsxKz70pU6aQLMv00UcfabXv3r2bVCoVbd++nYiqTxTHjx9PsizTJ598olh8D0uf4+ri4qK1q152djapVCrq3r07/fTTT5r2iooKWrlyJUmSRHPmzNFLLPqi79+5Is+9bt263fMhSRLZ2dlpnnfv3l2x+BqDPsdX5Pzl5OSQra2t1g6sYWFh9O2335KpqSlZWFiQl5cXtW/fniRJIisrK8rMzFQktsaixO+033//nV544QXy8PCgXr160eLFi2stUH5nPMb0xzdDF9kkSaLp06cb7P0flr7z15TnnpK4SNRMSJJEsbGxmufFxcVkZ2dH9vb29PXXX2vaS0pKaOLEiSTLMq1bt84QoT4wff7S69y5M02aNEnz/NKlS2RmZkZOTk705ZdfUlVVFRERXbt2jaZOnUqSJNHy5cv1Eou+6Ct/TW3ucZFIW2ZmJllYWJAkSdSlSxdyd3cnSZLIxMSE1qxZo2gs+iJKzErPPUdHRxo3bpzOY0FBQeTu7q55Xl5eTh07dqShQ4cqFd5DU3Kb4LfeeotkWabU1FSd/YcMGUIdO3bUSyz6os/8iT73aooHNYUEXQ9dx42JPsdX5PzNmDGDZFmmDRs2UFZWFq1fv57UajX5+PhQnz59tHb5e/PNN0mSJK2CsDHQ9++0goICzc6Id45fu3btdF6VK8rv2IbSZ7zp6en3fEiSRIGBgVptxkSf+Wvqc09J4i8AwvTi0KFDKCgowMqVKzF48GBNu4WFBbZv3w4nJyckJSUZMEKxXLx4UWsdkP3796OiogJbt25FQECA5l7cVq1aYevWrejbty8++OADQ4UrNNHmnqur6309du/erVhsxmDVqlUoKyvD+++/j8zMTJw+fRrHjh1Dp06dsHTpUkRFRRk6RGGJPvf++OOPOnd569SpE7KzszXP1Wo1Ro0ahRMnTigVntAsLS1RVFSkeV5QUAAA6NWrl87+vXr1wm+//aZEaEZB9LnXo0cPWFpaYvPmzaiqqtL5ICJMnTpVq41VEzl/hw8fxogRIzBv3jx06tQJ8+fPh7+/P1JTU7Fx40atBXhnzpyJwYMH45tvvlEkNmOxevVqnDt3DjNnzsTly5dx7do1rFmzBoWFhQgICMCXX35p6BCF5e3tjf79+9f7kCQJe/fu1Wpj1XjuNR4TQwfADCMrKwuSJGH06NG1jpmbm2Po0KH4+OOPDRCZmNRqtWZxNqB60TagejckXQYNGoRNmzYpEpuxEW3u5ebmQpblBi9AWVFRoeeIjEtycjIee+wxTJgwQdPWt29fpKenIzAwEKtWrUJlZSVWrVplwCjFJPrcc3BwwKlTp3QeO3XqlGY3kxo2NjYoLS1VIjTh9erVC/v379cssFxT8MjMzISXl1et/pmZmbC3t1c6TGGJPve+//57xMTEYM6cOfjoo48QHx8PZ2dnxd7f2Imcv/z8fIwbN06rzdPTEwcPHoSnp2et/l5eXpqdM1m1PXv2wMvLC5s3b9a0RUREYPjw4Rg1ahSCgoKQlJSk8zyQVf+RITAwECqVqtYxIkJiYiLc3NwwYMAAA0QnNp57jYevJGqmav4iU9eK823btkVZWZmSIQnN3d1da5eA9u3bA6i+wkiXixcvwsbGRpHYjI1oc8/R0RGPPvooysrKGvR45plnFIvNGFy9ehUeHh612m1sbLBv3z4MGTIEcXFxWLx4sQGiE5vocy8gIAAHDhzQOtkCgDfeeAMHDhyAv7+/Vnt+fr4w29wa2vPPP4/s7GzMnTsXVVVVGD16NNzc3DB79mzNHxlqxMfHY9++fQgICDBQtOIRfe6ZmpripZdeQlpaGi5fvgwPDw/+w9B9EDl/tra2KCws1GqreV5zReCdCgoKoFarlQjNaOTm5sLPz69Wu6enJ1JSUtCmTRs88cQT2Lt3r/LBCW716tWoqKhAdnY2IiMjkZCQoPXYsWMHAMDX11ernVXjudd4uEjUjOTm5uLo0aM4evSo5qqYK1eu6Oz722+/wc7OTsnwhDZlyhRkZGTglVdeAQCMHTsWDg4OWLhwIW7duqXV99ChQ/j44491fkg1VyLPvd69e+PMmTNaV4rVp+bWQlatVatWtU6oa5ibm+Ozzz7DkCFD8PLLLyMiIkLh6MQm+txbsWIFWrVqhTlz5sDR0RH9+/eHo6MjwsLCYGVlhdjYWE3fyspKHDx4EN7e3orGKKpx48Zh2rRp2LRpE7p27Yply5Zh7Nix+OGHH/D3v/8dfn5+CAwMRJcuXTB9+nS0adMGK1euNHTYwjCWude3b1+cPHkS06ZNw7x58+Dr64tz584pHoexEjF/Xbp0wZ49e1BcXAwAKCoqwp49e2BlZYV3331Xq29hYSH27t2LLl26GCJUYVlaWmq2Qr+bq6srDh8+jLZt22LChAnYs2ePwtGJLTIyEt9//z3Ky8vh5eWFtWvX1plLVhvPvcbDRaJmZOfOnfD394e/vz+io6MBVN97rcuZM2eEufRXBFOmTMGoUaMQGRkJf39/7Ny5E7NmzcL+/fvRqVMnhIaGYt68eRg+fDhGjBgBMzMzrZPY5k7kuderVy/89ddfyMjIaFB/ql7wX89RGY9OnTohNTW1zuNmZmbYu3cvhgwZgnXr1mHLli0KRic20eeek5MTvvvuO4wcORJ//PEH0tPTce3aNfj5+SElJQWdO3fW9K2oqMDHH3+Ml19+WbH4RPf2229jw4YNuH79OtatW4dXXnkFlZWVKC0txdGjR7F3715kZWVh2LBhSEtLg5OTk6FDFoYxzT0zMzOsX78e33zzDS5evIgePXpg/fr1BonFGImWv7CwMFy+fBk9e/bE5MmT0bNnT1y9ehXbtm3D8uXLER4eji+++AI7duzAoEGDcO3aNa3brRng7OyMH3/8sc7jLi4uSE5O1nxZP3DggILRic/d3R3p6emIiIjA8uXL0b9/f5w5c8bQYRkFnnuNh9ckaiZqvpjf7ZFHHqnVlpWVhe+//