solubility/Solubility_predict.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prediction of solubility"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we try to predict solubility of organic molecules just on basis of the chemical structure. Data was retrived from http://moleculenet.ai. Orginally the data was publiched by Delaney, John S. \"ESOL: estimating aqueous solubility directly from molecular structure.\" Journal of chemical information and computer sciences 44.3 (2004): 1000-1005."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from rdkit import Chem, DataStructs\n",
    "from rdkit.Chem import AllChem\n",
    "from rdkit.Chem.Draw import IPythonConsole\n",
    "from IPython.core.display import display\n",
    "from sklearn.metrics import r2_score\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "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>Compound ID</th>\n",
       "      <th>ESOL predicted log solubility in mols per litre</th>\n",
       "      <th>Minimum Degree</th>\n",
       "      <th>Molecular Weight</th>\n",
       "      <th>Number of H-Bond Donors</th>\n",
       "      <th>Number of Rings</th>\n",
       "      <th>Number of Rotatable Bonds</th>\n",
       "      <th>Polar Surface Area</th>\n",
       "      <th>measured log solubility in mols per litre</th>\n",
       "      <th>smiles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amigdalin</td>\n",
       "      <td>-0.974</td>\n",
       "      <td>1</td>\n",
       "      <td>457.432</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>202.32</td>\n",
       "      <td>-0.77</td>\n",
       "      <td>OCC3OC(OCC2OC(OC(C#N)c1ccccc1)C(O)C(O)C2O)C(O)...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fenfuram</td>\n",
       "      <td>-2.885</td>\n",
       "      <td>1</td>\n",
       "      <td>201.225</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>42.24</td>\n",
       "      <td>-3.30</td>\n",
       "      <td>Cc1occc1C(=O)Nc2ccccc2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>citral</td>\n",
       "      <td>-2.579</td>\n",
       "      <td>1</td>\n",
       "      <td>152.237</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>17.07</td>\n",
       "      <td>-2.06</td>\n",
       "      <td>CC(C)=CCCC(C)=CC(=O)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Picene</td>\n",
       "      <td>-6.618</td>\n",
       "      <td>2</td>\n",
       "      <td>278.354</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-7.87</td>\n",
       "      <td>c1ccc2c(c1)ccc3c2ccc4c5ccccc5ccc43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Thiophene</td>\n",
       "      <td>-2.232</td>\n",
       "      <td>2</td>\n",
       "      <td>84.143</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.33</td>\n",
       "      <td>c1ccsc1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Compound ID  ESOL predicted log solubility in mols per litre  \\\n",
       "0   Amigdalin                                           -0.974   \n",
       "1    Fenfuram                                           -2.885   \n",
       "2      citral                                           -2.579   \n",
       "3      Picene                                           -6.618   \n",
       "4   Thiophene                                           -2.232   \n",
       "\n",
       "   Minimum Degree  Molecular Weight  Number of H-Bond Donors  Number of Rings  \\\n",
       "0               1           457.432                        7                3   \n",
       "1               1           201.225                        1                2   \n",
       "2               1           152.237                        0                0   \n",
       "3               2           278.354                        0                5   \n",
       "4               2            84.143                        0                1   \n",
       "\n",
       "   Number of Rotatable Bonds  Polar Surface Area  \\\n",
       "0                          7              202.32   \n",
       "1                          2               42.24   \n",
       "2                          4               17.07   \n",
       "3                          0                0.00   \n",
       "4                          0                0.00   \n",
       "\n",
       "   measured log solubility in mols per litre  \\\n",
       "0                                      -0.77   \n",
       "1                                      -3.30   \n",
       "2                                      -2.06   \n",
       "3                                      -7.87   \n",
       "4                                      -1.33   \n",
       "\n",
       "                                              smiles  \n",
       "0  OCC3OC(OCC2OC(OC(C#N)c1ccccc1)C(O)C(O)C2O)C(O)...  \n",
       "1                             Cc1occc1C(=O)Nc2ccccc2  \n",
       "2                               CC(C)=CCCC(C)=CC(=O)  \n",
       "3                 c1ccc2c(c1)ccc3c2ccc4c5ccccc5ccc43  \n",
       "4                                            c1ccsc1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('delaney-processed.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1128, 10)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see some example of structures presented in the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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6Q39/f70HmTFjBp9penuPFkAJJ2Tizhi7c+dO/fr1icjGxmbXrl1paWl9+vQhIolE4u3t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SUlMDBQc5VIRC4uLoGBgRkZGc+fP/f399es0TIzM/vkk0/CwsKEel0tW7YUfOEpFEsZGRnGDgH0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\nsGEXL14s6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A4AAAAYAMEdAAAAMACCOwAAAGAABHcAAADAAAjuAAAAgAEQ3AEAAAADILgD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\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=1000x800 at 0x7F88FBE52C10>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mols = [Chem.MolFromSmiles(df['smiles'].iloc[i]) for i in range(df.shape[0])]\n",
    "Chem.Draw.MolsToGridImage(mols[:20], molsPerRow=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluation of ELOS model. Since prediction of the model is the regression, the best choice is r2_score for evaluating the results. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8113621507764678"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(df['measured log solubility in mols per litre'], df['ESOL predicted log solubility in mols per litre'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pretty good! Let's now try improve this score."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create firstly descriton of the molecules. Pretty often used fingerpret is EPCF, which recently was used for nice application in machine learning:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X = []\n",
    "for mol in mols:\n",
    "    fp = AllChem.GetMorganFingerprintAsBitVect(mol,2)\n",
    "    arr = np.zeros((1,))\n",
    "    DataStructs.ConvertToNumpyArray(fp, arr)\n",
    "    X.append(arr)\n",
    "X = np.array(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y = df['measured log solubility in mols per litre']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we have data ready we can use some ML algorithm. I like in particular Random Forest becasue it is simple."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "clf = RandomForestRegressor(n_estimators=100, max_features='sqrt').fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the score:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6893287230762319"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This seems like good result. Not as good as the ELOS, but it is good start!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Murcko train-test split\n",
    "However, above alogrithm could be critized becasue te data is limited (only over thousend of measurment) and might just identify similar molecules and give them average results. One of the strategies to prevent that is to do train-test split of different categories of the data. We could for example categorize molecules by their scaffold and create test set that scaffolds are not included in training test. (the idea is part of module deepchem.io, but I decided to implement it in python/sklearn to make it more transparent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from rdkit.Chem.Scaffolds import MurckoScaffold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a list of scaffold existing in the data set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "269\n"
     ]
    }
   ],
   "source": [
    "\n",
    "scaffolds= {}\n",
    "y = np.array(df['measured log solubility in mols per litre'])\n",
    "\n",
    "scaffolds[MurckoScaffold.MakeScaffoldGeneric(MurckoScaffold.GetScaffoldForMol(mols[0]))]=[(mols[0],y[0])]\n",
    "\n",
    "for n_mol in range(1,len(mols)):\n",
    "    # generic scaffold\n",
    "    #core = MurckoScaffold.MakeScaffoldGeneric(MurckoScaffold.GetScaffoldForMol(mol))\n",
    "    mol = mols[n_mol]\n",
    "    core = MurckoScaffold.GetScaffoldForMol(mol)\n",
    "    #invariant for atoms\n",
    "    fp_core = AllChem.GetMorganFingerprint(core,2)#,invariants=[1]*core.GetNumAtoms())\n",
    "    \n",
    "    already_scaffold = False\n",
    "    \n",
    "    # let's search if the scaffold already exist in the dictionary:\n",
    "    for scaffold in scaffolds:\n",
    "        fp_scaffold =AllChem.GetMorganFingerprint(scaffold,2)#,invariants=[1]*scaffold.GetNumAtoms())\n",
    "        if fp_core == fp_scaffold:\n",
    "            already_scaffold=True\n",
    "            scaffolds[scaffold].append((mol, y[n_mol]))\n",
    "    # we have not found the scaffold? Let's add new to the dictionary:\n",
    "    if already_scaffold==False:\n",
    "        scaffolds[core]=[(mol,y[n_mol])]\n",
    "print(len(scaffolds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In result our data can be categories on 269 different categories. Let's show some of the categories (only one which has more than 10 molecules, and show maximum 9 molecules (+1 scaffold)). First structure shows scaffold and it is followed by exact molecules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ycpo9e3ZERIRYLOY7KNA0cXFxTk5OiYmJb968ycrK2r59u7u7u729/fz582Nj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      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=2000x200 at 0x7F88F80385D0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WESoM7RXXS8ARRFlFdVLvxKTpeWnzThSeGN1ktF9HPybtkpEqpN1iu0VXRu822f1Li1/Y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DWL3NzcnJycTE1NawxAgHS7KhzmKxMTE8PhcHR0dIjUe6JZMVXBphp7oqalpUE2jZeXFyVW6Cap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\n2rZta78/8F01u1xzX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      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=2000x200 at 0x7F88F804DFD0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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bGRlJJJKC8QF07tw5c3NzAOjfv3+q1nOk4ODg8uXLA4CTk1NM3lvP5RwF3Ln3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o6TT1XCn+z8RtvZwcyZUKVK+p9KlcDQMONLvv0uKyurGTNmzJgxY/Dgwbdv32YfvvXr11+wYMH4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\n55//97//V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      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=2000x200 at 0x7F89350391D0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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SJDuT6bPJk610rOHDh+d/Yi/GUlfDM/ecrIxdEYvj9kdlZaQunzUuKmzeb19N\nOfxHeDGGQlH17dtX/t5V3nLbt2/v0qXLVV8zdW8ZQwpOUTqQX8EaOK8MROD+l127dukEwfBlDtOm\nTRORJk2a8O5YyuS3f9E1wgZas2aNgW1qUDroPSobNWqklPrxxx8tX3KulJowYYLO3NeuXauU+vPP\nPytVqqR7ktjZ2elupJGRkUXaT1Up9emnymRSJpOaPt3CCaI4UlPV3XcrEWVgt15PT08R0Rvg6JZE\nfn5+lgwYGxurNw/QO2EGBATolT0pKSnFGO2nn34Skceu2fwxJyfn/Pnzx1JSdqWkrL906ZeLFxfG\nx886fXrqiRPjjh4NjY9fnpCwq1hHLDWys7OLsey9SHT1roeHR05Ozs6dO/U1vKK+qmhXrlzRp2K7\ndu2uXLmSnZ3dq1cvfeb81650p06dWrp06bhx47p27erm5lZwSb6bm1u3bt1atWolIlWrVr1z2jic\nOKF27bp612vY0Pfff68jV6XUq6++KiJBQUHGHkJ3YbpDlgH169dPREJDQ5VS7du3FxHdQM8a9BXE\n7du35+TkVKhQoVatWkXdFM7yzD3tcsL+yOVhn7768bPtVn8ZuCti8ZIgv0WTnzy4bXVU2LxzsXuL\n9cxQZAUD97i4uGPHjhXvnS6f2WwODQ0tuKMDgTsKIr+CNXBeGYvA/S8PPPCAiLzyyiuGj5yVlaXX\nywcHBxs+OGzo5ZdfFpEePXpYY/DHHntMRJ555hlrDI7b0Ycffigiw4YNU0r5+/uLyFtvvWX5sEFB\nQf37989/2+vQoYN+Jbxw4YIlw4aEKDs7JaLujG/3JU5kpLKzUw4O6o8/DBgtLCxMRKpXr56enn7y\n5EknJyd7e/vDhw9bOOwnn3wiIpUrV46Pj8/MzNQre/z9/Qvz2LS0tJMnT+7atSsiImLx4sV6uUbN\nmjWHDh3ap0+fDh06NGzYUJdRiMiAP/7wjI6+9s/Yo0fzK9wzLftWfJtKTU3t3bv36NGjrXqUd955\nR0SeffZZpdSbb74pf29EUTwXL17Umzj16NEjMzMzLS2tU6dOItKyZctLly4ppVJTUyMjI4ODg319\nffVOrQXVqFHDx8cnODg4MjJSrxDKzs7u3bu3riIsxp4Et5eYGOXrqxwd1X33/WvX6+PH1fjxKinJ\n4MOlpGyMjR0QFzcyJaWYi1fuELNmzcrvueTr6ysi8+fPN/YQ+iOrIVuflXA5OTm6BfapU6cuXbqk\nm8ZaqW3U8ePHRaRixYq5ublbtmwRkXvvvbcY4xQjc09NTT0Svfa3r6Z8PrrnlH53TfGuof8smDhA\nV7grpb55yzcqbF7i2eOWPEcUXsHAXf+DJiQkGHsIAncURH4Fa+C8MhaBu1J/l5ZUqVJFf1sz3PLl\ny/UHsosXL1pjfNx6Bw4c0H30du/ebY3xY2Nj9b6CUVFRlo8WFRUVdo3VqxPDwtRVf6zzG1Ca7du3\n78MPP9y9e/fx48eteiD9Of7rr79WSumSzPXr1xsycn4BTkZGhouLi52dXX7XV0ssWKDs7ZWIeued\n08UeJCUl5aeffho2bFix2zTfscaMUSKqSRNlea8UvahZJzV6t169VN9CZrNZlyc/+uijSqldu3Y5\nOTmZTKa5c+du2bIlLCxswYIFH3300aRJk0aMGPHEE0/06NGjZcuWNWvWLNjvKJ+rq+u1N9rb21et\nWnX0li3PHTw49ujRt44fDz51av65c8suXlx76dLBtLQ7vMJ906ZNTk5OYp1ddvPpmvTly5ervzef\nsHCn02PHjtWoUUNEBg8enJeXd+HCBf3xvX79+q1bt9Z5RL5KlSr17t17ypQpv/76a9J/JMppaWn6\nJG/RosV/3ed2t2aNeuQRZTIpEeXoqHx9VUjIP7teDxumRFTt2mrdOiMPevr06xkZher2c4fTmxxM\nnTpVKfXwww+LFRZcDx48WES++eYbY4ctgSIjI0WkWbNmSqkffvhBjFgO+F9CQkJExMfHRyk1depU\nESn29cvCZO65ubnR0dFBQUFeXl7Ozs4fD+uqQ/Z3n2iw8I2Bm5Z+evZIjNls1j3cwz59df13HxG4\n30oE7riVyK9gDZxXhiNwV+np6fXq1RORkJAQ6x1FxwqWVHWhRLkF/6Cvv/66iHTs2NHy9f76+9tV\nWrbcIKKu+mNEvF/66Q1FAwICdKGlrmmqXr36nj17rHTE3NxcvXVJXFxcUlKSnZ2ds7OzhZsyXWvd\nunUi0rp1a6MG/P57dc89B6tUqTZy5MgirauNjY0NCQnx9vZ2dnbW/4UnW60Ba2mVkaGaNVMi6tVX\nLRpHd7jy8PBISUk5d+6cq6uryWTau9eYJepxcXEeHh4TJ07Mzc1VSk2ePFm31b4pNze32rVrt2rV\nqmfPngMHDtS9vMuWLbtw4cKVK1du3br18OHDhUlO7/DAXSm1ePFiOzs7k8k0b948a4x/4sQJk8lU\npkyZjIyMw4cPi0iFChWysrIsHHb37t3ly5evVavWqVOnlFKnTp3SfxURBweHpk2b+vr6hoSE7Nu3\nr5CvPAkJCbrR/AMPPFCadizPzc39/ntzu3Z/vcWXLavGjlUnT159t+PHVbduSkSZTMrfXxV3c5B8\n5suXl1+8ODc7+8zJk/7Hjz+dnr7PbLb0H70Ue/755/O/hui1Pjt37jT2EN27dxeRNWvWGDtsCRQY\nGCh/7+k9YsQIEXnvvfesdCwfH5/8fzi9McnPP/9c7NH+K3O/6667pk6d+vjjj1esWLHg5eRPJo9c\nu+iDE3u35OYQv5YIBO64ZcivYA2cV9ZA4K7eeustnTHpL/xWYu2CaNxKt+bSXEpKyl133SUiixYt\nsnCoN998s+81nn9+X9++6qo/FneJKM2OHj06a9asXr16XbWh6NChQ9u0aaOvBu/atcsah96xY4eI\nNGjQQCn1yy+/iMj9999v+FGmTJmS/zXVKGFhK3VoPmzYsBsnX+np6b/++uvo0aN1R9T8r5RdunR5\n9913//zzTwNndYfYtk05OKiKFZUlKxb0F/533nlHKTVu3DgRGTBggGFTVKpgu1vdZKZcuXKdOnXq\n27fvU089NXbs2HfeeWf27NlLlixZu3bt7t27T506de2lpuzsbJPJ5OjoaO125KWS3v3b0dGxYKNY\no8yYMUNEBg0apP7uIm3Uto2bNm3K37Y3Pj5et5tcvXp1sa9Enjp1qk6dOiLy6KOP5uTkGDJJG8rM\nzAwNDb333ns9PS+KqKpVVWDgjV4KcnJUUJBydFQiqnlzFRNTnIOazVkJCaH79zeLjpZduyrm5l5R\nSmVnxx8/PnTPnroXL1rx+9ttzdvbW0R++eUXpVS1atVExPDuRvp60r59+4wdtgTq3Llzfuh59913\ni8gfhvRWu0ZeXp7eySk2NjY1NdXZ2dnBwaGoDdyvsn37dl1doVtKpqamdunSJb/yQH8O9PPzW7Jk\niSErEWEsAnfcMuRXsAbOK2u40wP306dPu7u7i3HNGW7Aqi2/ccvcyuZT8+fPF5GaNWtaqfvk9Y6o\n9L6D7KKm5eTkREZGBgQE6K4I2rUbimZmZvbv318Xb27ZssXwaejQSjdBfuWVV0TkzTffNPwoPXr0\nEJGffvrJ2GHXrVunt8ccPHjwtTHW8ePHQ0JCfHx8ypYtm/9f2MPDw8fHJyQk5L82QkQhhYaqvzPJ\nIsjJyYmPj9+/f/8XX3yhE3C9rjAgIMDV1dVK7X2ys7N1VUXxtqHWTXuTk5MNn9idQHe0cHV13bRp\nk7Ejd+vWTUQWL16slLrvvvssrAD9L/pE7devn4Xj7N+/X6ckFu4JbFsJCQlTp06tUqWKfjnt3fvt\nkJDCtpbauvWvLZddXdUXX6QU/gpWbm5yfPyHMTF3RUdLdLTs2VPn/PmPExLmxsWNio31OXHiGX17\nbOyAnBzDgsK0tLRPPvlk+/btmzdvNmpMm2jXrp2IREVF5ebm2tvb29nZGR6o6eJow+O/kiYpKWno\n0KE1a9YcO3as/uCkG6xb41jbt28XkbvvvlsptWLFChHp3Lmz5cPu3Lmzdu3a+V9Lv/32W13k/uWX\nX544ccLy8WE9BO64ufnzVWSkUkoFBqpTp1RKinrhBTVqlBo2TBW6XKDE5ldXxQhXPVeUcCX2vLrd\n/avT5R0oICAgLS3Nx8dHbw5gVYGBgd988826det++umnxx9/3NqHg5XMmjXr8OHDjRs31r2Mrerp\np5/+/PPP//jjj169ehXjFHV3d09LS7vuj+zspprN12nd4OEhTk4SE1PkqZYyFy5cWL169YoVK37/\n/ffk5GR9Y5kyZbp3796vXz9vb2+9+CCfs7PzkiVLhgwZ8sMPPzz00EPLly/XdcFG2bBhg4joc6Dg\n/zdQdnZ2VFSUyWTSvYwN1L17919//bVv376LFy9OS0tbunSpg4PD7t27w8LCVqxYoRfO63s2bdq0\nX79+Xl5e3bt3v6oRM4rnqadkwQL55RdZtkzeeENefFFycyUxURISJDHxnz9JSZKR8eSpU8cSExMT\nEhLyz3kRqVKlSqdOnXTNXVBQ0Ouvv67/v+EWLVp04sSJJk2a6C2ji8rDw+PKlSuJiYk6eUeRvP32\n2+fPn//qq6/69++/cePGpk2bGjLs+fPnt2zZ4uzs3Lt379OnT0dHR7u5uT300EOGDF7QsmXLROT/\n/u//LBynadOmq1atevDBB+fMmVO9enVd6XMbiYuLmzFjxty5c/Vbf9u2bV9++eUhQ4bY2xd2hI4d\nZc8emTBBPvlEzZ375E8/Zc+fP/+q97urJCXFZ2R8fPFiSF5esoi4urasVm18pUqDTSZHEfHweE7f\nrVy5PnFxwy9d+iE1dVPduvPKl+9tyTNNSEiYO3fuzJkzz507V7du3dOnT48fP/7tt992dHS0ZFhb\nOX/+vIhUq1YtISEhLy+vSpUqxj6RrKysy5cvOzo6FuxJUmrk5ubGxMRERERERERs2LAhJydHRObM\nmZOWltaiRYtevXrZF/4XoCjCw8NFxMvLS0QiIiLy/7+F2rRpc/ToUb27hohs3LhRREaPHv3CCy9Y\nPjiAkuWLL+TZZ6VzZwkPl3nz5NIlycy86i5vXPOgFStW3Pr8asCAAY0bN86/3dHRLSdn4lV3vusu\nYoQS4Z133sm85kS6qZJwXhVS27Ztb6c01daJv43NmjWrTJky6/7eJSorK2vbtm0Gjp+Xl1ewXmzg\nwIG1a9e20tpG3Bq///57tWrVpkyZov9qNpsj9dVb42zatCm/+cYXX3xR7PxIV+Jfl719zrUN3EXU\nm2+qiAj14ot3boX75MmT9Zak+Vq1ajVhwoTIyMibFknl5OQMGTJERNzc3MIt2xWwILPZ7OHhISLH\njx9PTk7WnRMMX/Sg9xlr3ry5scPmW758uQ5q69atW/CULl++vI+Pz7x58+Lj46106Dvc/Plq4EC1\ne7eaNEkdOnSd33r9p169RgVeH+yrVq3apEkTvcVluXLl4opRJ18Uubm5+vVKbwtcDLpElLfXYsvN\nzX3iiSdEpGbNmkbVURYsPA8ODpa/dxc01qVLl5ycnOzt7Y1q8rZixQp9we8WrGMz0JEjR/S0TSZT\n3759LSxQCgs7ot93qlatqje8vdbRo0f9/f1dXV137Lg3OloOHuxy+fJypf6zKD4r68TBg/dHR0t0\ntOnkSX+zuTit4mNjY0eNGuXm5qZfrDp16uTj46P3fujQoUP+9pK3EbPZrHuGZGRk7N69W0RatGhh\n7CFOnjypf7WNHdaGzGZzTEzM9OnTe/XqlX8yiIizs3PPnj3fe++9OXPm6KV1zz33XJG2kCk8XVfx\nww8/qL+3g964caPhR7FqVxwYiwp33Nz8+ap/fzV8uPL0VKdOqRdeUPoL3cWL6qWXVNmy135Av5aL\ni8stzq/y18zlq1Ch4rVfJaZN+1eMcNVzxS1TcNV44ZWE86qQjOpOeWvc6SWEO3fuTE1NnTlzZvfu\n3S9fvtyhQ4ezZ88eOnToxuU8hbdo0aJnnnlmxIgRn3/+eVJS0po1axITE+Pj4w0ZHDZx4MCB8+fP\nL1q06PXXX3d2du7Tp8/q1avXr19v1MXAXbt23X///R07dty4caPJZPrqq6+uXLnSs2dP3e6jSNzc\n3NLT06/7I5PJXl3vPbxMGXF3l9q1JTq6qEcrJTZt2hQTE+Pm5ta5c2dvb+/HH3+8du3ahXysg4PD\nokWLKlasuCw0tOOrr0pgoFhccSkie/fuTUxMrF27dr169VatWpWXl9ehQwe95stAViqcz/fpp59m\nZGTUqFGjevXqcXFx9evX79evX79+/e6///78Si5YiZ+fzJkjFStK2bJSr55UqCAeHlK5snh4/POn\natUF5cvbeXh4VKlSpXz58vmP9fHx+eGHH5599tmIiAiTyWSlGS5duvTw4cMNGjQYNGhQ8UbQ4WBi\nYqKh87qD2Nvbf/311xcvXty4cWOfPn0iIyN1XmCJgoXnRhWhXyssLCw7O/vBBx/U/ZQt17dv3/nz\n5z/11FOvvPJKjRo1Bg4caMiw1tawYcP777+/Zs2ar732WvPmzS0czdu74f79+5977rlVq1Y9+uij\nvr6+n3/+ef77zubNm6dNmxYWFmY2m+3t7fft6/XEE4vc3e+78ZhOTnUbNVobH//+uXNTL1yYlZy8\nycFhUePGhV1OERMT89FHH3333Xe5ubkmk8nb2/vll1/WNcVbt24dOnRoVFRUy5Yt33//fb1U2RAH\nDhzYsGFDnTp1unXrZqXVM5cuXcrKyqpQoYKLi0t+qbuxh9DfO6pXr27ssLfe+fPnN27cGBERsWrV\nqtOnT+ff3qBBAy8vLy8vr0ceeST/n6lx48Z9+vSZN2+eiHz55ZeF3JG7kDIyMrZs2WJvb9+jR4/4\n+PgDBw6ULVu2Y8eOBh5CROLi4mJjYytWrNi2bVtjRwZgM+PHS9euMmWKiEijRrJnj3TqJDEx0qiR\nvPmmZGVddfe3rxngl19+iY6OvsX51ZAhQ/5d4e6ak3P1A93dr44RCj5X3DJvvvlm1jUn0k2VhPOq\nkK4qjizpbJ3421h8fLz+ZLZ69WqllF6b8NRTTxky+JUrV2rUqCF/l+yNHDlSRB588EFDBoet5OTk\n6K+yH374oVJq6tSpItKqVSujekTef//9IjJ+/HilVEhIiIjUrl07LS3NkMFvav58tXWrSk5WlSrd\noRXu69atCw8Pz8rKKvYIZrP54ptvKhHl6KiWLLF8SrNmzRIRX19fpVRqaurq1at/++03y4e9iu7z\nsMSICV8rJydHX2yPi4vTV5IqVKhg1f1YkE//Ur//vnriieL8Ul+8eFGnP7NmzbLC7JRSymw2t2zZ\nUkTmzJlT7EH+97//iQUF8tCSk5Nbt24tIu3bt7dwGU3BwvOsrKwWLVo4OTlZuKPgdekQ/9NPPzV2\n2KCgIBFxcnKyxuutlRheyWs2m0NCQnQFcePGjbdv3758+fL8vhnOzs6+vr4HDx4s6rCpqX/s29do\n7NjuLi4uQUFBN512ZGSkt7e3vuDn5OTk6+t77eafycnJvr6+emL/93//Z3lhaWRkpI+Pj+6o7ujo\nWLdu3Q0bNlg45nXt379fRO6991590AceeCAgIMDYQ4SFhYlInz59jB321khNTQ0PD9db6RS86Fu9\nenW918vp06f/67EbN260Up376tWrReS+++5TSi1cuFBEvL29DRxfmzNnjhi9UTmshwp33NxVfc2v\nXFHPP6/8/dULL6hCf9kvsfnVVTHCVc/1l1/UhAnq9vlIdccpsefV7e5OD9yVUu+//76ING3aNDs7\n+9ixYy4uLiaTyZAFFAEBASLSqVMns9m8b98+BwcHBweHvXv3Wj4ybEv3aixbtuzZs2czMjL0Rn8h\nISGWj7x48WIRqVq16uXLl5OTk3U50vfff2/hsAEBAT2vMXTonp491VV/iv7FGf9h8mQlouzt1bx5\nxR7jxIkTs2fPrlWrlogEBQUZOLur6EDcZDJZqa/L1q1bdV6jlFq6dKmIeHl5WeNAuNaHH1p6Fe2X\nX34RETc3t0OHDhk9O6WU+vnnn0WkVq1allzoCl67duivvy7lJcxiZ86c0W9qffv2vXaX40I6ceLE\nqFGj5N8bIt0gFyu2tLQ0d3d3k8l0ygrLlfUO1WXLlrXSRsG3i127dumOGfmrkTw8PCZPnnzhwoVi\nj5mXlzpixHA9Wq9eva67P3ZeXt7y5cvbt2+v71a2bFl/f/+TJ0/eYNglS5boNuXVqlVbuXJlsSaW\n99NPP3Xq1Ekf1M3NbejQobqWyt7e/o033jA86lq7dq2IPPDAA8YOW9CXX36pQ2frHcJ69Ndyzd3d\n3cvLKygoKDo6upA7+q5du1ZfMXr99W8NjNzHjx8vIhMnTlRKPfXUU2KdDlQ+Pj4i8sUXXxg+MqyB\nwB23zG2aX8XHq88/N2QkWMVtel6VcATuKisr65577hGRTz75RCk1YcIEEfH09LSwFCI2Nlafo1FR\nUerv6tGXX37ZkDnD5vr16ycizz//vFJqyZIlIlKlSpVLly5ZMmZ6erqOOb788kv191f9Ll26FPJL\nxQ08/PDDco2WLTdc23wtKsrCQ6GAoCAlokwmVZTS4Nzc3Ojo6MDAwILFXC4uLrVq1Tp8+LCVZlow\nELcGXSs6fPhwpdTo0aNF5O2337bSsVDQb78pOzs1erSl4+jS0U6dOlljXYLOtmbOnGnJICFnz3pG\nR39x5oxRs7qTHTlyRC9rGDJkSCHfgFJTUyMjI4ODg319ffUbmYjUrl27RYsWllxHuakff/xRRDp2\n7GiNwc1m8zPPPKPf3610tem2oBPhunXrvvbaa1WrVm3Tps23335ryMjLli3TjYAqV678888/59+e\nmZkZGhqavw9NtWrVAgMDk5KSCjNmXFyc7o1mMpn8/PwKv0AwKysrNDS0SZMm+qCVK1cOCAg4e/as\nUionJycwMFDvvXnfffcZeDJkZ2e/+OKL+sOeUWNe65133hGRCRMmWO8Q1vPLL79069btrbfe2rx5\nc/EuAa5du7Zr17ddXNRzzymjMne9Emjt2rVKKV0VsX//fmOG/lteXp7+7YiNjTV2ZFgJgTtumdsx\nv8rJUZMnq8zi7N6CW+R2PK9KPgJ3pZTSrUUrVqx48eLFlJQU3aho4cKFloz52GOPicgzzzyjlPrh\nhx9EpFKlSoa/78JWjh496uzsbGdnpzcy0t/uXnnlFUvGnDJlioi0bt06Nzf3yJEjevzt27dbPttd\nu3ZFXGP9+ksREeqqP8nJlh8NBXz6qTKZlMmkPvroJvc8d07Nm/fSc88VbJ9dvnz5AQMGzJ49u2vX\nrjp02LNnjzWmWTAQt4bevXuLiM5odPMQK63Nx1W6dVMi6oMPLB3n8uXLderUEZH333/fiHn947ff\nfhORqlWrWtg46/vz5z2joz+w8uaud47t27frVgyvvfbade+Ql5e3b9++uXPnDhs2rEWLFjqLzFe5\ncuWePXvqfZJ9fX0tv2z8X4YOHSoiH1h+iv+H7OzsRx55RETuvvtuw7eqvl18++23IjJo0CCl1Jgx\nY0Rk2rRpRg0eHx+vUyp9qpw4cSIwMDC/Hf/dd98dHBycnp5epDHz8vKCg4N1PX7Tpk137dp14/tf\nuXIlODi4Zs2a+qD16tULDg6+9hVp69ategdLV1dXy8uZU1JSgoOD9euqvr7l4+NTyIsKN5aenj57\n9uwnnngi/5aXXnpJbrdNgI21caMqU0a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      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=2000x200 at 0x7F88FEA20A50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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xOsLixYv1PMTX15eIxo8fX9wDYmJi+M6h5PvD4sycOZOIGjduLC179EVX5KXw\np5/+cymMiMhatSq8ECkBF0MuhXrmPV27ds3MzMzExOSff/4xaCz5+fncoXfOnDl6HsIrN40bNy45\n52v+/PkcwjBo4ZmHxMmns2fPNujAZ+Dzzz9/0VPvP/5YEIlly8TGjYJI6JG1oGXQhPavv/4iImdn\n5/T0dIMG+Pnnn/Pdn/4LKlydr2rVqmlpaUU+YP369XwVNvTFpotrPhh0x/Gya9OmDQe2vv76ayIa\nPXq0ngeuWbOGg5tXr17V5/F8y//hhx8aOsK7d+9y9rH+oQze6+Di4lJcpoI21rZy5UpDx8OePHnC\n2V1r1qyRdoYXXHJyMlcPd3BwkDZnkMuuXbsUCoWpqenRo0dLeJharW7SpAkRzZ8/X8KzSIhj8qSo\nbt26xb3naDMhJO985d23H+u9DPbK4PWPhg0bSpxwVqkiiMSVKyU/ShuALi4OyVGCoUOHShmDEAcO\nHDBoBSg2NpZXJaOioop7DPfR9PHxMTRvRgih0Wg4djpjxgxDj5XRqxZwf/r0adWqVUnuuiuzZ88m\nIj8/PyPP8/vvv/O05kppvw/F4dQ/7kRh5GBeW59++ikRLVy48O9du4IbNdqi91aDHTt2mJqa1qlT\nR89UXK6+J+2aMWDAACJq3bq1/j/ofv36lZB6wPuDunfvLmEwuvQsX/NC082t04YAPvtMEBX406JQ\ntdYBAwZs2LDBsKczpG6DEGLWrFlE1KhRoxJ+9KtWrZK8YHvr1i2+thmz9FIYv9p79eol4dhz584p\nlUpzc3NDtx++5ngJzcidAdxImYiaNGmyZMmSr7/+OvRfs2fPnqgjKChIWwNh6NChfDe4ceNGub4d\nXVeuXDExMTG0s3zfvn2l5Va/7vbvF0SiZctSH7h69WoOABVeZXn06BH/vPQv01EYh35cXV31fFVf\nuHCB3zqKW2nmrdkBAQHSxpOVlcU9S3799VdpZ3ihFXkp7N27wHVQVVTXlr59+xocntO9FM6dK/hm\nrPhLoT55T3xnPnLkSMNGIoT497bQ2tpaz3hrVlYW31+UkAv8+PFjXnjWf5O+riNHjigUCmtra8ll\nvsoCN2NQKBTjx483JrBbpsb7+896443YAwduLFuW0Lx52rRpeh64bdu2cuXK6V9vqmPHjmTIOo1W\nZmYmZ6NPmTJFz0NUKhWnVgwfPrzwVzUaDS8aGZ+c3r9/f77jML4K60vBycmJiBISEjhl26D/QI6h\n16xZs9SLVHR0NCddSavB8u233xKRp6ennks7Go2G+z8Vd9e5bNkyHrkxv8Vr164lImdn50ePHkk+\nyYssJyeH6/OYm5s/3+v++PHjiahy5colRK6XL19ORF5eXpL3RRlaqUNb9eWHH34o/FWVSqVnrb8S\n3Lx5k+9ST548KfkkLyNt/ZNVq1ZJOb5fP0Ekik9dZxqNpoRFGu0Og4cPH0oZgxDi34n3p59+qufj\nuQBDcSVS79+/b2jtowK4JICNjY0x35SRXoWAu0qliouLO3PmzPbt23nVunnz5rm5uTI+RVJSEm8m\n1bO2cpFu3rzJuauS15aFEPn5+Tzj3717t+STvByKzL3Sex9fCbi+/86dO7nL3+eff67ngenp6Zx/\n1LVr11LnK3fv3jU3NzcxMbkhqUD8kydPKlasSIYUMktMTORDCqcHpqSkcCik5KVyPZ+FK978+OOP\nRp7qudF9aWmjDKNGCX////8zcKAIDJwZHKxbaJVvtDw8PCTuSzp1SjRrJt5/v+RHZWVlcb/ctWvX\nFvmA9PR07tsmeRMxT+NatWpVeNVkVXz8yvj4PcWXaMjKyir8SeMj5hyv79Gjh7TDX08+Pj5EZGhq\nZ2H37t2zsbEptddWAR4eHra2tsVl3hmJS4Hb2Nh4eXlxzWUfH5+mOjp27Oj7r7ffftvf37979+6m\npqayryS9FtLShFIpLCxEaZsVSqgAy2GIt956y8ixdOrUiYgmT56s5+M5YvJ+Ue+rnGllZ2dnTHVB\nLknp4eEhbVfiC63IS+GgQf+5FPr7Z40eXaDmOO+7l/52/cMPwtlZlJZSp817OnPmTJEP4FxjOzs7\nCVvpGScQDBo0SM/Hc8qLs7NzcREQbi9mTHYOZ07oX0ri2QgPDzc3Nyeitm3bPsdb1hLw9evmzZsj\nRowwaOYcERGhUCiUSqU+3SNOnjzJLzlDN4SxEydOmJiYKJXKI0eO6HnItWvXuFVP4eYZHBnx8PAw\nfpPZ06dPeSf3t99+a+SpXnwPHjwgIhcXFyEEVz8wqHJCVlYWh6t69uxZckYUF7zWP+pUQH5+Pg/v\niy++0POQGzdu8I6uv/76q8CXsrOz+Uesfz2c4vBNtORl7BefRqPh/UwKhUKfsmZFMzpwoVKpuLRa\n9+7di0xxS09Pd3NzIyKD88D+q2fPnmTIPgx+53F0dHz8+HGBL3GttmrVqhkZhZs0aRIRNW3a9HXL\nLuX0cz0b2BT03XeCSAQGlvpAXkR3cHAoUI8xOzubt7EaWUM/Ojqatx5evnxZn8c/ffqUaygVuaGH\ncy/69+9vzJC4DeFz3DbxcgTcc3JyHjx4EBUVtX379vDw8JCQkMDAQD8/v9atW3t7e+v2fPfw8ChX\nrpyhFTz0wR2TRo0aJe3wnJwc3rnfr18/I0fChZLffvttI8/zoisy96q0Pe/68PLyIqIbN2589tln\nhi7D3rlzh8PNQaWNhO+7jJmRcLDA2tpa/5A9XwWtra0LpJ7x1KFr166SB6NLW/HmZe2cI3UtR1vI\nZZreyVP/ceOGIBIVKpT6QN61WtyGr8mTJxNRy5YtJW8ySEtL41nab7/9VuBLHHA/nppa+KinT5/2\n7t27SpUqhVebeA9mCYUdSqWtSLNr1y7JJ3ndvPPOO0S0Y8cOI89z+PBhIrKwsPjggw8mTJigTWmf\nOnVqqI6FCxdqC0r8+OOPnMZizA+9ONoNgPqkq2s0muTkZJ6UT5w48fWco8vAx0cQCT3WKvj939XV\nVXet5dixY9J6pRbGq3eWlpZ6JvnGxcVxPsSxY8d0Py+5V2oB2i3b+pekfGlIvRRqdzPs3LlTyvOu\nXi2IRJ8+pT6Q14bbt29f+Ev5+fn16tUzMkTIFWn1qcyuxevuRdaguHLliqmpqampqZ43mUW6f/8+\nF8k9ePCg5JOUhdOnT3NQ29XVtXA4zzCy5tAIIXJzc01MTExMTHJzc7l8cMm9nQuYMWMGEdnY2JRa\nuJn32k+dOlXyUHn+Vq1aNf0r0nz33XdE5ObmVmCZh6+SRr6/ae3du5ffw/VdwlepREKCiIuT5dmf\npb179/K7ilqt5t81Q5dPbt++zYXvZ82aVdxjoqKiFAqFpaXl/fv3JQ/14sWLHLcqodhCAbxHtkaN\nGgVSnr/55hsiatCggfGzo9u3b/P/m6EFJF8uYWFhSqWSg3SG7Qk4f15MniwGDDA+cHHv3j1+pRX5\naz516lQjbwbZ3bt39exQosV7ywrUJtWGTfVZvCxZZmYmX25et6ZxRtU/OXZMEImGDfV5LO+GmTRp\nku4nuYhZvXr1jN/KxsUk9C+rUFwPJ86rMH7bX0xMDCdwnD171pjzSPZCB9zVavWCBQs4ClMCpVLp\n5ubWqFGjd955hzcdlEXhrcuXL3PCnbS8Bo7tVq9ePbWoSJZBkpOT+Q5BzxJyL6sic6+MDrjrzsu5\nTLBB83IhxJkzZzjfpIRK+vfv37ewsDAxMbl27Zoxo+Weq2+++ab+2zwHDRpE/61Fk5qa6ujoSESH\nDx82ZjC6eP9p4fI1OTk58fHxd69eFUePiu3bxZo1YsECMX26+PxzYXiX4BcQF5a1tLSUuKrn5iaI\nRGk78dVqdbNmzaioWq6xsbGcwHLq1CkpA/gXp20WLkqzKj4+rfgXGwc4CiTIcNJf+fLljaxtwvtn\na9euLaFG2+uJV/X0L3hdJI1G07x5cwnTu7Nnz5qYmJiamhqfYl8A94qoUKECh3SPHTvm4eHhqMPM\nzKzANCAmJkbozNF/+ukneYf06hsyRBAJ/V5LnHU1c+ZM/qeRvVILGzhwoEHL1VwwrcCWHcm9Ugsz\ntCTl64CDgNWrV5eSXRsTI4iEi4soLUyQlpbGKQ6Fb+B/+OEHDlwamd6rZ2V2rcuXL3NUvfD7Hid+\nBgcHGzMe8W/8V5abXnklJiZyONvU1DQkJERK5C4lRezeLW8OjRDi+vXrROTl5SWE4AppBs29NRoN\nT7bd3d1LCI9euHCBC/4UzuvUX25uLm8S0n9zrVqt7tChA/13H09kZCRJKiVfAk4sa9y48ePHjy9d\nunTw4MFNmzaFh4fPmTNnzJgxeyZOFN27i5YtRY0awsHhfyWn2raV69mfGd7c/Omnn966dYuIKleu\nLOEk+/bt480KxS06vv322yRHeVsuttCwYUM9L2QqlYpfYLqJQenp6Vx7Xa6MFq59X7Vq1efbh7Cs\nbd68me/3e/XqVfoWt8uXRUiIqF37f78ajRv//7tcRkaplbWLs2PHDoVCYWZmduLECd3P379/n6Pk\nxu9cF0JMnz7doOtgkVVfvvjiCyq+MIihuEFFCfvJXlXS659kZwtzc2FiIvS4KJw+fbpAmxztuoss\ni/3a8nr79u3T5/EqlYo3beve0qpUqvr16xPRvHnzjB9ScHAwEXXq1Mn4U0nwQgfcx40bx1cI7gXf\ntGlTbgQ/ceLEsLCwiIiII0eOxMTE6E5JeXGmd+/eZTEeXg6S0CGX3y4tLCzkWlfhLZMjRoyQ5Wwv\nqCJzr378URhXNi46Oppv0oQQnP4mISNvw4YN3MykuDSfUaNGUTGb3A2SkpLCewD1f9Vpd4Zq18O5\nA4Fcl0CmrXjzxRdftG/fvn79+ry5hINfPatVK1wPXZiaisuX5U1rei74xuzdd9+VcvC77woioUd1\ntoMHDxJR4Ssub4GX0IKpgCKz9RNycwOvX79VVNEYxn0y2+rcYmVnZ3ORK+NDnHl5edy9Z8GCBUae\n6jUxd+5c4+/ofv31V5K6gZGvRJ07dzZmAAXk5uZyxESb2MK/C8VRKBSOjo7a3TbcKMXJyam4vmFQ\ntGXLBJFm8GB9HnvkyBF+d+Iqrkb2Si3s9u3bvKxYXC2RAtLS0vh6tGnTJv6Mkb1SC+PdJJL3OL56\nVCoVr79+8803Uo738BBEQo+o6OLFi6nQFnUZM+m0ldn1r5LH3S8L3LZpd9kbHx3Izs7mjZjLli0z\n8lSyy8/PDwkJ4cTPHj16GJCBdPWqCAoSNjbC3FwMH/7/oaiICCNn9UKI3bt385UoPz/fzMxMoVAY\nWtE4Ly+P9y40adKkuPcxrvYzZswYI0d7+fJlfnfSvzTo7du3uWXL77//zp/h0c6dO9fIwejKyMio\nXr06v7YLX2rnt2z5nym9iYkoX174+cm+X6GszZ8/v2LFiu3ateMJjOT94pxL7ujoWLibBV8ibW1t\njVmbYZmZmXyvqv877cmTJ5VKpampqXbHBi8rtm7d2sjBaKlUKt74pdf8MzNTJCeL2FhhRLL/83Li\nxAlXV1cieuONN4osW3/lypXrCxaIOnX+/1ejfHkxcqT47bf//V78/rvo1UvY2Aipqx1BQUFE5O3t\nrZvVxNl1AwcOlPiN/VdWVhZfdEpIJSyAN+tod5RqM4j1341RKn6Xew3nXVyNSkr9k2bNBJHQL2LO\n8QRtg5BevXoR0QcffGDwkxaDC3I0bNhQz1Uc3j7r4uKifZ3zvhyJiR2FJCcn836R57KT/oUOuPOu\nij/++EP/Qx49emRpaalUKsui2AWX+HBwcPj444+/+uqrlStX7tu37+rVqyXfZGo3BBlZEUnX9evX\nlUqltbX167Xul5Mj3nhDVKokDCm3V8Cff/5JRG+99VZeXp6pqSmnuks4D6fUOTo6Fo7Xx8fHW1lZ\nKRQKWRI/9+3bx6s1+jfa3bFjBxFZW1tfv349IyOD5wr79+83fjC6+PbSwsJCd0Zubm5esWLFnq1a\nidathZ+f+PBDMXq0mDFDLFokfv1VBAXJm9b0XMTHx3MzBim7Kb//XhCJ/+7CK06PHj2IKFCnHBsX\ntra2ttauSBujcLb+kri4plFRU4tP3s/MzOTdEtoZFS/nNGrUSJZeW9y9x87OLj4+3vizvfI4Vm7M\nwp6RLXqSkpI47GV8VVAtnqI1aNBA+4rKzs6+d+9eso6SU71465K0PoqvrYfnzw+oXbuBj4+ej+cN\nxWPHjtVWFzE+9KlrwoQJVEwtkSLxWqC3tzdf0I3slVrY1atXOa/5Fd9ZaAjOsbW1tZVS1Lt/f0Ek\n9OhVWGQTtjFjxhBRx44dDX7eovzxxx+8SqfnjDo5OZnf97QLPHl5edwPs8g+chJs3LjRoCE9Y3/+\n+SfPBKpXr37x4sWSHpqdLdasEdpYrUIhOncWO3b8LxQ1Y4YwNRWVKgnjkjT513/48OGxsbFE5OHh\nIeEkT5484WaAffv2LZy8f/PmTRMTEwsLizg5iqjwYnmlSpWSk5P1PIS7Izo4ONy/f59Lydvb20vb\ncl2CgwcPKpVKExMTb2/vdu3a9enTZ/jw4VOmTPnuu+/2//ab2LFDHD8url8XupV/5d6vUEZycnK2\nb9/u7+/PO+QUCgURVahQITw8XNoJNRoNR6zq169fIA7Qvn17klYUoihccNna2pp38umD6zm0aNFC\nrVY/efKE71nk3f1/5swZ3uMYFhY2dOhQf3//bt26+fr6Nm3atF69eit69hSursLO7j+LNM2byziA\nZ+bWrVv8zuDt7a3t5Hf79u2wsDDe7TeqYUNBJBwdRUCA2L5dFJig5uSI998XRMLcXBTTmqtkeXl5\nLVu2JKL33nuPP3P27FmDKu/pY9OmTRzW0DNbRbuj9Oeffxb/xoiHDBki13iEEFeuXOGSSqUW+3rF\nSK5/cv6rr+a1aLHy++/1efD169f5v/fq1auG9s7VhzZ96ufS+rhqcWYzL+PFx8fLXmmWtzfVqVPn\n2W8ffHED7nl5eRy11H86wrhzl/E5CIX98ccfHOYuvPJvaWnp7e3t6+vLCfjh4eGRkZGXL19OTk4u\nueWFZLxb7RUsKlqCJ09E27aCSFhYCKmJP1u3bq1Xr17FihU5R8nb21vaeTQaDZdV8fb2LnBxGjt2\nLEnOgC4K95Ns2bKl/jFN/i1o1aoVR69atWol12B0vfvuu6amplOnTr148eL9+/cLrjxlZYm4OHHp\nkjh0SGzdKlatEiNGvBTz8lLxzZKPj4/Bb9mnTwsiUbu2Po/Vdh3hlRttnZkSqkYa6oMPPtC+VnM1\nmi4XLjSNirpY4mY0jnQMHTpUCJGQkMDzeBmrzXIgT7vkDiXgLCpjfrv5ldywYUPJZT3Dw8M5wCHL\nxnbtJkRjaoPyHB3dUw2iUql4M6meM65//vmH+yTzXZZcDUK0Su6hVFh+fj6HZRcuXChLr9TCAgMD\niaiPHpXHXx+cEvXRRx8ZfOQPPwgiod9WLc4h0CaP37p1S/ZanFwNptTePFpLly4lIk9PT64zwAV2\n5K2HxguH+hceecbu3r3Lm+QsLS2L3N928+bNcePGPeFJO5FwcBDBweLfcNX/PH4sfH3/twMyNLTU\nEkPF4VIGAwcO5OtRu3btpJ0nOjqaFxImTpxY4EsfffQREX0iU2lEtVrdtm1bIhqs36YixhXku3bt\nyh9IbCZUmq5du9asWZP7EKSmpt66devkyZM7d+7ctX69WLBATJsmRo4U770nOnQQ9euLypVf/IB7\nVFRUUFAQX1CIyMTExNfXNzw8nF/ANjY2kleL09LSuAyCbt7Dzp07icjFxcX4ErJaXGZN/57kqamp\nvNd5yZIlXJTmnXfekWswWlyigZ+ogHktWvx/nN3SUjg6iqpVRbduL91+CPbkyRPOAXV0dAwMDOSi\nPczJySlw2DDN3r2ihPtBjUaEhPxvxVFSF9Zbt27x3Jgz0PndY8qUKVK/oaJxZEn/dzmu+lK+fHlO\nDdStTyKX0aNH821O06ZNfXx8vP8r38dHODr+54+5uTFJmS8OaVkFa9euNWiayoVJ+/Tpw/vLJdTw\nKBm/QrQFQkt1/vx5vrO4desWv+nJO+XOy8vjxTP9dzTK5cUNuPMCvo/e+VZa//zzD6/SyHipE0Lc\nu3fPycmJiEaOHBkeHv7ll18OHjy4U6dONWvW5ApfxVEqlZUqVSrQCNh4vIOyUqVKr1e9Y5VKTJz4\nv0v4Bx+IUkuq/UutVu/atat3796mpqb8c1EoFAqFYvjw4ZIzc7Oysrj2cbt27bRp8omJidxIQMZN\nVampqZ6enkT09ddf63lISkpK5cqVOehARHv27JFrMLq4M0GHDh3GjBnz0Ucf9ezZs23btnXr1nV3\nd9c4OhZRVWbjxpdxplVYbm4uv2UbXD5bpRK2tjk1aqTrt8+U/4d5x+tPP/1ERJUrVy69kqDedLP1\nt58+3f3cuYGl5W/eunVLqVRaWFg8evToww8/lHdtSehUBtSzmsTr7N69e0Tk7u4u7XBDS+wVSVub\naPLkyZJPosVpWcbfGXL8pXnz5uieqj9ODtB/qYN3NBORLL1SC+NKNYV7KBVHuweRK4TI1UtQ69Gj\nR/xuuXz5cm4jvGTJEm0b4YMREUL7Z/duERkpIiPFkSPyjuFFoy3do1vIVS8XLmicnO7p3VCLA+Jj\nx44VRe39Mp42k66UfO1/ade/Z8yYkZSUxHcH8u5TLpzcl5KSot3l8zQ5WRT+k5go5NhqpqecnByu\ndUBEAQEBPDNRq9WRkZH+/v4mJiZE9H2rVqJpUxEeLorbCpyfL0JChFIpiESvXsLAlG1+ur59+5qY\nmJiZmVlbW1euXLlw8xv9HTp0yNzcvMAN+b1798zNzU1MTAoXD5EsJiaGbxY2btyo5yEPHz7kDdPG\nl5IvTl5eHmetOjg4FOiV0qB8+SJm9UTi5MkXc2IfFxcXFhbWoEED7bfg4+MTGhqqLQySk5PDk1iF\nQhEiKQwqhIiOjuZ51Pfffy+E0Gg0XGtF3tBVYmIib1Zet26dnofwfm5bW1tOXjSy7VOR0tPTK1Wq\nZG5uPnXq1IiIiB07dkRGRp45c+bixYsJsbHi0aMifp1f+OWZ4sydO9fLy4s3uvEvSEBAwPbt2w3Y\nJf/TT8LUVBCJIUMKZsHrgXdiWVhYcAH98uXLyxvmEkLcuHGD77/0f7Vw1RdutfLVV1/JOx4hRGpq\nqpubW3ERNrWraxHvSL/++mK+IxlEW//EoNb0N27cICI3Nzc9H//w4UNtNWAfHx/ZI4oajYbvLPR/\ng+X35M6dO/MSjsSGecXjnRzly5cPDQ0NDAz84osvJuq4sXixCA39/z/h4SI8XJYlHIUQooRg8XO0\ncOHC4ODgjz/+eMWKFYYe2759+8OHDy9atIhLaRsvPz+/ffv2x48f9/Pz2759O+9E0/X06dOHDx/G\nx8fHxsbqfsD7v3r16hURESHLSLSEEHXr1r127dqGDRu4sOBrZP16Gj6cMjPv+furv/6a644VJyEh\nYc2aNcuXL+etpubm5r169QoMDIyJiRk1apRKperQocOGDRu4W4ChHj582KJFi7i4OO0LddKkSV9/\n/XWPHj345l8u+/fv79Kli7m5eVRUFBdOJaLs7Ozk5OSnT58+ffqUP9D9+8aNG2fPniWi+vXr8yqU\nvJKSkrhhTpFfzXdzM0lJIUdHcnL6/78nT6aaNWUfyXOxbdu23r17Ozo63rhxQ5s4o4/+fn4RO3du\n2bKld+/epT74yZMn3Gl569atI0eOjI+Pl/33fd68eVOmTPHx8bGxsTkTFfX7rl393nqr5EN69Ojx\n559/jho1asmSJaampleuXOG6n3IZN27cd9999+abb3p5eeXl5RX46goHB7uUlP98KjOTpkyh1q1l\nHINc8vPz7927p1KpdH9TMjMztd+XECIlJYWIKlSo0K5dO4NOrlarrays1Gp1dnY2xwgMMnLkyB9/\n/NHPz48TSCWLiopq0aIFdxHkLAlprl+/Xr9+fY1Gc+HCBe27nDTp6el16tR58ODBqlWrODMRSvXF\nF198//33s2bNmjZtWnGPefjw4dl/nT59+vHjx6ampsOGDVu2bJns4+EeSrdu3WrWrFn16tU52E1E\nSqWSAxwFPlYoFKtXr7527RoR1a1b9/z584Wb6xpp5syZISEhlStXvn//foEvTWjS5Otz5woeULUq\n3b4t7xheNFOnTp07d27Tpk1Pnz7Npb31ITQaFxeX5KdP4+LiKlWqVOrj//nnnyZNmpiYmKxYseLD\nDz+0tbW9ceMGF+6XS1BQ0A8//NCpU6f9+/fr8/hjx461bdvW0tLyvffe++WXX3x9fbnGjoxGjx69\naNGiRo0aXbhwocCXKtrYxBc5+0pIoAoV5B1GyX755ZcRI0ZkZWXVr1/f19d348aN/NthZWX1/vvv\nfzpyZLPmzUs/y9at9NFHlJqa1aDBnV9/9alfv9QjEhMTV61aFR4eznN7CwuL9u3bnz17NikpqXLl\nyhEREVyEQYJVq1YNHTrUzMxs9+7dvMP9s88+W7p0aUBAAOcPymXx4sWjRo1ydXW9dOkSR6xycnK0\ns3qt+Pj4hw8f8sd3796Ni4tTKBRDhw6VcHdcqpUrV3788cfe3t78v2pra+vi4uLq6urs7OxRocJy\ne3tycSFnZ3Jx+d8Hzs7k5kZ6/+I/Azk5OTt27Fi7du3u3bvVajURubm5+fv7DxkyhJt7F7Bw4cKx\nY8dqNJr3339/5cqVJafQFWnbtm19+vRRKpW7du1KSUnp37+/u7v7zZs3i9wQLxm/LF1cXK5everq\n6pqenp6Xl5eampqdnZ2Tk5OSkpKbm5uZmZmRkZGbm8ufX79+PQfg/P39ZY9CsEaNGsXExIwePdrT\n0zMtLS0vLy8tLS0rK6uuldUnd+5Qbi5lZlJ6OuXlUWoqlStHnTrRt9+SqSmNHk0LF5bFkMpI06ZN\nz507t2zZsm3bth08eHD27Nmc2GGYrVtp0KDTXl7f1qq1au1abaxTTzxpNzMzU6lU4eHhvOtOXhMm\nTPjmm2/c3d1b69xS8WtM92E5OTnZ2dn8Je5QXb58+djYWEO/I30sXrx47Nixo0aNGjp0aIFfzyqW\nliYFfmHLlaMJE17S11gBYWFhY8aMqV279qVLl7TZoiXjH8STJ0/u3bvHaZdFysvLu3nzJs/kN2/e\nHBcXR0T79+8vsnuHkXimZG5u3q9fP+2Pz9bWVvc7sre3184e09LSvvvuu9zcXCKaOXPml19+KfuQ\n2rVrd+TIkSpVqty9e7fAl660betz5EjBA8aPp/nzjX1W42P2ZeT9998nqY34ePmievXqcmW3jR8/\nnogqV65saDnFS5cuca9h3U4XcuE9rc2bN69bt263bt2GDx8+Y8aM1atX//XXX9HR0aL4zoevgkuX\nMtu3r2Jv7+TkVGRWkTbLRnvL7eHhERISotvz5NChQ3zDVrlyZYPzs/519uxZnlQtWrQoKSmJ+xqV\nRSoBX1ldXV05hdzS0lKfX3AbG5u6devKUl+7gEmTJhGRn5/fl19++d13361atWrr1q2HDx++dOnS\ngwcP8l/tl58Q4t8d35999plBR3Ev+HHjxun5eM5l4IXuN998U97KVEInW5+IHB0d9Umf37t3LxFx\nhFeWvOYC0tLSOKOhyCBydo0aRWQ0LFz4YmY08DxGH76+vhLOz8lo+lf21Lp27Ro3seCd40b6+OOP\niahLly7GnIQTV+Xas88F7suXLy97ldtXFfeb7dGjh/YzarX6+vXr69evHz9+vK+vLxdb0MWXIdnr\nyWj17duX0/oMYmlpKWNTAV0ZGRkVKlRQKpUffPDBxIkTP/3008B/rQoOFv7+//vz9tvC11f4+ooP\nP3xJd9DrLzMzk3fgrVy50qADuQ+ttglkqfhNhtdd9N/tp7/k5GR+sS1evDimGBcuXIjSwXMAhUJh\namp66dKlshsSs7e3d/xXDXf3glvpHR2Fi4s4cuTZv96++eab8uXLm5iYcDZSjRo1QkNDDa4+f/Om\naNjwozZtLC0tSy75GhUVFRgYqL11r1SpUkhICKd7x8XFcZzI1NTUmJKbfNPn5OQUHR2dkJDAOcKy\n/4g1Gg0H9F1dXT09PfWPVSmVyjfffFP2/Vv5+fncimDNmjUPHz6U1uDqOYqKivroo4/4LoyIrKys\nBgwYsHv37lLvgHbu3MlvLC1btpTWQIgbSDo7O3P2SVnUK9BoNG3btuWeXgZdEG1sbGTsIadr165d\nRKT9D9c1oHbtIibq5cq9pBfE3NxcTv1OS0vjZKlff/1V2qnUJ0741KxJRM2aNUtISNDnkJiYmDVr\n1gQFBTVu3JiILCwsypcvXxb39UKIS5cuWVlZafMY9GRvb1+vXj3Z708ZV5WpW7du4H8FBARkDxny\n//Muf3/Ro4fw9RUDB76kuygK0NY/Mah9Os+sCsyB09LSDh8+vGDBgoCAAB8fH95/psX/3LZtm9zf\ngRBCqFSq8uXL8yxRf1ZWVnZ2dlllE0o6deqUQqGoUqXKsmXLvv3221AdMfPni4kT//9PYKAIDBSb\nNxv/xvXiZrh7eXnduXPn0qVLEjLd1Gp1zZo1Y2Njd+zYwb2zjLFnz57u3bsrlcqDBw+2NjyJ0tfX\nd//+/d999x2X9pZRVlaWp6dnUlJS4S+5WFsnZmWRiwt5eFDlylS58v8+8PcnAy/VL6y0tLQhQ4Zs\n3rxZoVBMmDBh7ty5vD729OnTtWvX/vDDDzExMURkYmLSsWPHwMBA3nNa4CQPHjzw9/c/ceKEhYXF\nDz/8MHz4cAkj2bhxY79+/ZRKpb+//++///72229zwR95Xb58uVmzZpaWlqmpqfwZKysrR0dHJycn\nvgHjD3T/trCw+PDDD+Pi4ubNm8fxcbkkJydXrVo1PT392LFjXNjuNXTt2rWGDRtqNJpz587p7lot\n2b59+956662WLVueOHGi1AffunXr6NGjQUFB6enpCoXizJkzTZs2NW7URdi6dWufPn2IyMHBgbfG\nF05nKCA1NVWj0Tg4ONy7d6/I2baRVqxYMXz48C5duhT+lXzH1NQmP/8/nypXjrZupaVLX8CMhidP\nnrRo0cLMzIw3j7Ny5crpriVwHLNu3bohISGGnp+3cx04cID3deqve/fuu3btGjlyJC/cGikpKalW\nrVpJSUl6bt0o7ODBgx07drSxsbl586ZciasdO3Y8ePBgUFDQwhfpJfEs5ebmbtmyRXvhs7Oz036s\nm1HCH9+/f79Dhw5OTk7ff//9+fPnz507d+HChfT0dN0TVqhQoUmTJo0bN27SpEmTJk3s7e1r1KiR\nnJy8d+9eLvohoxs3bjRo0EClUs2aNcvb2zstLY0/r1artR9rNBrtBZE/Pnjw4I0bNwYMGPDbb7/J\nOx5+6tq1a2dlZYWFhXl7e+vm15czNa2gfSc0MyPt73tw8KuRbFWC3377bdCgQeXLl79x44b+N+pz\n586dOnXq559//sMPP5T8yISEhFOnTv31119Lly7VaDTu7u6xsbGGRp30sWjRojFjxmg0GoOOMjMz\n8/Pz27x5s+zjIaKxY8euW7du3bp1vr6+eh3wPF5vbdu2PXr06KxZsw4dOnTw4MEZM2ZMmTJFwnny\ns7KGf/bZ6tWriWjkyJELFizQ/Smnp6evX79+2bJlnO+vVCo7depUeG6fn58/bdq0+fPnCyH69Omz\natUqQ4NHRMT9MLds2eLl5fXOO+8sXbq0b9++nMslr/Xr1w8dOpRD20RkaWnpWIi7u7ubm5v2nyYm\nJl26dImLi1uwYAEX0ZbL77//PmDAAG9v7+vXr+uZUPlC+emnnzgzqWnTpgEBAR988AGnqujj0qVL\nvXr1un37dqVKlbZu3coFo/SnVqvbtWt3/PhxIqpWrdq1a9dk319FRB9//PHatWvz8/OJqFy5chYW\nFg4ODpaWlhycsrCwsLW11X7ewsLC2tr6woULv//+u5ub29WrV7m9uVyEEM2aNTt37lxwcHBiYmK5\ncuVsbGwsLCzs7e0tLS097ez6WFuTuTnZ2lK5cmRhQQ4OZGlJ7u4yjuGZiYqKat68uY+Pz5UrVzw9\nPe/fvx8dHS15Q2dsbGy3bt2uX7/u5eW1a9eu2rVrF3hAenr6qVOnTpw4ceLEiZMnTz59+lT7JXNz\nc963cfr0aS5eJK/+/ftHRER07tyZq3szfo3pPkz3M5mZmf369Xv06NFvv/02YMAAeccTExPj4+Oj\nVqv5uy5AVamS6YMHBT+7YgXFxJCtLVWvTv7+8o7nGeNdlS4uLn/99ZeTk5PujST/ghc+ZMaMGV99\n9dWoUaP69eun3ZAaHR2tO7ExNTWtWbNm03+dPn36iy++qFWr1qVLl2R/4+I8fXd39ylTpmhPnp6e\nnq9zI88hBf5YCJGYmLhp06a0tLSIiAj/MvgJnj59umXLlrVr1w4JCTExMdF9Y2xoauqqG9NQKsne\nnhwdacYMY2dWsq0XyCohIYGI7OzsJK/gcQsjaTmDBUbCN//z5s2TdgYuLVKlSpWyWI3s0qVL1apV\n16xZs2PHjmXLlk2dOjUgIKBDhw6D27YVlpYF15YVilcs7V2j0YSGhvJUu1u3bgcOHCiQ9jJx4sRS\nO3ioVKqJEyfyIQEBAdLW0zhtmbN7jpRN2VaOPPbo0YNTyPUc5/79+zkhghtvyoW/X/0b+LyquICp\nQV1N0tPTTU1NzczMiswlT0tLO3LkSFhYmL+/v26ZI1tbW9n7UmhduHBBqVQauhOwXLly7u7uOTk5\nZTGkJUuWEFGrVq3CC0lbtUqEh4sff/xPnbUPP3xJMxoyMzOTdcTExMTGxnI25bVr13SzKQ8ePBj5\nr71790ZERERERHDrpFmzZhnUv/fAgQP8opKWzFWkxYsXE5Gnp2fB5sl6UKvVvJI0Z84cucYjhLh8\n+XKBIshCiNTUVO3/tnj0SMTE/P+ff/4RUVHiZUvrK8GjR48MnRMWiLO4ubn5+fmFhIREREQUuRmC\n+3I3aNBA9hkOpw8buuMhLi6Ou78eO3ZM3vGIf9+XigxbhOr2iNP+qV//5S1Za5D27dsT0RdffKH/\nIYcPHyaihg0bFv6SSqW6fPlyeHg4Z2PpVnG0tLSsUaNGGSX3cZjY1tbWuxgNGzZs+l/cMLCM9hHm\n5+fzWr6Hh0eB523XooVo2vT//zRoILy9hbe3GD782b/e3N3diejOnTu8C8GgdLzC1qxZw3GEJk2a\ncP3W6OjoiRMnanfYVKhQYeLEiSWXdt26dSv/nnIQQcIwsrKyuD0J32KUUVMZXikfNWrUnTt39G88\nvnfvXp7Yy7JBjWk0Gs6fXb58uVznfMZSUlKmT58uuc7+kydPOnTowG8yepZKf/DgQURERGBgoLZ6\ng6Oj43vvvSdtAKU+F++0OHr0qP5HqdXqNm3aENGnn34q73i4Ro27u7uMbaVeWFw078MPP3z8+DFf\nI4zcX5KUlMQ/F0dHx0OHDol/X0tBQUGtW7cuEPTkaVhoaOiRI0eys7O5C26rVq1kzyg/ceIEF86+\ne/euQQf+/PPPRFSxYkXZ71J79uxJRIGBgd99912Bm8E1a9Zkb9okdNvnbNsmIiOFoZurXlR///03\nTzgL7y4tjFMwHR0ddYPyWubm5s2aNQsMDAwPDz9z5kyB3Utcv5GIfvjhB3m/hYSEBL4Q79ixw6AD\nly9fTkReXl6yBxnUanWLFi34vavwf1Rs69ZFzORDQoyfyb+gAXdOFTFmf3pqaipvENOzA1KR1Go1\nJ5V07NhR8mRao9HwKujmzZslj6RIUVFRFhYWCoViwIABYWFhGzduPHny5IMHD/7/MpCQIKKixNat\nYtEiMWGCCAx8STdzlWzPnj3aPkJEZGpq2rt3by7ep/9JfvnllwJTfH0kJibyZItXZWxtbV1dXcti\nG6a2ZVN0dLShx44YMYKIGjduLFc3jNTUVH73P3z4sCwnfHlpd3zr3/ZKCMFZCQcPHhRCqFSqs2fP\nLlmyZPDgwbVr1y7QH6JixYq9evWaO3cul57UvxCNQfhdjhsbsIcPHyaXKDExsX79+mTESmQJkpOT\nuSx+kXWTsmrWLOJyGBb2Yr6zpaWleXt7Ozs7a9PTSt0QYOimLk7fMzMz8/HxCQgICAsL40l5cUNS\nq9X8CpT3Z6dWq7mJ9PTp0w09llMaK1WqJPudG+9FLTIPyNzEpOgWcLdvyzuG5yglJeX999/3/1fX\nrl19/9W8eXNt8K5atWocT+Roe/fu3efNm7d3797ExMRSnyI7O5vrGhlaUaRkXN/GyclJnzEUwLv7\nZb8dffLkCc80Pv/8c/5/a9SokTYU+2P37v9f2cPG5n+vpcaNX8lJV2Hnz5/nrpVczlUfOTk5lpaW\nSqWSiz7dvn17/fr1o0ePbtmyZYHsdTs7u86dO0+bNm3Tpk38Ylu6dKns38K9e/d4qcageFZOTo7E\nDup64ChPkTXu7Swsin772r//Gb/esrOzFQqFmZlZfn4+h4/1b7xcnKioKG7O5ODg0LBhQ+133a5d\nu/Xr1+s5x+amIERkY2Ozfv16gwYQExMTHh7u6+vL4adq1apJ+j5KcfLkSX55S6h7NmzYML60yTWx\n37ZtGxF5eHiUURbFSyE3N3fo0KFEpFAoJk6cWORd5L1799asWTN48OACRRLc3Ny6du2qVCrNzc2v\nXLki+9g4ef/999839MDo6GguhyJjNlh+fn6dOnWobIrnvID4123hwoVcRad9+/bGnzMzM7NXr15E\nZG5uXmArhpmZWYsWLYKDgzds2HD//v0CB6alpXGscPXq1cYPQ0vb31LCHF6j0XDPjIkTJ8o4JI44\n29jYPHz4UMbTvhRUKhUvt48fP/7bb7+1t7evVKmS7s6nElpEKBQKpVKp5y0h4/d/JyenpKQkGb+L\nwYMH038rVeopPz+fL9/ffvutjOMR/4byPTw8vvjiC39///fee89XR8zQof9JZWjcWHh7ix9+MH4m\n/4IG3CdMmEBEX375pTEn+fzzz4lo2LBhks8wY8YMIqpQoYKRaYC8n12WN2it9PR0juNzSkKBd+pV\nPXqINm3EoEFiwgSxaJHYtk2cPSuePHlVk61u3749ffr0Dz/8cObMmQ8ePJB2kvPnz3t7exORs7Nz\nCTcM+fn5x44dmzZtWtOmTXVbhFWrVo0vmUa+bov02WefEdHAgQMlHJuRkVGtWjUimjlzpiyDmTVr\nFhF16tRJlrO91LKzsz/44AMiqlix4i+//BKhH66w1qZNmzZt2hTYpmdlZdW6deuxY8du2LDhzp07\n2ic6d+6ciYmJubm5hBWXkm3cuJGIXF1dDb3l485yNjY2kn/jisNB0o4dOwYFBQUWkjJunAgMFJ98\n8p86a7Ju4JBRcV2FtaytrXWnUF5eXl26dOEQXq1atXRTGtu3b6+dE3Tp0oVDqFzsvkKhLnkWFhbN\nmzcfMWLE8uXLo6KidCMUnIri4eEhe3T72LFjnHZ3/fp1/Y/i2mhE9Msvv8g7HiFEamoq/xdp2dnZ\naf+3RYUK/0sL5T/16ommTZ99xOoFkZeXZ2JiYmJiYtBuCfFvuXx3d3cJmxuKlJmZyXFVabmWaWlp\n/Bshb5YDB2KMbFTwqrp27Rov3DZp0iQiImLFihWFNyfpWrJkSWhoaNWqVYmoZcuWBapIKZXKevXq\ncS/6S5cu6Ya9uKyHtJWYkvXv35+IAgICDD1wy5YtROTo6CjvkNLS0vi/Zd26dVGFnI2KErp/Llz4\n3zYdA395jXflyhUiqlGjhhCC38lv3bpl/GlTU1P79Onj5uZmZmZma2sbGBgoIX0qKyuLf22JKDAw\nsORIfU5Ozr59+0aPHq3bBJ47nSgUiv379xvx3RSte/fuRDR16lQJx8o+sefikGFhYbKc7aUWHh7O\na8/+/v48TXr06BFnHxco6mhra+vr6xsaGhoVFcXru1yIo2XLlvJW2L958yZv19N/RVMXNx6sVauW\nXKspK1euJCIvL6+XrtC/NBxpOXr06MyZM8nAvVwlyM/P79mzJ0fPK1SowLsJIyMjS93Czt2bK1So\nIGODwHXr1vE509LSJBweFRXFq01y3aWW0c7Xl8X3339PRNWqVcvOzh44cCARDR48uITHa3dL88+x\nW7duhj4jT+FkTOyTdkuoxUEGBwcHGWdW2izJDRs2yHVOPb2gAfd27doR0c6dO405yY0bN5RKpYWF\nhW6fTP0dPnzYxMREqVTu27fPmGEIIdLS0jgJUcY9iRzmq1+//v79+7/55pugoKDevXs3a9aMJ+h/\ntW9fROZLr16vasBdLqmpqVyA2MTEJCQkRHfC9Pjx44iIiICAAC5yzaysrHiyxekMx48fNzExMTU1\nPX36tIyjio+PN7Jl05EjR5RKpampaVRUlJGDycjI4Herv//+28hTvQLmzJnD1wNDa/npxtnd3Nz8\n/f15FbqE2TDXFJI33JObm2tMi6e+fftKi1CUgGtfmpiYGLM56cWh0WhiYmISExO1mwOkzWWLw4HF\n27dvP336lK8F77//fs2aNQtslTA3N2/SpMnw4cMXLVqkDeLIOAwtTmfw9PT09/fv27evbuJAmzZt\ndNcPatasyesKzs7OCoWifv36sreAYzNmzHB0dDSgHdDrepXkridVqlQx9ECNRsObG+QK/XDHwqZN\nm0p+SXCBo2rVqskVDjh9+rS8N5OvmG7duhGRvb29oa2xtPW17e3tfX19Q0JCtm/fXnKOlbR25SU7\nfvy4tK307O233yaikSNHyjgkrhvQunXrMupEJxcum/nWW2/l5ubyip1cOdcajebu3bt79uzRv9ZK\nkdasWcMzrmbNmt0utIHp8ePHa9as8ff31y317uzs7O/vv2bNmuTkZE4x8fDwSE5ONmYYBVy4cEGh\nUFhbW3O7Vwm0E3vj7zj++usv/q6N/K9+ZezatYtfD1WqVOFiC7pvWT169FiwYMGFCxcKX6FSU1N5\nS4q8qd8ccZOcQZiTk8MJ6bJco3Nzczk7TXLj0JdMTo7aze3xG29kpqev+fTTFu7uMs6fOa46ZMgQ\ng47SaDRcxm3s2LGyDEOWrYry3qWWXW7Qi09bieXPP/88cuQIT070rL7ATXEkzEa4uqy5ubnkqly6\njNn0rMUzqyD57sVGjhxJRO3atXv206oXMeCuUqnKlSunUCiMX9Pg9AEJi2PJycl82zBt2jQjx8DG\njBlDRB999JEsZ1uxYgUR2djYFLnWnZOTkxETIw4dEmvXirlzxaefCj8/0bChmDz5NdndbAyuC8+p\n635+fn///XdoaGjr1q11k9m9vb0DAwMjIiIKz02/+OILIqpTp06p+3f0x22R/P39jTkJb/ho2LCh\nkQEIrtjbqlUrY07yakhISOC6VXv27AkODq5QoULXrl399cAXoYYNG+7evVv/W7jHjx/zBdjIlUhd\n8+bNI6K6desamtPK7t69y3vwZdypyoGbESNGyHXC50utVg8cOFD7oy+weU03Bt2/f39DT84R0vLl\nyxf+krYZQOGW9HZ2dt7e3mU029i7d6+VlZVuS1h9KJXKFi1alMV4hBBclbVq1aq+vr5vvvmmbtBf\n4+Pz/+ntVar8rx5IQMDrGXDnyv7t2rWTcOyhQ4dIpp2/V69eNTMzUyqVJ0+elHwSbUnKRYsWGTke\nIYRareZqzlOmTDH+bK8ejtY5ODg8ePCgdevWVapUGTBgQOHNSbo+/fTTiRMnctrg4MGDDbrB41dI\ngd4MxtBoNPzzDQkJkXYG7YvW+JwGFhMTw1Ugyqh0uIzCwsKI6NNPP71+/ToReXl5Pe8RFeHs2bNc\no8bFxWXPnj1qtToqKorn9rqL097e3kFBQZGRkbprBiqViku+GhoUK1m/fv2IaMyYMcachO8OjL/j\n4FpAc+fONeYkr5gbN254eHjwLhxra2teDizw2ijSH3/8wROtuLg4WUZy6dIlDoTpX++0sEOHDnG2\n6dWrV40cz6JFi4ioXr16ZZQk8cI5dUoQiXr1hBCicmVBJORbd+cechJ+9S5dumRqampqaipLhzbO\nHmvYsKExP9OkpCTe6L9lyxYjx5OZmcl9EcooN+gF9+GHHxJRjx49VCoVF1WbNWuWnsdywoq0kqGc\nMtWvXz8JxxbAHY+ktfXSunr1Kr/IZSnSxaUC5PqVMdSLGHCPiooiolq1ahl/qn379hGRk5PTqlWr\nDh8+HBsbq0+0UaPRcF2tli1bypWpcfv2bRMTEwsLC+Ob1F25coUrN61du1aWsUFhO3bscHBw0J2I\na5PZS56s5OTk1K1bl4gmT54sy0gSExNtbGwUCsXZs2eNOU9mZiZXGpV8S8kn4U6extfofAXwFfG9\n997Lzc2tWbOm/sEdntlIKHXHqRDVq1eXZVuodsHAmE0806ZNI6ImTZrIMvOOjIzkG5WEhATjz/Yi\n0O0LXzIfHx9DT/7bb78RUc+ePUt9ZHp6+pEjR+bMmcMbpa2srOS6FSyAd6f16dMnIiJi48aNkToO\nHz6sWxIhOjo6JiaGyzhwgN74OXphW7duJZ0s2gI05coVsRVs8+bXc1maN4lL3rDCm8MM7XFaWKdO\nnWQ5D//oZSlJyXcOlStXlqtmzqskPz+f20589913sbGxHCbWM+WWZ9qbNm0y9Em57FibNm1kWTjU\n9pAw5ufLWTVyJaTzb9PHH39s/KnK2t0ZM062b3/ixx/PRka2r1y5W9euz3tERXvy5AmnyymVSt2+\nx+XKlevZs2d4eHjhWsla0dHRnCMv4bVapJs3b/ItoZEXYu0dx/jx4yWfhEvJ29vbSygl/2rj+jDB\nwcGGhgL4nU2u7qncNzI4ONjI83AhciOzOzMyMniX5NatW40cz0tj6VJBJAYPFgkJgkjY2Qn5Vho4\nxCktr/zTTz8lOeq7Pnr0iG8GIyMjjTwVby709PQ0Mi19+vTpRPTGG2+84Bu8ykL+8eOhLVtaWlrG\nxMQsWLCA/i0so+fhfFclbaEiLi6uXLlyRGRkFp126cX4uo7cu6JXr15Gnkej0fD9qVz1oAz1Igbc\n+de15FpFetq7d6+5ubmHh4fuPbajo6OPj4+vr29gYGBoaOiaNWsiIyNjYmK0OZ7fffcdP0y3hrLx\neAL91VdfGXOS7OxsXuySN9UCCrtw4QIRmZiYBAcH7927V/83u5MnT3IxIoNabxWH+xkY/14jhDh2\n7Bgv7hWXM6VSqR49ehQdHX38+PGdO3f+8ssvixYt0l1W5V+NsstFfYmcPXtWu/fq22+/JaI6dero\nOSnnivwSamWqVCoObcjSRWTIkCFE1Lt3b2NOoi3AbXzLRO1399133xl5qhfKunXrtOX7//jjj+Ji\n0JcvXzb0zBx10n8LV1BQEBHxzZKE1lul4m7nLi4uepaVvHLlCicv8LKNkakQhWnTnBcsWMD/4UeP\nHtUN+uffuPG/wscxMeLOHZGcLJKThdQG6S+7kJAQMmJX3/Xr1znvWHLpM2Fcr9TCOnfuTEZ38dL2\nSpUr1vaK4f3L1apVy8nJeffdd8mQrZy82evEiROGPmlqaiq/jxlfiFObSWdkDwltuwhD+3MWxntN\nXppOcX5+/1unXLxYEInAwOc9oGJpNJqpU6c6OTkplcqqVasWt1G1SJzI7+rqKq1IaQEfffSRLMuK\nQoizZ8/y7oqDBw/qfv7p06c3btw4ceLEn3/+uWbNmu+//37q1KmffPJJ375927dv37FjR+0jeS+4\nXPu5XyUBAQEkKbntwYMHvMxvfFT69OnTCoWiXLlyxqehpKSkcLnwn376SfJJ5s6dS0TNmzd/jSKh\nH38siMSiRWLnTkEkOnSQ8dxcIW3Xrl0SjtUWpI4wLjtErpimECI/P79Ro0ZGBrvkCvu+lPLzRePG\ngujxN99oC8vs2LFD/xNw91rJAShe6mjRooUxv+C8tidLcSHtatBff/1lzHlWrVpFcrc9MMiLGHDn\n6uTLli0z8jw///wzZ/O1atVqwIABbdq08fT0NDMzKzLTjUOrlSpVatKkiampqUKhMKDkq36423L5\n8uWN2frHFbJq1KghbyFgKOzRo0c8vZZwLO8Rq127dqmdT0r25MkTW1tbIpKrKDwnYfn4+GhfhDt3\n7mzUqJGnpyc/UZH4u8jOzua52p9//inLYF5eGo2mTZs2RDR58mRtpZfdu3freXifPn0kz5A4B9zW\n1tbIW/Fz587xgsGNGzeMOY/Q6bRj5GWMm0tz4MbIIb0meF6l5yzk9u3bnHy6e/dunsseOHBAxsHk\n5eXxPo/FixfreQjfaYwaNSo/P5+LSxhT7K8w3vhcs2ZNuXaqvdo4BmTMrTgXLpPQrImlp6dz9Vtp\nvVIL43c5S0tLY5InPv74Y7nuHF49ycnJLi4uHFc6evSoQqEwqI02Z8NI++ksX76ciDw8PIxcpeN2\ngsY0DNDiYo9GDknbKe6lqe/h4yOIxIULYuxYQSQk7WR/ZqKjo4nI29vb0APVajXXXTEyR0EIce/e\nPXNzcxMTE1lK5Yp/X8NeXl7aG8NvvvmmuPk8Mzc350caX0r+Febn52dotEuLpx/u7u5GTox5y5e0\nzrqF8ZK2vb297rv0yZMnIyMjN2/evH79+vDw8LCwsNDQ0EmTJo0dOzYwMHDgwIHbt2/nR6akpHAb\nMyODXy+Zhg0FkTh+XMyYIYiErBmynEN57tw5aYeHh4cbedHRJr7IUrVDCMEzgQI1x+Pi4mL+6/z5\n87rpL7q3tLx9XJbCJi8fXrf29BQZGeqhQ8+8+eZH775r0Ak4UHPv3j1pz5+ens6pA7///ru0M8je\nPnf27NlE1KhRI8mTNG0+hJF5FcZ4EQPuPNdcsWKFMScJDQ3leiBBQUEFVmmSk5OjoqK2b98eHh4+\nceLEgIAAX19fb29vjs7zO1fjxo2N+yaKxut+a9as4X/+8ccfVatWbdOmzaBBgyZMmLBo0aJt27ad\nPXu2uASKiIgIIrK0tDx//nxZDA903blzh4g8PT0lHJuTk8O5uka2e546daox8YvCsrOzOeVTW4t2\n06ZN2im4qalp+fLla9Wq1bJly27dug0aNGjUqFHTp0/nrWGcyNa4cePXKK+hGNkREUtbt3Z3c0tL\nS+M9p927d9f/cK4HeuzYMWnP3qNHDyIaOnSotMMZb62aMGGCMSdh2o1axmxqTk5O5jRSafc2r6Pc\n3MctWx7o2DFFv23gvJLNBUN4+uLj4yNjJJoTAGvVqqXnOXfv3k06DeiNbGdf2NOnT7WhQFlO+Mrj\ncJIxe4oTExM5rU9alSrje6UWxi/7QYMGaT9z7Ngx7Y4T3apH+/fv5xs/3dtO9EotWfL06ZObNu3U\nqZNarW7WrBkZUmlUrVabmZkpFAppK6yytOS6f/8+t4ySJZNOW+vfmGRhDqB4eXnJ2AeoDGk0wspK\nEInUVNG7tyB6wStxXbx4kYgaNGgg4dj79+9zdoWRZYW5EMQHH3xgzEl0qVQq/l3QNr9ZuXKlvb19\n9erVW7Ro0a1bt4CAgODg4NmzZy9btmzjxo1///33P//8wzN5f39/MrqU/KuKE2ukvTmo1eo333yT\nUwokD4AzbBwcHIwvjKbFBWp0mwbxTKkE2nd1vieV1ujlZZWdLczMhImJyMgQPXsKIvHbbzKevkKF\nCkQkOYNKe9GRvCTDiS+jR4+WdniRBg0aRER9+vTRfoZzKUqgrXarzQaTaz3yZfLkiXB2FkRiyxZx\n7JhQKISFhTDknig3N5c7aUtrzMY4daBq1aoSJiFqtZpDHJMmTZI8gAK0HX214VMhxG+//Rb+r/Xr\n10fo4Cm97uuHt4M/3xb0L2LA/cSJExwr79+/v4Qlmvz8/BEjRhCRiYnJkiVL9D8wLy/v7t2769ev\nVyqVVlZWsuxoLoBrpGqj+V9//XVxbz2WlpY1atTw9fXVvjhiYmL4Vlbe1udQnGvXrhFR7dq1pR1+\n7tw53uZ5+PBhaWdISUnhyb0spWm0Tpw4wYVlTp06JYRITk4+f/78nTt3CuyZyMnJiY+Pv3LlytGj\nR7dv375y5UqeFpRFneWXTFaW8PQURBlr10ZfumRiYmJmZlZk++Li8Ab227dvS3v+W7ducaoy/wQl\n2LBhAxGVL19erq1V3IqkQFjqzp07MSXSbRjLubGdO3eWZTyvBe7jVLeuPo/9559/eAobExMjhMjN\nza1VqxYRff/997KM5enTp7xeos2EKll+fn79+vWJ6JtvvtF+UtsmSJYhjRs3jojat28vy9leB97e\n3kRk5JYX7sMsofWWLL1SC7t//z43dtbuEuP5YQmaNGnCj0Sv1FJcvy7MzYWJScY//0SsXUtEnp6e\n+u/q402ELi4ukp//+PHjhTPpDDJw4EAiGjBggOQxFDkkCwsL7e9RZmZmeHj4smXLQot369YtfnBa\nWhqXyjGyRMCzExcniAQ37m7QQBAJmdrGlpFTp04RUfPmzaUd/vPPPxORg4OD5PzBhIQEKysrhUJh\nTOmtwq5evWppaalQKArXpsjJyXnw4ME///zz999///HHH0uXLp01a1ZwcHBAQEDnzp1lKSX/quLE\nKck/qX/++YcvapLTa3gXo7ybXe7evcsbmrX7+Pv06ePr69u7d+9+/foFBgYGBQVNnDhxzpw53377\nbXh4+C+//ML/A48fP+YDjx8/LuN4XnTXrgkHh/91TK1USRAJo7cFa+Xn53MFWmPCo2fOnOHpvYRs\nlZ07dxKRo6PjkydPJA+gsPj4eA5Yad+OWrVq5f1fDRs2bKpj6dKl/EgZs8FePly8qEsXoVaL5s0F\nkTAwn+DWrVscKzdmFGq1ukmTJkQ0f/58Q4/Vbj3Us1abnn755Rf6b6MdDqSU4Msvv+RHXr58mctd\nXrhwQcYhGepFDLjn5uaGhYXx27q1tXVISIj+aywZGRm8BczS0vKPP/6QNgCuZ6d/YVz95eTkcNSS\ng7C5ubkPHjyIioqKiIgICwubOHGiv79/06ZN3dzceMnBw8ODD8zLy+Prrlw9WKBUZ8+e1V0dkYBz\nAby9vaW973z11VdE5OvrK3kAxeFEwqpVqy5cuHDOnDnjxo0bOnRonz592rdv36BBAw