x6zZ8/Wc1TGQ5Ik7N69G5GRkdi8eTOOHj0KoPpL2+XLl7Fr1y7Nl7du3bohISEB3bp1M2TIwhB97gUHB8PFxQVt2rRpUP9169ZxAfAOI0aMQFRUFDIyMtCzZ0+dfczNzbF3716MGTMGhw4d4qux/ssY5p6rqys+//xzlJeX4/r167Czs0OLFi1q9TM3N8fAgQMVjc0YzJ07FzNmzMCBAwfwn//8B7///juqqqpga2uLLl26wN/fH66uroYOU0jGNvd8fHxw+vRpLFy4EIsWLTJ0OEZHlPwFBQVh/vz5ePXVV3H+/Hmo1WqsW7cO48ePx5kzZxATE6MpYhERfHx8MH/+fIPFKyJfX1+8/vrruHr1ap23gbq6uiI5ORn+/v747rvv+LzgLiYmJoiJicGYMWMQHBwMLy8vREVFITIy0tChCY3nXuPhIlEzUdcXdV3atWuHnJwctGzZUo8RGR+1Wo0NGzYgPDwcSUlJOHHiRK0T/sGDB8PX1xeyzBfp1RB97tXsHNVQ9vb2vMDsHSZMmIDTp0/XWyQC/nfr2YwZM5CXl6dghOIyprlnZmYGR0dHg7y3vjg7O9e5+UBjMjc3x5gxYzBmzBi9v5eSlMqfMc09CwsLvPnmm3jyySeRkZEBd3d3Q4dkVETJ37p16xAREYHc3Fy4ublpzklWrFiBAQMG4KuvvsLt27cxcOBAjB8/ns/57hIUFIR///vfSExMRHh4eJ39am7/8ff3R35+voIRGg8vLy+cOHECUVFRWLFiBXbv3s1FjXrw3Gs8XCRitVhZWcHKysrQYdw3W1tbrW3q9cXR0RFz587V+/soTan81cdY557o9Dm2nTp1wnvvvdegvmZmZppFF42JCD8bIrh06RKysrJw8+ZNANV5cXNzE/I2qcLCQpSVlaF169Z1foELCQlBSEiIwpEZB9HyZ0xzDwD8/PyMfl1CpYqAuoiQv7Zt2+q8EmHo0KEYOnSoASJqPPoe24EDB9a57uTdXFxckJubq7dYmgK1Wo24uDgEBgYiJCSElzyoB8+9xiMRz7RmpaKiAqdPn4aJiQk8PDzqrEafOnUKJ0+eRHBwsMIRsqbK2OZeSUkJtm7dirS0NJSUlMDZ2RmTJk3CoEGDDBoXa/pEmnu3b9/Ghg0bsG3bNuTk5Ojs4+LigunTp2Pu3LkwMzNTJK5Lly4hLy8P3t7eWkWMt99+G+vWrdMsfmtpaYmgoCCsXbu2wbf1Nbbc3FwkJyfj7NmzKCgogCzLaNOmDf7xj39g2LBhBtkZyRjyJ+rc08XYilgAUFpaClNTU5iamho6FKPMn8hEGtumaOfOndixY4fWrsdKqaysRHFxMczMzGBubq74+zcGQ+aP3QdizcaHH35I9vb2JMsyybJMf/vb3+jdd9/V2TcmJoZkWVYstuLiYrp06VKt9q+++oqGDRtGdnZ2ZG5uTl27dqXo6GgqLS1VLLaGKCoqoqtXr1JlZaVB3l/0/Ik89/z9/Wnnzp1abefOnSMXFxeSZZkkSdI8ZFmm5cuXKxbb3a5fv043b96st09eXh4dOXJEoYju3/Xr1ykvL89g7y1S/kSfe8XFxdSvXz+SJImsra0pICCAwsLCaNmyZbRs2TIKCwujgIAAsra2JlmWydvbm4qLixWJbeLEieTs7KzVFh4eTrIsk0qlIjc3N+rTpw/Z2dmRJEnk6upK165dUyS2Gjk5OTRy5EjN596dj5oxbd26NW3atEnRuIjEz5/Ic69GeXk5rVmzhjp16qRzjGVZpr///e8UFxdHt27dUjQ2IqKff/6ZnnvuORozZgy99tprmvOTzz77jDp37kyyLJOJiQn5+PjQDz/8oHh8ouevPnZ2djRv3jyDvb/oY3u3ixcvUnJyMn366af06aefUnJyMl28eNHQYdVpz549lJGRYegwjJZI+TO2uScSLhI1E+np6aRSqUitVtOIESNo9OjRZG5uTrIs08yZM2v1V/qL+pQpU8jBwUGrbcOGDZqTaXNzc2rdurXmxLpPnz5UUlKiWHx5eXk6v1x+9tln1LNnT80JjbW1NU2dOpVu3LihWGxEYudP9LknSRLFxsZqtfXt25ckSaLg4GBKS0ujX3/9lXbu3EkODg4kyzIdOnRIsfiIiFJSUsjd3V0zz7y9veno0aM6+yqdv/sVGhpKKpVK0fcUNX+iz73w8HCSJIkWL15c7+dFSUkJRUZGkiRJFBERoUhsLi4uFBISonmenZ1NKpWKunfvTj/99JOmvaKiglauXEmSJNGcOXMUiY2IKD8/nxwcHEiSJOrVqxcFBQVRr169SJIk8vT0pFdeeYVCQkI0xXNdn4X6JHr+RJ57ROIXsXJycsjW1laryBwWFkbffvstmZqakoWFBXl5eVH79u1JkiSysrKizMxMxeITPX/3IkkSTZs2zSDvLfrY1jDmIqAkSTR9+nRDh2G0DJ0/Y557IuEiUTPx+OOPk6mpKaWmpmra8vLyyMfHh2RZppCQEKqqqtIcU/qLZufOnWnSpEma55cuXSIzMzNycnKiL7/8UhPbtWvXaOrUqSRJkqJ/VZdlmVauXKnVlpiYSCqVimRZJjc3N+rfvz/Z2NhovgQo+cEjcv5En3t3f1FPT08nSZK0vkDVOHPmDKnVagoKClIsvszMTLKwsCBJkqhLly7k7u5OkiSRiYkJrVmzplZ/YygSKRmfyPkTfe45OztTQEBAg/sPHz681tUp+mJubk5LlizRPH/rrbdIlmWtz5k7DRkyhDp27KhIbETVhXtZlumjjz7Sat+9ezepVCravn07EVVfhTp+/HiSZZk++eQTxeITPX8izz0i8YtYM2bMIFmWacOGDZSVlUXr168ntVpNPj4+1KdPH/rtt980fd988806P3f0ReT8devW7Z4PSZLIzs5O87x79+6KxEYk/tgSiV0ETE9Pv+dDkiQKDAzUalPS77//Ti+88AJ5eHhQr169aPHixXT9+nWdfWNiYhT9w