8PD644UUC9\nevV0V4BeX199JYhE48ZCrRa+vjGtW39vSINcbVqfMflrXLOoVatWEn4c2dnZVatWJeNqRxTGJdt0\nM/05cFCCmTNn8iM51mZiYvJcWoe/rBYtEkRCv40O3bp1o/823dq7dy8R2dra6l//oQRjx44log56\nl7bUZnHqJrdKq1dYJG7eqFAoiutXAQWo1Wpzc3Mj35eETirKqlWrDDqQN85rMzRlNGnSJNJJyluz\nZo3/v959913ff3Xs2JFv/LRtY9ErtRQ9eggi8cknIj1dU7nyufbttxqyB/n8+fNEVL9+fWOGwDsY\n3jVwzzXj5B4rKyt52zXxwqE2uS8+Pr7k66DuOx737HnzzTdfmonW4cOCSLRsKYQQtraCSOiso7+A\nDh8+TERt27aVfIa+ffvyzFzaz4in33379pU8gOLMnz+fiBwdHfv16/f22283a9asSpUqNjY2Jb/2\n/Pz8dNMGQRfXvCqhm26pJk+ezO9yEnYTbtmyhYhcXV1lLyHLnRg9PT11z/z06dOEhISYmJiLFy+e\nPn06MjJyx44dERERP/300+LFi0NDQ3n5WZZK3y+fxEShUonx40X37jJ2TH348CERVahQwcjzcEeu\nt956y6CjtA3P5cq80cVvRzVq1DBoBxvvuXd1dX1ehbafp6gooVQKc3MRHS2WLNEWljHoHNwAxvg9\nKHweW1tbg1pHaJsKGN9cpwCNRsO7uLS7bSZMmBD4r/79+/vr4Cm99rrWoUMHMm6nkSwUQohS54LP\nxYMHDyZPnvzrr78KISpXrjx79myexZYgKSmpZ8+ex48fd3Jy2r59e+vWraU9dWRkZNeuXd3d3e/c\nuVNCzXdpRo0atXTpUqVS6enp6e3t7ebm5u7urv2gRo0a3BwgKyvr3r17GRkZvEt37NixCxYs8PT0\nPH/+PBdQg7J27NixNm3avPnmm8eOHZN2hry8vObNm//zzz/BwcE8v9FfWlqal5dXcnLyoUOHeL1X\nRjk5OVWrVjU3N79//35xjzE3N3dycnJ0dNT+3bx5c05Dfq09eEC1alFmJh06RElJ1LcvOTnRjRvk\n7KznCR49elSxYkUXF5fExETJo8jIyKhVq9bDhw9/+eUXjjjob/bs2V9++WWjRo2ioqJMTEwkj6GA\nx48f16pVKyUlZdeuXe+88w4RtWrV6vHjxyUcEhwcPGrUKCJ655139uzZ8/nnn3PZItDLBx/QunUU\nHk6BgSU/8MiRI+3atbOxsYmJiSlfvrz287169dq+fXtAQMDatWuNGcjt27fr1KmjUqlOnz7NFeFK\nlpGRUbNmzfj4+A0bNvTr10/3S2FhYWPGjKlWrdrly5ctLS0lD6l///4RERHGf2uvj7i4uMqVK7u5\nufEdoDFWrVo1dOhQCwsLa2tr/oxSqeR0JyIyMTHhSY7ux0lJSefPn3d2dr5+/bqz3u+lekpPT69R\no8ajR4/Cw8N9fX2JyNLS0srKir+qO05dSUlJtWrVSkpK2rhxI/cChf84cIA6dyZbW7pxg374gebO\npZYt6fhxUij0PMHu3bu7devWtWtXXvyTJiEhoVatWmlpaXv27OGt8XoSQrRt2/bYsWNffvnlzJkz\nJQ+gsEePHtWqVSs1NXX37t1vv/12Wlra+PHjdX8FChs8eHCdOnW0b6QnT57ke8uXwOrVNGQIDRxI\n339PFSuSkxMlJT3vMZXkr7/+6tKli6+vLxfrkCAxMbFevXqPHz9etmxZqdtlCkhOTq5atWp6evqZ\nM2f45k5GGo2mUqVK3LJC9/Pm5ubOzs7Ozs4uLi7Ozs6urq7OOqpVq1a7dm15R/LKsLGxyczMzMjI\nKDIJSR+5ubmNGjWKjo6eO3cuB9/1pNFomjRpcvHixUWLFvE8WUa8eevcuXPOzs4ajSYrKys3N1ef\nA99+++358+fz9sTXyNWr9OuvZGtL1auTv7+MJz5//nyTJk0aNmx44cIFY86jvf+aNWtWy5YtNRpN\namqq7gPUanVaWpruZ1QqVWRk5LZt27y9va9evWphYWHMAApTqVR169a9efOml5eXtbV1dna27lez\ns7NzcnJ0P5OZmZmXl6dUKjUazZIlS7ju1uslOppGjqSWLWn8eKpZk5KSaPNm6tPHoHOsXr16yJAh\nH3zwAaeEG6Nz584HDhxwdna2t7e3sbHRDYfa2dnpRg8cHBw4S/jixYs3btzo1KnT/v37jXz2wo4f\nP96mTRtra+tNmza5uLg4Ojpqv1RgfmVqaqrtSvjbb78NGjSofPny169f54yu5+b5xvtLdfDgwQYN\nGvBQO3XqdPny5eIeGRMTU6NGDSLy8vIyvtomP+lvshbqEkLk5uY2bNhQqVSW8BNxdXVt3Lhxjx49\nPv/889DQ0F9++WX+/PkKhcLMzOz12sb1vPF03MgE8wsXLpibmyuVyoMHD+p/VH5+PjdBat26tTHP\nXpy4uDhO/xwwYMCUKVO++eabFStWbN68+e+//7548SKv9JTF874KBg4URKJ/f5GbK2rUEETi331w\neuKdE9JKiOpavXo1EVWsWDE1NVX/o+Li4jjjyaAXpJ6+++47IqpevbpBGQ07duwgIkdHx7Ko4vUq\nq1btf63qSsOFRLX7CbTu3r3L1TaMfDG89957RDR48GA9H89bf1q0aFE4Q1ClUnELqcKj1d/Jkyc5\ncfXu3buST/K6OXr0KBG15ExVI+Tm5vJ6m6HrJeXLl9d/h4ShZs2apU/yhIWFheO/eL7etWvXMhrS\nyy0/X9SvL4jE11+Le/eEtbVQKISBJc64TuhHH31k5Fi4ipGhHSl+/fVXIqpQoYLs2aPi33KRNWvW\nvH79+q1bt6JKc/To0cjISN7kMWTIENnHU4YePBCbNokjR0RsrOjaVZRB4ra8eL5hUNOdwjj1uFy5\ncoYW4JoyZQoRvf3228Y8e3FOnjxJRDY2NuHh4bt27Tp9+nRsbGxZvLxfE3l5eURkZmZm5HkOHjzI\nZaZ0K0+qVKrk5GTuJBkVFXXs2DHdtqULFizo378/EVWpUkVai4tSffzxx9pVZ2Zvb+/q6urt7V2/\nfv2mTZv6+vp2797d399/6NChI0eOnDhx4owZM7ih12tn9GjBJV+CguQ98a5du8jwzPQijRgxosAP\nVB/Ozs7ayhuy69atmza7Qk8mJib79u0zpsDOSy8v73/txyW9KmbMmEFy9FhOTEysVq2ahLQnW1vb\npQZGRfTXqlUr3Ti7Pngl6eeffy6jIenP1ND/ymesffv258+f//XXX7/44osDBw40btx45MiRs2bN\nKvA7fPr06R49ejx+/LhZs2Z//vknl20xxmefffbJJ598//33AwYMMPJUuiZMmHDx4kVvb+/jx49n\nZmbGxsbGxsY+fPgwPj6eP7h3715iYmJiYiJvtmUKhcLU1HTu3LmtWrWScTBQMl6PlXAB09WwYcPJ\nkyfPmDFjyJAhp06dUqlUT/Xw+PFjU1NThULBe+FlN3/+/Nzc3P79+//2229lcf5X1smTtH49WVlR\naCiFhdHNm+TjQ8OHG3QOTiDlNuLG+PDDD5csWXLmzJmmTZtyiRh93Lx5MyMjo2/fvu3btzdyAIWN\nGjVq2bJlMTExVatWLTJpVJdKpcrIyCAizqwJCQkptXET/L+kJIqNpXLlqG7dkh+4ZcuW48ePu7q6\nBgcHF/iSp6fn+PHjZ8yY8fnnn58/f17bNtwgJ0+e3LRpk5WVlZ5Zog8ePODtPt9++62iUDKsqanp\n4sWL27VrN2/evA8++MDLy0vCkL744gshxNixYz09PSUc/nripEiuBiOZRqMZPHjw7t27XV1dDx48\n6Obmxp/Xza7S/Tg/Pz89PZ2I4uPjhw0bdvjw4fPnzzdu3NiYMRTp0qVLKpXKwcGBNwjm5ORo8610\nP87NzdVN9Pvhhx+6du0q+2BeBcuX06VL5O1No0fThx9SVhYFBNAbbxh0Dq61on2RSDZmzJhly5Zd\nu3atSpUq2glbXl5eZmZmCUepVCoimjdvnjYTSkbBwcFhYWFPnjzhVhl6qlixop2dHXe0fmm4u1Pt\n2v9L/xw2TN70z7LAv+DGbJ8iot69ew8YMGD9+vUfffTR4cOHTUxMsrOztRP4+Pj4hw8fFpjVx8fH\nP3jwQKlUKhQKrioju1mzZhFRcHBwYGmb3kAfKSkpRGR8UmT79u15s12LFi1sbW2zsrLS0tLUanWp\nB7q7u7du3Vr21GMiSkxMjIiIyM7O3rx5c4cOHXS3fMGzlJCQQESllt8s1YULF9a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      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=2000x200 at 0x7F88F804DFD0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0EIVC4efnJ5PJ+N2UtbX13LlzT5w4kZ2dXeiVoaGhHh4ednZ2fFTrzp07qq/J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      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=2000x200 at 0x7F89350391D0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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6n4rPh8kmdEi6dlqlqiorYfn8WUOfvCouLqYLUZh+Jd58eZWcnExH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      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=2000x200 at 0x7F88FEA20A50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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S0uK+unl5eRVfxiWVSrdu3WpiYkJEampqdnZ23OxUbW1tb29vFVyTBfwqkWj4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\nk8m43ThsbGyyFfADZHHcbr2DBim0k3JDwp0xxs6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      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=2000x200 at 0x7F88F804DFD0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=2000x200 at 0x7F89350391D0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for scaffold in scaffolds:\n",
    "    if len(scaffolds[scaffold])>10:\n",
    "        #print(len(scaffolds[scaffold]))\n",
    "        selected = [scaffold]+list(np.array(scaffolds[scaffold])[:,0])\n",
    "        display(Chem.Draw.MolsToGridImage(selected[:10],molsPerRow=10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is the solubility depending on the scaffold? Lets plot distribution of some of the scaffolds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f88f39444d0>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XU4PZgP+qiJgrOixlkwBw5hldeP3N5TuHnXlGdr+tP2d8o7+iLT3HX3gF9xxc6ECtNInR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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = 0\n",
    "x_e = []\n",
    "y_e = []\n",
    "\n",
    "for scaffold in scaffolds:\n",
    "    temp = list(np.array(scaffolds[scaffold])[:,1])\n",
    "    if len(temp)>3:\n",
    "        y_e += temp\n",
    "        x_e += [a]*len(temp)\n",
    "        a+=1\n",
    "           \n",
    "plt.figure()            \n",
    "plt.plot(x_e, y_e,'o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oh, yes. It seems that if we know the scaffold we can estimate fairly what is the solubility of the molecules. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's tain-test split results again and change to fingerprints:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_size = 0.25\n",
    "test_len = int(len(mols)*0.25)\n",
    "\n",
    "size = 0\n",
    "#np.random.seed(10)\n",
    "keys=scaffolds.keys()[:]\n",
    "np.random.shuffle(keys)\n",
    "\n",
    "X_test = []\n",
    "X_train = []\n",
    "\n",
    "y_test = []\n",
    "y_train = []\n",
    "\n",
    "for scaffold in keys:\n",
    "    if size < test_len:\n",
    "        #change to fingerprints:\n",
    "        for mol in list(np.array(scaffolds[scaffold])[:,0]):\n",
    "            fp = AllChem.GetMorganFingerprintAsBitVect(mol,2)\n",
    "            arr = np.zeros((1,))\n",
    "            DataStructs.ConvertToNumpyArray(fp, arr)\n",
    "            X_test.append(arr)            \n",
    "\n",
    "        y_test+=list(np.array(scaffolds[scaffold])[:,1])\n",
    "        size+=len(scaffolds[scaffold])\n",
    "    else:\n",
    "        for mol in list(np.array(scaffolds[scaffold])[:,0]):\n",
    "            fp = AllChem.GetMorganFingerprintAsBitVect(mol,2)\n",
    "            arr = np.zeros((1,))\n",
    "            DataStructs.ConvertToNumpyArray(fp, arr)\n",
    "            X_train.append(arr) \n",
    "            \n",
    "        y_train+=list(np.array(scaffolds[scaffold])[:,1])\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.dummy import DummyRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, -0.618077346144875)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy = DummyRegressor(strategy='mean').fit(X_train,y_train)\n",
    "(dummy.score(X_train, y_train),dummy.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "clf = RandomForestRegressor(n_estimators=100).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9569041619604731, -0.3034373751696722)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(clf.score(X_train, y_train),clf.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This result is far from the ELOS aglorith, but  still little better then the just guessing the mean. For now then the most important part of the prediction is scaffold. Let's see how the scatter plots of both look like. \n",
    "\n",
    "The diffrence between scores for train set and for test set's imply that the model is overfitting. We have to validate model once again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f88f9438fd0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dzt3Kx+tw22C1B+4gOnynLNOI0nhfWd3vqdw+v0LDEGrAJ6KZAC4GsD/M+whN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\nRK5CYII=\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ax1 = plt.subplot(121)\n",
    "plt.scatter(clf.predict(X_train), y_train)\n",
    "plt.scatter(clf.predict(X_test), y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grid Search\n",
    "Firstly let's try to find better results by grid search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "clf = RandomForestRegressor()\n",
    "param_grid = {'n_estimators': [2,8,64,256], 'max_features' : ['log2', 'sqrt','auto']}#, 'max_depth': [None, 4,16,64,256,1024]}\n",
    "# This means that there is 3-fold cross validation:b\n",
    "grid = GridSearchCV(clf, param_grid=param_grid,verbose=100,cv=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 12 candidates, totalling 36 fits\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[CV] max_features=log2, n_estimators=2 ...............................\n",
      "[CV]  max_features=log2, n_estimators=2, score=-3.06675762233, total=   0.0s\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[CV] max_features=log2, n_estimators=2 ...............................\n",
      "[CV]  max_features=log2, n_estimators=2, score=-0.062949897392, total=   0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[CV] max_features=log2, n_estimators=2 ...............................\n",
      "[CV]  max_features=log2, n_estimators=2, score=-0.022103981832, total=   0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.1s remaining:    0.0s\n",
      "[CV] max_features=log2, n_estimators=8 ...............................\n",
      "[CV]  max_features=log2, n_estimators=8, score=-0.819469111157, total=   0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.1s remaining:    0.0s\n",
      "[CV] max_features=log2, n_estimators=8 ...............................\n",
      "[CV]  max_features=log2, n_estimators=8, score=0.298951102988, total=   0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.2s remaining:    0.0s\n",
      "[CV] max_features=log2, n_estimators=8 ...............................\n",
      "[CV]  max_features=log2, n_estimators=8, score=0.385889072818, total=   0.1s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.2s remaining:    0.0s\n",
      "[CV] max_features=log2, n_estimators=64 ..............................\n",
      "[CV]  max_features=log2, n_estimators=64, score=-0.701574580003, total=   0.4s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.6s remaining:    0.0s\n",
      "[CV] max_features=log2, n_estimators=64 ..............................\n",
      "[CV]  max_features=log2, n_estimators=64, score=0.37340492523, total=   0.3s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.9s remaining:    0.0s\n",
      "[CV] max_features=log2, n_estimators=64 ..............................\n",
      "[CV]  max_features=log2, n_estimators=64, score=0.457826833578, total=   0.3s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    1.2s remaining:    0.0s\n",
      "[CV] max_features=log2, n_estimators=256 .............................\n",
      "[CV]  max_features=log2, n_estimators=256, score=-0.600577019696, total=   1.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    2.3s remaining:    0.0s\n",
      "[CV] max_features=log2, n_estimators=256 .............................\n",
      "[CV]  max_features=log2, n_estimators=256, score=0.394155355281, total=   1.5s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    3.8s remaining:    0.0s\n",
      "[CV] max_features=log2, n_estimators=256 .............................\n",
      "[CV]  max_features=log2, n_estimators=256, score=0.471151562504, total=   1.1s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    5.0s remaining:    0.0s\n",
      "[CV] max_features=sqrt, n_estimators=2 ...............................\n",
      "[CV]  max_features=sqrt, n_estimators=2, score=-1.00533562913, total=   0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    5.1s remaining:    0.0s\n",
      "[CV] max_features=sqrt, n_estimators=2 ...............................\n",
      "[CV]  max_features=sqrt, n_estimators=2, score=0.0406095111014, total=   0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    5.1s remaining:    0.0s\n",
      "[CV] max_features=sqrt, n_estimators=2 ...............................\n",
      "[CV]  max_features=sqrt, n_estimators=2, score=-0.0158773239827, total=   0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    5.1s remaining:    0.0s\n",
      "[CV] max_features=sqrt, n_estimators=8 ...............................\n",
      "[CV]  max_features=sqrt, n_estimators=8, score=-0.938080546, total=   0.1s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    5.2s remaining:    0.0s\n",
      "[CV] max_features=sqrt, n_estimators=8 ...............................\n",
      "[CV]  max_features=sqrt, n_estimators=8, score=0.434645291818, total=   0.1s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    5.3s remaining:    0.0s\n",
      "[CV] max_features=sqrt, n_estimators=8 ...............................\n",
      "[CV]  max_features=sqrt, n_estimators=8, score=0.369410577028, total=   0.1s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    5.3s remaining:    0.0s\n",
      "[CV] max_features=sqrt, n_estimators=64 ..............................\n",
      "[CV]  max_features=sqrt, n_estimators=64, score=-0.780209525199, total=   0.4s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    5.8s remaining:    0.0s\n",
      "[CV] max_features=sqrt, n_estimators=64 ..............................\n",
      "[CV]  max_features=sqrt, n_estimators=64, score=0.41653030862, total=   0.4s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    6.1s remaining:    0.0s\n",
      "[CV] max_features=sqrt, n_estimators=64 ..............................\n",
      "[CV]  max_features=sqrt, n_estimators=64, score=0.470641920178, total=   0.4s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    6.5s remaining:    0.0s\n",
      "[CV] max_features=sqrt, n_estimators=256 .............................\n",
      "[CV]  max_features=sqrt, n_estimators=256, score=-0.684628469502, total=   1.3s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    7.8s remaining:    0.0s\n",
      "[CV] max_features=sqrt, n_estimators=256 .............................\n",
      "[CV]  max_features=sqrt, n_estimators=256, score=0.417674391182, total=   1.8s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    9.7s remaining:    0.0s\n",
      "[CV] max_features=sqrt, n_estimators=256 .............................\n",
      "[CV]  max_features=sqrt, n_estimators=256, score=0.47707538837, total=   1.3s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:   11.0s remaining:    0.0s\n",
      "[CV] max_features=auto, n_estimators=2 ...............................\n",
      "[CV]  max_features=auto, n_estimators=2, score=-1.31320401784, total=   0.1s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:   11.1s remaining:    0.0s\n",
      "[CV] max_features=auto, n_estimators=2 ...............................\n",
      "[CV]  max_features=auto, n_estimators=2, score=0.187457761207, total=   0.1s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:   11.2s remaining:    0.0s\n",
      "[CV] max_features=auto, n_estimators=2 ...............................\n",
      "[CV]  max_features=auto, n_estimators=2, score=0.231385689378, total=   0.1s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:   11.2s remaining:    0.0s\n",
      "[CV] max_features=auto, n_estimators=8 ...............................\n",
      "[CV]  max_features=auto, n_estimators=8, score=-0.94232330185, total=   0.3s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:   11.5s remaining:    0.0s\n",
      "[CV] max_features=auto, n_estimators=8 ...............................\n",
      "[CV]  max_features=auto, n_estimators=8, score=0.368751181232, total=   0.3s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:   11.8s remaining:    0.0s\n",
      "[CV] max_features=auto, n_estimators=8 ...............................\n",
      "[CV]  max_features=auto, n_estimators=8, score=0.452430325099, total=   0.3s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:   12.1s remaining:    0.0s\n",
      "[CV] max_features=auto, n_estimators=64 ..............................\n",
      "[CV]  max_features=auto, n_estimators=64, score=-0.949519957984, total=   2.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:   14.1s remaining:    0.0s\n",
      "[CV] max_features=auto, n_estimators=64 ..............................\n",
      "[CV]  max_features=auto, n_estimators=64, score=0.386078054353, total=   3.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:   17.1s remaining:    0.0s\n",
      "[CV] max_features=auto, n_estimators=64 ..............................\n",
      "[CV]  max_features=auto, n_estimators=64, score=0.461873264869, total=   2.9s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:   20.0s remaining:    0.0s\n",
      "[CV] max_features=auto, n_estimators=256 .............................\n",
      "[CV]  max_features=auto, n_estimators=256, score=-0.823082732827, total=  26.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:   46.2s remaining:    0.0s\n",
      "[CV] max_features=auto, n_estimators=256 .............................\n",
      "[CV]  max_features=auto, n_estimators=256, score=0.38981237397, total=  33.9s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:  1.3min remaining:    0.0s\n",
      "[CV] max_features=auto, n_estimators=256 .............................\n",