5vo+RN57hkbLhI1Ew4ODjR+/Pha7RUVFTRp0iSSJImeeeYZzZd1pb9oWlhY0OLFizXPt2/fTrIs0759+3T279evH7m5uSkVXq0vc8XFxWRnZ0f29vb09ddfa9pLSkpo4sSJJMsyrVu3TrH4RM6f6HPv7rHdtGkTybJc56WygYGB5OjoqFR4FBwcTJIk0QcffKBpS09Pp65du+q8BYmLRNpEzp/oc8/MzEyrkHAvS5YsITMzMz1G9D/29vb0wgsvaJ6vWbOGZFmu8wvnokWLFIuNiMjR0ZHGjRun81hQUBC5u7trnpeXl1PHjh1p6NChSoUnfP5EnntE4hexunTpQiNHjtRqGzFiBMmyTGlpabX6DxkyhNq3b69UeELnr+bqnDtv9737oeu4UkQfWyKxi4A143e/D6UUFBRoroC5c361a9dO563whjhnFjl/Is89Y8O7mzUTN27cgJubW612ExMTvPPOOzA1NUViYiKqqqqwa9cuxeNTq9UoLy/XPL969SoA1Ln7wqBBg7Bp0yZFYtPl0KFDKCgowKZNmzB48GBNu4WFBbZv347U1FQkJSVhwYIFisQjcv5En3t3KywsBAB07txZ5/HOnTtj3759isWTnJyMxx57DBMmTNC09e3bF+np6QgMDMSqVatQWVmJVatWKRbTne5nC3cA+OOPP/QUiW6i5+9Oos09BwcHnDx5ssH9T5w4AQcHB/0FdIdevXph//79ICJIkqT5jMnMzISXl1et/pmZmbC3t1ckNqB6nuv63AOqdwS8cxzVajVGjRqF999/X6nwhM+fyHMPAK5cuYKJEyc2uH/v3r1x5MgRPUakLT8/H+PGjdNq8/T0xMGDB+Hp6Vmrv5eXF1JSUhSKTuz89ejRAzk5OYiLi8OsWbN09pFlGVOnTsWWLVsUielOoo8tACQlJWHEiBFYvXp1vf0sLCywZs0a/Pjjj/jwww8RFxenSHyWlpYIDAyESqWqdYyIkJiYCDc3NwwYMECReO60evVqnDt3DrNmzUJUVBRMTU0RHx+PlStXIiAgALt378bIkSMVj+tOIudP9LlnTHTva8qaHAcHB1y7dk3nMUmSkJCQgMmTJ+O9997D008/jb/++kvR+Nzd3bVWuW/fvj0A4OLFizr7X7x4ETY2NorEpktWVhYkScLo0aNrHTM3N8fQoUNx5swZxeIROX+iz72aOGo4OjoCAIqLi3X2LSkpgYWFhSJxAdUFPw8Pj1rtNjY22LdvH4YMGYK4uDgsXrxYsZjulJubiwsXLuDKlSsNepSWlioan+j5E3nuBQUFYf/+/Vi2bBnKysrq7FdWVoalS5fi4MGDePzxxxWJ7fnnn0d2djbmzp2LqqoqjB49Gm5ubpg9e7amSF4jPj4e+/btQ0BAgCKxAdWfe6dOndJ57NSpU7C1tdVqs7GxUfRnQ/T8iTz3APGLWLa2tpqic42a5wUFBbX6FxQUKLrLnsj5+/777xEWFoY5c+ZgyJAhwm2RLfrYAtVFwF69ejW4f+/evRu8bfnDWr16NSoqKpCdnY3IyEgkJCRoPXbs2AEA8PX11WpXyp49e+Dl5YXNmzfDwcEB9vb2iIiIQGpqKuzs7BAUFITPP/9csXjuJnr+RJ57RsewFzIxpQwfPvyetxdVVVVpbs2wsbFR9PLA+Ph4kiSJXn75ZSKqXqfB0dGRRo0aRWVlZVp9Dx48SKampjpvYdKXu28LiYuLI1mWqby8XGf/xYsXk1qtVio8ofMn+tyrWVvAxcWFXFxcyNHRkWRZpuTkZJ39R48eTV26dFEsPkdHR3r++efrPH7r1i0aNmwYybJM4eHhil967OTkRD169Ghw/5CQEEXjEzl/os+9wsJC8vT01Pxcjhw5kubMmUNRUVEUFRVFc+bMoZEjR2qtxVZYWKhYfNOnTydJksjNzY0WLVpEERERZGJiQpaWluTr60tjx47V7PTj4OCg6I4m06dPJ1mWa+1ctnnzZpJlmZ566imt9qefflrRNX+IxM6f6HNv/vz5JMsyLV26tN7dQktLS2nJkiUkyzItWLBAsfj8/PzIycmJioqKiKg6n46OjmRjY0Nr167V6nvz5k1q27Yt9e7dW7H4RM8f0f9uS7aysqLXX39d65ghF64WfWyJiDp27Fjrlrj6jBgxQtHPv9OnT5OXlxe1aNGC4uLitNbFJDLs+Jqbm9PChQt1Hjt37hx16NCBzMzMaM+ePURkmCUGRM6f6HPPmHCRqJnYsGEDSZJU544+NaqqqigkJERzz6lSqqqqaPTo0STLMvn5+dGmTZvoxRdfJBMTE3JycqKQkBCaO3eu5suclZUV/fLLL4rFJ0kSTZkyhY4cOUJHjhyhlStXkizLlJubq7N/aGgotW3bVrH4RM6f6HOvY8eO5OzsXOtx90LlRNUnrDY2NrW+4OmTj4/PPYswZWVlmrGtKTQoZcyYMWRqatrghdqVXpNI5PyJPveIqu/bX7FiBTk5OdW5PoeTkxNFR0cruuNkjY0bN1LLli3rXUdkxIgRdO7cOUXjunjxIrVp04ZkWaZ27dqRt7c3tWvXjmRZJhsbG/r11181ff/66y9q06YNPfnkk4rGSCRu/ojEnnuiF7F2795NkiSRq6srPfPMM+Ti4kIqlYo+/PBDUqvVtGjRIvr8888pISGBPDw8SJZliouLUyw+0fNX49atWzR//nxSqVTk4+ND2dnZRGTYL8Gijy2RcRQBKyoqKDo6mkxNTalfv35a58SGHF97e/t6c5GTk6MpFH366acGW4dS1PwZw9wzFlwkaiYuXbpEixcvbtDuKVVVVRQdHU2hoaH6D+wO5eXlNG/ePDI1NdUsdKZrkcDu3bsrvtPA3Qu11TzfsWOHzv79+vWjfv36KRqjqPkzhrnXUJmZmRQTE6Nz8UB9eemll0iWZTp58mS9/WoKHUoX2aKjo0mSpAbPqZpCoFJEz19DGWLu3e3s2bP0xRdf0L///W/697//