      "[CV]  max_features=auto, n_estimators=256, score=0.466077728445, total=  26.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:  1.8min remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:  1.8min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score='raise-deprecating',\n",
       "       estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=None,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'n_estimators': [2, 8, 64, 256], 'max_features': ['log2', 'sqrt', 'auto']},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=100)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='log2', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=256, n_jobs=None,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.08824329936299721,\n",
       " -0.2768230089028709,\n",
       " {'max_features': 'log2', 'n_estimators': 256})"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " (grid.best_score_ , grid.score(X_test, y_test), grid.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is definitly an improvement, but a lot can be still done."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grid search with custom cross validation splitter\n",
    "Let's firstly create simple splitter. All scaffolds has their own category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test_size = 0.25\n",
    "test_len = int(len(mols)*0.25)\n",
    "\n",
    "size = 0\n",
    "np.random.seed(10)\n",
    "keys=scaffolds.keys()[:]\n",
    "np.random.shuffle(keys)\n",
    "\n",
    "X_test = []\n",
    "X_train = []\n",
    "\n",
    "y_test = []\n",
    "y_train = []\n",
    "\n",
    "groups = []\n",
    "group = 0\n",
    "\n",
    "for scaffold in keys:\n",
    "    if size < test_len:\n",
    "        \n",
    "        for mol in list(np.array(scaffolds[scaffold])[:,0]):\n",
    "            fp = AllChem.GetMorganFingerprintAsBitVect(mol,2)\n",
    "            arr = np.zeros((1,))\n",
    "            DataStructs.ConvertToNumpyArray(fp, arr)\n",
    "            X_test.append(arr)            \n",
    "\n",
    "        y_test+=list(np.array(scaffolds[scaffold])[:,1])\n",
    "        size+=len(scaffolds[scaffold])\n",
    "    else:\n",
    "        for mol in list(np.array(scaffolds[scaffold])[:,0]):\n",
    "            fp = AllChem.GetMorganFingerprintAsBitVect(mol,2)\n",
    "            arr = np.zeros((1,))\n",
    "            DataStructs.ConvertToNumpyArray(fp, arr)\n",
    "            X_train.append(arr) \n",
    "            \n",
    "        y_train+=list(np.array(scaffolds[scaffold])[:,1])\n",
    "        \n",
    "        groups+=[group]*len(scaffolds[scaffold])\n",
    "        group+=1\n",
    "        \n",
    "X_train = np.array(X_train)\n",
    "X_test = np.array(X_test)\n",
    "\n",
    "y_train = np.array(y_train)\n",
    "y_test = np.array(y_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import LeaveOneGroupOut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "logo = LeaveOneGroupOut().split(X_train, y_train, groups=groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 1188 out of 1188 | elapsed: 23.2min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 132 folds for each of 9 candidates, totalling 1188 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/piskorz/Programs/miniconda2/lib/python2.7/site-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=<generator object split at 0x7f88fc3132d0>,\n",
       "       error_score='raise-deprecating',\n",
       "       estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=None,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'n_estimators': [2, 8, 64], 'max_features': ['log2', 'sqrt', 'auto']},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=1)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = RandomForestRegressor()\n",
    "param_grid = {'n_estimators': [2,8,64], 'max_features' : ['log2', 'sqrt','auto']}#, 'max_depth': [None, 4,16,64,256,1024]}\n",
    "# This means that there is 3-fold cross validation:b\n",
    "grid = GridSearchCV(clf, param_grid=param_grid,verbose=1,cv=logo)\n",
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-4.208869963453095,\n",
       " -0.14500145102355355,\n",
       " {'max_features': 'log2', 'n_estimators': 8})"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(grid.best_score_ , grid.score(X_test, y_test), grid.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This low score probably is a result that now the esmiator is biased towords smaller chemical groups. Single tree for the train test with single element (single molecule with particular scaffold) now is as important as tree with many elements (many molecules with the same scaffold)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f88f9468110>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4XlWOx/QdytfJipGse+cLXrFnkKzE8zYLdpmjOWx4cOqUNn1dUmn12Hxj3804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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(grid.predict(X_test), y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grid Search with custom cateogry splitter (but categories with only one element are grouped)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_size = 0.25\n",
    "test_len = int(len(mols)*0.25)\n",
    "\n",
    "size = 0\n",
    "np.random.seed(10)\n",
    "keys=scaffolds.keys()[:]\n",
    "np.random.shuffle(keys)\n",
    "\n",
    "X_test = []\n",
    "X_train = []\n",
    "\n",
    "y_test = []\n",
    "y_train = []\n",
    "\n",
    "groups = []\n",
    "group = 1\n",
    "\n",
    "for scaffold in keys:\n",
    "    if size < test_len:\n",
    "        \n",
    "        for mol in list(np.array(scaffolds[scaffold])[:,0]):\n",
    "            fp = AllChem.GetMorganFingerprintAsBitVect(mol,2)\n",
    "            arr = np.zeros((1,))\n",
    "            DataStructs.ConvertToNumpyArray(fp, arr)\n",
    "            X_test.append(arr)            \n",
    "\n",
    "        y_test+=list(np.array(scaffolds[scaffold])[:,1])\n",
    "        size+=len(scaffolds[scaffold])\n",
    "    else:\n",
    "        if len(scaffolds[scaffold])>1:\n",
    "            for mol in list(np.array(scaffolds[scaffold])[:,0]):\n",
    "                fp = AllChem.GetMorganFingerprintAsBitVect(mol,2)\n",
    "                arr = np.zeros((1,))\n",
    "                DataStructs.ConvertToNumpyArray(fp, arr)\n",
    "                X_train.append(arr) \n",
    "\n",
    "            y_train+=list(np.array(scaffolds[scaffold])[:,1])\n",
    "\n",
    "            groups+=[group]*len(scaffolds[scaffold])\n",
    "            group+=1\n",
    "        else:\n",
    "            for mol in list(np.array(scaffolds[scaffold])[:,0]):\n",
    "                fp = AllChem.GetMorganFingerprintAsBitVect(mol,2)\n",
    "                arr = np.zeros((1,))\n",
    "                DataStructs.ConvertToNumpyArray(fp, arr)\n",
    "                X_train.append(arr) \n",
    "\n",
    "            y_train+=list(np.array(scaffolds[scaffold])[:,1])\n",
    "            groups+=[0]\n",
    "            \n",
    "X_train = np.array(X_train)\n",
    "X_test = np.array(X_test)\n",
    "\n",
    "y_train = np.array(y_train)\n",
    "y_test = np.array(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "logo = LeaveOneGroupOut().split(X_train, y_train, groups=groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 333 out of 333 | elapsed:  2.3min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 37 folds for each of 9 candidates, totalling 333 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/piskorz/Programs/miniconda2/lib/python2.7/site-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=<generator object split at 0x7f88f93d9d20>,\n",
       "       error_score='raise-deprecating',\n",
       "       estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=None,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'n_estimators': [2, 8, 64], 'max_features': ['log2', 'sqrt', 'auto']},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=1)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = RandomForestRegressor()\n",
    "param_grid = {'n_estimators': [2,8,64], 'max_features' : ['log2', 'sqrt','auto']}#, 'max_depth': [None, 4,16,64,256,1024]}\n",
    "# This means that there is 3-fold cross validation:b\n",
    "grid = GridSearchCV(clf, param_grid=param_grid,verbose=1,cv=logo)\n",
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-5.634468953689906,\n",
       " 0.19008956874653116,\n",
       " {'max_features': 'log2', 'n_estimators': 64})"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(grid.best_score_ , grid.score(X_test, y_test), grid.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Definitly improvemnt. Probably categories has to be splitted more evenly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Neural network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "clf = MLPRegressor(hidden_layer_sizes=[256,64,16,8], max_iter=1000).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9986834294469394, 0.4688390569976115)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(clf.score(X_train, y_train), clf.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That was simple improvement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding QSAR methodology"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from mordred import Calculator, descriptors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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