TV988QWdPXvWYPHUKCsroz179tCKFSto5syZNH36dAoPD6dt27YZpLhR49y5czRq1CgyNTXV7Kg3ePDgWguTl5WVUWpqKl24cMEgcYqavzuJOPdELmIRES1YsIBUKhVJkkRmZma0ceNGIiKKjY3VOqeRJIl8fX3p9u3bisYnev7udOTIEXJ1dSULCwtat26dQb8EE4k/tsZSBCQi+s9//kOPPvoomZub00svvUR//fWXQce3d+/e5O/vX2+fmiuK1Go1DRgwwKDnLKLlz5jmnugkIiJD3/LG2J0uX76MpKQknDhxAr///juqqqpga2uLLl26YPDgwfD19YUsK7ucVmxsrM52T09PjB07VqstKysLXbt2xezZs/Haa68pEZ4WEfPHHlx2djaioqIwcuRIBAcH19u3vLwcM2bMQF5entYaVfqUk5ODlJQU+Pr6wtnZ+Z79r1+/juLiYnTs2FH/wUH8/Bm7vXv3wtnZGT169DB0KMIqLy/H9evXYWdnhxYtWhg6nCZDpLmXlZWFrKws3Lx5E0D1ujFubm51Ll6ulKtXryI3Nxdubm5o2bKlpv3QoUP46quvcPv2bQwcOBDjx4836HmBqPm7U2lpKRYuXIi3334bAAy2cHUN0ce2tLQUcXFxiI+Px+XLl3X2cXR0xNSpUxEREaHoent3u337NqKiorBu3Tr07NkTJ0+exHPPPWeQ8V24cCFef/115Ofno23btnX2y8nJgb+/P/Lz8yFJEiorKxWMUptI+QOMa+6JjItEjDWy4uJiXL9+HS1btoS1tbWhw2GMMb0x5C4/rHnjuccM4fDhw8jIyIC7uzuGDBli6HCMgjEUAQHgu+++Q0hICLKzsw322ZKWloYnnngCCxYsQHh4eL19z58/rykUGbJIVEOE/N3NWOaeiLhIxBhjjLFajh8/fs8+3t7eGDt2LJYsWaJp69u3rz7DqiU3NxfJyck4e/YsCgoKIMsy2rRpg3/84x8YNmyY4jv76HLp0iWdJ6pOTk4GjkzM/BnL3KuoqMDp06dhYmICDw8Prd0K73Tq1CmcPHnynlczNjecPyaayspKFBcXw8zMDObm5oYOx+hw/poOLhIxo1BcXIzS0lK0atXKoJdEG+sJjSj5Y/p348YNFBcXo0OHDgaNo6SkBFu3bkVaWhpKSkrg7OyMSZMmYdCgQQaN615EyZ8IZFmu8zOuPkr9RfP8+fOYPXs29u/fX+sYEUGSJNjb2yM6OhqzZ89WJKY73b59Gxs2bMC2bduQk5Ojs4+LiwumT5+OuXPnwszMTNH4RM6f6HMPAJKSkjBr1iz8+eefAKpvX4iLi8OkSZNq9Y2NjcXKlSuF+Gt/jZYtWyIkJAQbNmwwyPsbS/5ELvDWxdBje7/i4+ORlpaG7du3GzoU1szw3KubiaEDYAwALly4gEceeQQ2NjZa7Z9//jmWL1+O06dPAwAsLS3x5JNPYu3atbCzs1M0xvs5ofnkk0+wcuVKxYpExpA/poyFCxdi165d+OuvvxR5v8GDByM0NFRrrufk5GDo0KHIy8vDnX+HePvtt7F06VK8+OKLisT2IJTOn+gsLS0RGBgIlUpV6xgRITExEW5ubhgwYICicV28eBEDBgzA1atX4enpCRcXF5w/fx4nT55Ez5498cwzz+D06dP4/PPPMWfOHPz000948803FYuvpKQEQ4YMwfHjx2FlZYXhw4fDzc1N8xldWFiIrKwspKWlYcmSJfjkk09w6NAhWFpaKhKf6PkDxJ17QPWVThMnToRKpcKwYcNgamqKQ4cOYfLkyUhJSVE8Vw+ioKAAJSUlBnlv0fMneoH3Xgw5tg8iNTUViYmJ/EVdB2MsUhoTnnv1MMhy2YzdRZblWts+JyYmkkqlIlmWyc3Njfr376+1Gn1Dt9xuDOnp6aRSqUitVtOIESNo9OjRZG5uTrIs08yZM2v1V3pLStHzx5Sj9BbzkiRRbGysVlvfvn1JkiQKDg6mtLQ0+vXXX2nnzp3k4OBAsizToUOHFIvvfimdP5GtWbOGzM3NqX///nTmzBmdfQy1i8mUKVNIlmX66KOPtNp3795NKpWKtm/fTkRERUVFNH78eJJluUE7LDaW8PBwkiSJFi9eXO/OTCUlJRQZGUmSJFFERIRi8YmeP5HnHhHR448/TqamppSamqppy8vLIx8fH5JlmUJCQqiqqkpzTOlzgm7dut3zIUkS2dnZaZ53795dsfhEzl9xcTH169ePJEkia2trCggIoLCwMFq2bBktW7aMwsLCKCAggKytrUmWZfL29qbi4mJFYiMSf2wfhNK/d3///Xd64YUXyMPDg3r16kWLFy+m69ev6+wbExNDKpVKsdiIqncrXrNmDXXq1ElrZ+U7H3//+98pLi7OIOfyoufvfvA5X924SMSEcPcXzeLiYrKzsyN7e3v6+uuvNe0lJSU0ceJEkmWZ1q1bp1h8Ip/QEImfP6YcQxeJ0tPTSZIkCgkJqdX3zJkzpFarKSgoSLH47hefMGg7ffo0eXl5UYsWLSguLk7rc47IcF/UHR0dady4cTqPBQUFkbu7u+Z5eXk5dezYkYYOHapUeOTs7EwBAQEN7j98+HBydnbWY0TaRM8fkbhzj4jIwcGBxo8fX6u9oqKCJk2aRJIk0TPPPKOJ2RDnBDVboNf10HVcKSLnT/QCr+hjS0S0c+fO+3oMGjRIsfEtKCjQFF/uzE+7du3oyJEjtfor/bMrepFS9PyJPPeMDReJmBDu/qL56aefkiRJtHnz5lp9y8rKqH379uTt7a1YfCKf0BCJnz/24FxcXO7rUXPioJS7596mTZtIlmXKyMjQ2T8wMJAcHR2VCk/4/BmDiooKio6OJlNTU+rXrx/98ssvmmOG+qKuVqvr/GIWERFB5ubmWm3PP/88tWzZUonQiIjIzMyMlixZ0uD+S5YsITMzMz1GpE30/NUQce4RVedv6dKlOo9VVVVRSEgISZJEkyZNosrKSsXPCXr27EnW1tb0xhtv1NmH86eb6AVe0ce25v3rugJG16OmvxJqCnvPP/88Xblyhf744w+Ki4sjS0tLatGiBe3bt0+rv9I/u6IXKUXPn8hzz9jwmkRMSFlZWZAkCaNHj651zNzcHEOHDsXHH3+sWDw3btzQuV2iiYkJ3nnnHZiamiIxMRFVVVXYtWuXYnHVRbT8sQeXm5sLWZZhamraoP4VFRV6jqh+hYWFAIDOnTvrPN65c2fs27dPsXiMLX8iMjExQUxMDMaMGYPg4GB4eXkhKioKkZGRBovJwcEBp06d0nns1KlTsLW11WqzsbFBaWmpEqEBqI7v5MmTDe5/4sQJODg46C+gu4ievxoizj2gOn/Xrl3TeUySJCQkJICIsGvXLlRVVaFTp06Kxvf9998jJiYGc+bMwUcffYT4+Hg4OzsrGkN9RM7flStXMHHixAb37927N44cOaLHiLSJPrYAoFar4ejoiBkzZjSof1JSEn788Uc9R1Vtz5498PLywubNmzVtERERGD58OEaNGoWgoCAkJSXpPH9WQlJSEkaMGIHVq1fX28/CwgJr1qzBjz/+iA8//BBxcXGKxCd6/kSee8aGi0RMSFVVVQBQ50lz27ZtUVZWplg8Ip/Q6CJa/tiDc3R0hL29PTIyMhrUPzQ0VPFC5Z27EDk6OgKo3lFP1/anJSUlsLCwUCw2Y8ifsfDy8sKJEycQFRWFFStWYPfu3Q+0A1VjCAgIwLZt27B582atnbfeeOMNHDhwABMmTNDqn5+fj7Zt2yoWX1BQEF599VUsW7YMy5cvR4sWLXT2Kysrw4svvoiDBw9i3rx5isUnev7uJtLcA4CuXbvi8OHDdR6XJAk7duwAAOzatQvW1tbKBPZfpqameOmllzB27FiEhITAw8MDq1evxgsvvKBoHHUROX+iF3hFH1sA8PDwwIULFxpczM3MzFTsi3pubq7O3Ro9PT2RkpICf39/PPHEE/jwww8xZswYRWK6k+hFStHzJ/LcMza8FzYTRm5uLo4ePYqjR4+ivLwcQPWHpS6//fabortzNfSEZvLkyfjggw/w2muvKRZbDZHzxx5c7969cebMGc2Y3oshvjht2LABrq6ucHV1xdKlSwEAP/30k86+eXl5in7ZNIb8GRO1Wo24uDikpKSgqKhIa/c6Ja1YsQKtWrXCnDlz4OjoiP79+8PR0RFhYWGwsrJCbGyspm9lZSUOHjwIb29vxeKLjY1Fjx49sHr1ajg4OOCxxx7D3LlzsWLFCqxYsQJz587FY489BgcHB6xZswY9evRATEyMYvGJnj9dRJl7ADBy5EhkZ2cjJSWlzj415wXBwcEoKipSMLr/6du3L06ePIlp06Zh3rx58PX1xblz5wwSy51Ezl9QUBD279+PZcuW1fvHtLKyMixduhQHDx7E448/rlh8NUQdW6D69+4ff/yB/Px8Q4dSi6WlZZ2fHa6urjh8+DDatm2LCRMmYM+ePQpHJ36RUvT8iTz3jI4Bb3VjTOPue0hrnu/YsUNn/379+lG/fv0Ui2/Dhg0kSRIdPXq03n533ktvyHtwRcsfe3DR0dEkSRKlp6c3qH/N/FNKx44dydnZudbj7t32iIhKS0vJxsaGnnrqKcXiEz1/xuyvv/6igoICKisrM8j7nzt3jkaNGkWmpqYkSRKZmJjQ4MGDa62HVVZWRqmpqXThwgVF4yspKaEVK1aQk5NTnQvMOjk5UXR0dL1rT+iL6Pmrj6Hn3qVLl2jx4sUN2vGtqqqKoqOjKTQ0VP+B1ePIkSPk6upKFhYWtG7dOoOuWyNy/goLC8nT05MkSSIbGxsaOXIkzZkzh6KioigqKormzJlDI0eO1NottrCwUJHY6iLS2BIRvfPOO+Ts7Ky1cUp9tm3bptj49u7dm/z9/evtc+7cOerQoQOp1WoaMGCAoufz8+fPJ1mWaenSpVRaWlpnv9LSUlqyZAnJskwLFixQLD7R8yfy3DM2EpEB/xTD2H/d+VfLO3l6emLs2LFabVlZWejatStmz56t2BU7ly9fxuuvv45+/fohMDCw3r5EhNjYWOTl5SEhIUGR+ETPH3twOTk5SElJga+vb4PWHbh+/TqKi4vRsWNH/Qd3n3799Ve8//778Pf3h4+PjyLv2ZTyx3QrLy/H9evXYWdnV+dtXYaWlZWFrKws3Lx5EwBga2sLNzc3nWvdKc0Y8scaR2lpKRYuXIi3334bADB16lRs2bLFwFGJp7S0FHFxcYiPj8fly5d19nF0dMTUqVMRERGh6C3UdeGxbZiFCxfi9ddfv+cttDk5OfD390d+fj4kSUJlZaUi8RUVFcHHxwcZGRmwtrbGwIED4ebmplkn7ubNm8jKykJaWhqKiorQs2dPHD16VLFbMkXPH2s8XCRiRqe4uBjXr19Hy5YtFb/Pvyng/DHGmOHt3bsXzs7O6NGjh6FDYc3M4cOHkZGRAXd3dwwZMsTQ4QhN5AKvLjy29UtLS8MTTzyBBQsWIDw8vN6+58+f1xQ6lCxyiFykNIb8scbBRSLGGGOMGbVLly7p/CLn5ORk4MjqJsuyMH/tN8b8McZYU2dsRUrWdPDuZkwoFRUVOH36NExMTODh4VHnIrKnTp3CyZMnERwcrHCEYuP8NR8lJSXYunUr0tLSUFJSAmdnZ0yaNAmDBg0ydGhGgfNn/G7fvo0NGzZg27ZtyMnJ0dnHxcUF06dPx9y5c2FmZqZYbMePH29Qv2vXrmn17du3r75CqkXk/LHGxUXA+2cs51M8tk0bF4SYofCVREwYSUlJmDVrFv78808A1ZdSxsXFYdKkSbX6xsbGYuXKlXz54h04f03T4MGDERoaqnUCmpOTg6FDhyIvL09rlwlJkrB06VK8+OKLhghVSJy/pqmkpARDhgzB8ePHYWVlpVm3wcbGBgBQWFioWbehpKQEffv2xaFDh2BpaalIfLIsP9BOeUp9JoueP/bwuAj44EQ/n+KxbTzGXmSLj49HWloatm/fbpD3N/b8sbrxlURMCMePH8fEiROhUqkwbNgwmJqa4tChQ5g8eTJSUlLw5ptvGjpEoXH+mq7Dhw/Dz89Pq23ixInIzc3F5MmTMWPGDLRq1QrHjh1DZGQkVq1aBT8/P16L4L84f01TbGwsjh8/jsjISERFRdW5JkNpaSlWrlyJtWvXYuXKlYiLi1MsRktLSwQGBkKlUtU6RkRITEyEm5sbBgwYoFhMNYwhf+zB3V0EHD58eJ1FwCVLluCTTz7hIuB/iX4+xWP78JpSkS01NRWJiYmKFomaUv5YPQyzqRpj2h5//HEyNTWl1NRUTVteXh75+PiQLMsUEhJCVVVVmmMxMTGKbqkoOs5f0yVJEsXGxmqep6enkyRJFBISUqvvmTNnSK1WU1BQkIIRio3z1zQ5OztTQEBAg/sPHz6cnJ2d9RiRtjVr1pC5uTn179+fzpw5o7OPIbepFj1/7OGEh4eTJEm0ePFiKikpqbNfSUkJRUZGkiRJFBERoWCE4hL9fIrH9uEUFxdTv379SJIksra2poCAAAoLC6Nly5bRsmXLKCwsjAICAsja2ppkWSZvb28qLi42dNh1Cg0NVXT+NbX8sbrxlURMCGlpaQgMDMTAgQM1bR06dMDXX3+NkJAQJCYmorKyEomJiQ90CX9Tx/lrPr7//ntIkoQFCxbUOta1a1c89thjOHbsmAEiMw6cv6bhypUrmDhxYoP79+7dG0eOHNFjRNoiIyMxatQohISEwMvLCzExMQgPDxfm81f0/LGHk5SUhBEjRmD16tX19rOwsMCaNWvw448/4sMPP+QrxSD++RSP7cMR/SrKxMTE++qfnZ2tp0h0Ez1/rPFwkYgJ4caNGzoXZjMxMcE777wDU1NTJCYmoqqqCrt27TJAhGLj/DUfhYWFAIDOnTvrPN65c2fs27dPyZCMCuevaXBwcMDJkycb3P/EiRNwcHDQX0A6uLu7Iz09Hf/3f/+H5cuX4+OPP0ZCQgK6deumaBy6GEP+2IPjIuCDE/18isf24YheZAsNDb2v4iMRKVqsFD1/rPFwkYgJwcHBAdeuXdN5TJIkJCQkgIiwa9cuVFVVoVOnTgpHKDbOX9N25wmAo6MjAKC4uBjm5ua1+paUlNT5l53mivPX9AQFBeHVV1/FsmXLsHz5crRo0UJnv7KyMrz44os4ePAg5s2bp2yQqP5iGRMTgzFjxiA4OBheXl6IiopCZGSk4rHcyVjyxx4MFwEfnOjnUzy2D0f0IptarYajoyNmzJjRoP5JSUn48ccf9RzV/4ieP9Z4uEjEhNC1a1ccPny4zuOSJGHHjh0AgF27dsHa2lqZwIwE569p27BhAxISEgAA5eXlAICffvqp1oLMAJCXl4e2bdsqGZ7wOH9NT2xsLJKTk7F69Wps2rRJszuXra0tAODmzZuaxVuLiorQs2dPxMTEGCxeLy8vnDhxAlFRUVixYgV2795t0FvPjC1/7P5wEfDBiX4+xWP7cEQvsnl4eODChQsN/kNCZmamokUi0fPHGpFhl0RirNqGDRtIkiQ6evRovf2qqqooJCSEJEnihZfvwPlrujp27EjOzs61HitXrqzVt7S0lGxsbOipp54yQKRi4vw1XSUlJbRixQpycnIiSZJ0PpycnCg6OrreBV6V9u2335Kbm5tBF64mMt78sXsrLCwkT09PkiSJbGxsaOTIkTRnzhyKioqiqKgomjNnDo0cOZJsbGxIkiTy9PSkwsJCQ4ctBNHPp3hsH878+fNJlmVaunQplZaW1tmvtLSUlixZQrIs04IFCxSLb8aMGSTLMl24cKFB/ZVeuFr0/LHGIxERGbpQxdjly5fx+uuvo1+/fggMDKy3LxEhNjYWeXl5mqsDmjvOHwOAX3/9Fe+//z78/f3h4+Nj6HCMDufPeGVlZSErKws3b94EANja2sLNzU3n2iIiqKysRHFxMczMzHTe9qg0Y8sfu7fS0lLExcUhPj4ely9f1tnH0dERU6dORUREBN9m+1/GcD7FY/vgioqK4OPjg4yMDFhbWzfoKsqjR48qdsXYu+++i+XLlyM+Ph6DBw++Z//4+HikpqYqNv9Ezx9rPFwkYowxxliTs3fvXjg7O6NHjx6GDsUocf6aDi4CNl08tvePi2wPh/PXPHCRiDHGGGNNjizLmDp1KrZs2WLoUIwS548x1tRxke3hcP6aLl64mjHGGGNG5fjx4w3qd+3aNa2+ffv21VdIRoXz1zxUVFTg9OnTMDExgYeHR52LpZ86dQonT55EcHCwwhGyB8Vj2zi4oPFwOH9NF19JxBhjjDGjIsvyA+0OVllZqYdojA/nr+lLSkrCrFmz8OeffwKovv0jLi4OkyZNqtU3NjYWK1eu5PE1Ejy2yomPj0daWhq2b99u6FCMEufPePGVRIwxxhgzOpaWlggMDIRKpap1jIiQmJgINzc3DBgwwADRiY/z13QdP34cEydOhEqlwrBhw2BqaopDhw5h8uTJSElJwZtvvmnoENkD4rFVVmpqKhITE7nI8YA4f8aLi0SMMcYYMyqrV69GTEwMsrOzsX37dnTt2rVWn8TERPj6+vKaOjpw/pq2tWvXQpZlfPPNNxg4cCAA4MKFC5g8eTK2bNmCsrIyJCQkPNDVZMyweGwZY0rgIhFjjDHGjEpkZCRGjRqFkJAQeHl5ISYmBuHh4fzFqIE4f01bWloaAgMDNUUEAOjQoQO+/vprhISEIDExEZWVlUhMTOQxNzI8tg8nMTHxvvpnZ2frKRLjxPlrPrhIxBhjjDGj4+7ujvT0dPzf//0fli9fjo8//hgJCQno1q2boUMzCpy/puvGjRs6F5M1MTHBO++8A1NTUyQmJqKqqgq7du0yQITsQfHYPpzQ0ND7Kp4RERfb7sD5az64SMQYY4wxo2RiYoKYmBiMGTMGwcHB8PLyQlRUFCIjIw0dmlHg/DVNDg4OuHbtms5jkiQhISEBRIRdu3ahqqoKnTp1UjhC9qB4bB+OWq2Go6MjZsyY0aD+SUlJ+PHHH/UclfHg/DUfXCRijDHGmFHz8vLCiRMnEBUVhRUrVmD37t3818v7wPlrWrp27YrDhw/XeVySJOzYsQMAsGvXLlhbWysTGHtoPLYPx8PDAxcuXGhwITwzM5OLHHfg/DUfsqEDYIwxxhh7WGq1GnFxcUhJSUFRURGIyNAhGRXOX9MxcuRIZGdnIyUlpc4+NcWE4OBgFBUVKRgdexg8tg+nd+/e+OOPP5Cfn2/oUIwS56/54CuJGGOMMdZk9O/fH2fOnEFxcTHMzMwMHY7R4fwZvwkTJuDq1au4fv16vf1qbk9ydnZGXl6eQtGxh8Fj+3D++c9/Yv/+/cjKykL79u3v2X/QoEEKRGU8OH/Nh0T8pyLGGGOMMcYYY4yxZo9vN2OMMcYYY4wxxhhjXCRijDHGGGOMMcYYY1wkYowxxhhjjDHGGGPgIhFjjDHGGGOMMcYYAxeJGGOMMWZghw8fhiRJiImJaVB/Pz8/SJKkl1hiYmIgSRIOHz6sl9dXirOzM5ydnR/qNXJzcyFJEkJDQxslJsYYY4yJj4tEjDHGGDN6O3bsgCRJ2LFjh6FDYYwxxhgzWiaGDoAxxhhj7H4kJiaitLRUL6/9wgsv4KmnnkKHDh308vqMMcYYYyLjIhFjjDHGjIo+CzitWrVCq1at9Pb6jDHGGGMi49vNGGOMMSaM1NRU+Pn5wdraGo888ggef/xxZGdna/W5e02i0NBQTJkyBQAwZcoUSJKkedS4cuUK5s6dCzc3N7Ro0QKPPPIIunXrhpkzZ+LmzZuafrrWJKp5v7oed6/ZU1RUhOjoaDz66KOa9xoxYgRSU1MfOC/Jycl49tln0aVLF1hZWcHKygr/+Mc/sGXLlga/xp3/b/Hx8fDw8IC5uTmcnJwwf/58FBUV1flvs7OzMW7cONjZ2cHS0hJDhw5FRkaGXuJkjDHGmOHwlUSMMcYYE8KxY8ewevVqBAQEICwsDD///DM++eQTpKSk4NixY3B1ddX57wIDA1FQUIA9e/Zg7Nix8PT01DpeWlqKgQMHIjc3F8OHD8e4ceNw+/ZtnD9/Hrt27cKiRYtga2tbZ1yhoaHw8/Or1f7ll1/i+PHjsLCw0LTduHEDPj4++PnnnzFw4EDMnDkThYWF2LNnD/z9/ZGUlITAwMD7zk1cXByys7Ph7e2NcePGoaCgAF999RVmzJiBX3/9FevWrWvwa61fvx5ff/01nnzySYwaNQqHDh3Cxo0bcezYMRw9ehSmpqZa/XNzc+Ht7Y1HH30Uzz77LM6dO6f5/zlz5gzatm2rlzgZY4wxZgDEGGOMMWZAycnJBIAA0FtvvaV17K233iIANHr0aE2br68v3X0Kk5CQQAAoISGh1uvv3buXANC8efNqHSsqKqJbt25pnkdHRxMASk5Orjfmo0ePklqtJldXV7p27ZqmfdKkSQSAtm7dqtX/6tWr1L59e2rdujWVlZXV+9q65OTk1GqrqKigYcOGkUqlory8PK1jHTt2pI4dO2q11fy/qdVqysjI0LRXVVVp4n7llVc07efPn9eMy5o1a7Rea/ny5QSAVq9e/VBxMsYYY0wsfLsZY4wxxoTQuXNnTJs2Tatt2rRpcHNzwxdffIFr16491Ou3aNGiVpuVlRXMzMzu63Vqbr2ysLDAF198oVnD6I8//sAHH3yAwYMHY+rUqVr/pk2bNggPD8e1a9dw6NCh+47dxcWlVpuJiQlmzpyJyspKJCcnN/i1goOD0aNHD81zSZKwatUqqFQqnbvDubi4IDw8XKvtueeeAwB8//33eouTMcYYY8rj280YY4wxJoSBAwdClrX/fiXLMgYOHIisrCxkZGRg6NCh9/26Pj4+aNeuHdasWYOMjAyMHj0avr6+6Natm9a6RQ3x559/YtSoUbh58ya++uordO3aVXPs+++/R2VlJcrLyxETE1Pr32ZlZQEAMjMzMXr06Pt636KiIrzyyiv49NNPce7cOZSUlGgdv3z5coNf65///Getto4dO6J9+/b4+eefcfv2bajVas0xT0/PWuPyt7/9DQBQUFCgtzgZY4wxpjwuEjHGGGNMCHeubaOr/c4Fpu+Hra0tjh07hhUrVuCzzz7Dvn37AADt27fH4sWL8fzzzzfodSoqKhAUFISzZ89iy5YtGDJkiNbxGzduAADS0tKQlpZW5+vcXTi5l9u3b8PPzw8nTpxAr169MHnyZNjb28PExAS5ubnYuXMnysvLG/x69eU5NzcXRUVFsLe317Tb2NjU6mtiUn0KWVlZqbc4GWOMMaY8LhIxxhhjTAhXr16tt72+xaXvpUOHDtixYweqqqpw6tQpHDhwAK+99hpmz54NOzs7TJw48Z6vMWPGDBw+fBgLFy6sdVsc8L9iysKFC/HKK688cKx327NnD06cOIHnnnsO27Zt0zr2/vvvY+fOnff1evXlWZIkWFtbCxEnY4wxxpTHaxIxxhhjTAhpaWmoqqrSaquqqsK3334LSZLQs2fPOv+tSqUCoH1liy6yLMPT0xMRERF47733AAB79+69Z2yrV69GQkICxo4di7Vr1+rs06dPH0iShO++++6er3c/zp07BwAYO3ZsrWMpKSn3/Xq6/k1eXh7y8/Px6KOPat1qdj8aO07GGGOMKY+LRIwxxhgTwtmzZ7F161attq1bt+Ls2bMYNWoUWrduXee/bdmyJQAgPz+/1rGff/5Z59UzNW3m5ub1xvXRRx9h2bJl8PLywrvvvltrfZ4aDg4OmDBhAr799lu8/PLLIKJafdLT01FaWlrv+92tY8eOAIDU1FSt9iNHjtTKV0MkJibi1KlTmudEhKVLl6KyshKhoaH3/Xr6ipMxxhhjyuPbzRhjjDEmhBEjRmDOnDnYt28fHn30Ufz888/47LPP0KpVK7z66qv1/tv+/fujRYsW2LhxI/78809NQWn58uU4ePAgwsPDMXDgQHTu3Bn29vbIycnB3r17YW5ujtmzZ9f72sHBwSAieHl54eWXX6513NPTE4GBgQCAN954A7/++isiIiKwa9cu9O/fH4888gjy8/Pxww8/ICsrC1euXIGFhUWD8/Kvf/0Lzs7OWLt2LX766Se4u7vj119/xeeff45x48bho48+avBrAdV57t+/P5566im0bt0aX3/9NX744Qd4e3sjLCzsvl5Ln3EyxhhjTHlcJGKMMcaYELy9vbF8+XIsX74cr732GlQqFQIDA7F27Vq4urrW+29btmyJjz76CDExMdi6dSvKysoAVBeJRowYgdzcXBw9ehQff/wxiouL4eTkhCeffBIRERHo3r17va9d81p3r7NTIyQkRFMkatmyJb799lts2rQJH3zwAd59911UVVXBwcEBPXv2RFRUFFq1anVfebGyssI333yD8PBwHD16FIcPH8ajjz6Kd999F23btr3v4suCBQswZswYbNy4EdnZ2WjZsiXmzp2LF1988YFvNdNHnIwxxhhTnkS6roVmjDHGGGNNSkxMDGJjY5GcnAw/Pz9Dh8MYY4wxAfGaRIwxxhhjjDHGGGOMi0SMMcYYY4wxxhhjjNckYowxxhhT3KeffoqTJ0/es5+fnx/fGsYYY4wxxfCaRIwxxhhjCgsNDcXOnTvv2S86OhoxMTH6D4gxxhhjDFwkYowxxhhjjDHGGGPgNYkYY4wxxhhjjDHGGLhIxBhjjDHGGGOMMcbARSLGGGOMMcYYY4wxBi4SMcYYY4wxxhhjjDFwkYgxxhhjjDHGGGOMgYtEjDHGGGOMMcYYYwxcJGKMMcYYY4wxxhhj4CIRY4wxxhhjjDHGGAPw/8phAw7SbqRCAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1400x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Best Fingerprint Method / Performance\n",
    "from collections import defaultdict\n",
    "res_dict = defaultdict(list)\n",
    "for i, row in df_training_stats.iterrows():\n",
    "    fp_name = row['param_fp_transformer'] \n",
    "    if(\"Morgan\" in str(fp_name)):\n",
    "        res_dict[fp_name].append(row)\n",
    "\n",
    "for fp_type, rows in res_dict.items():\n",
    "    df = pd.DataFrame(rows)\n",
    "    df =df.sort_values(by=\"mean_test_score\")\n",
    "\n",
    "    #plot test score vs. approach\n",
    "    xlabels = map(lambda x: \"_\".join(x), zip(df.param_fp_transformer__nBits.astype(str), df.param_regressor__alpha.astype(str)))\n",
    "\n",
    "        \n",
    "    plt.figure(figsize=[14,5])\n",
    "    plt.bar(range(len(df)), df.mean_test_score, yerr=df.std_test_score)\n",
    "    plt.xticks(range(len(df)), xlabels, rotation=90, fontsize=14)\n",
    "    plt.ylabel(\"mean score\", fontsize=14)\n",
    "    plt.xlabel(\"bitsize_alpha\", fontsize=14)\n",
    "\n",
    "    plt.title(\"Fingerprint Transformer \"+str(fp_type).split(\"(\")[0]+\" per Bitsize\", fontsize=18)\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-12T12:11:09.030301Z",
     "iopub.status.busy": "2024-04-12T12:11:09.030024Z",
     "iopub.status.idle": "2024-04-12T12:11:09.425867Z",
     "shell.execute_reply": "2024-04-12T12:11:09.425174Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot ALL test score vs. approach\n",
    "df =df_training_stats.sort_values(by=\"mean_test_score\")\n",
    "\n",
    "plt.figure(figsize=[16,9])\n",
    "plt.bar(range(len(df)), df.mean_test_score, yerr=df.std_test_score)\n",
    "plt.ylabel(\"mean score\", fontsize=14)\n",
    "plt.xticks(range(len(df))[::5], df.param_fp_transformer[::5], rotation=90, fontsize=14)\n",
    "plt.title(\"test score vs. approach\", fontsize=18)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
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   ]
  }
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