model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "    PubchemFP0  PubchemFP1  PubchemFP2  PubchemFP3  PubchemFP4  PubchemFP5  \\\n",
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       "    PubchemFP873  PubchemFP874  PubchemFP875  PubchemFP876  PubchemFP877  \\\n",
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       "0              0             0             0  4.522879  \n",
       "1              0             0             0  3.522879  \n",
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       "..           ...           ...           ...       ...  \n",
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       "\n",
       "[79 rows x 882 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"ERBB_bioactivity_data_pubchem.csv\")\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 881 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    PubchemFP0  PubchemFP1  PubchemFP2  PubchemFP3  PubchemFP4  PubchemFP5  \\\n",
       "0            1           1           0           0           0           0   \n",
       "1            1           1           0           0           0           0   \n",
       "2            1           1           1           0           0           0   \n",
       "3            1           1           0           0           0           0   \n",
       "4            1           1           1           0           0           0   \n",
       "..         ...         ...         ...         ...         ...         ...   \n",
       "74           1           0           0           0           0           0   \n",
       "75           1           1           1           1           0           0   \n",
       "76           1           1           1           1           0           0   \n",
       "77           1           1           1           1           0           0   \n",
       "78           1           1           1           1           0           0   \n",
       "\n",
       "    PubchemFP6  PubchemFP7  PubchemFP8  PubchemFP9  ...  PubchemFP871  \\\n",
       "0            0           0           0           1  ...             0   \n",
       "1            0           0           0           1  ...             0   \n",
       "2            0           0           0           1  ...             0   \n",
       "3            0           0           0           1  ...             0   \n",
       "4            0           0           0           1  ...             0   \n",
       "..         ...         ...         ...         ...  ...           ...   \n",
       "74           0           0           0           1  ...             0   \n",
       "75           0           0           0           1  ...             0   \n",
       "76           0           0           0           1  ...             0   \n",
       "77           0           0           0           1  ...             0   \n",
       "78           0           0           0           1  ...             0   \n",
       "\n",
       "    PubchemFP872  PubchemFP873  PubchemFP874  PubchemFP875  PubchemFP876  \\\n",
       "0              0             0             0             0             0   \n",
       "1              0             0             0             0             0   \n",
       "2              0             0             0             0             0   \n",
       "3              0             0             0             0             0   \n",
       "4              0             0             0             0             0   \n",
       "..           ...           ...           ...           ...           ...   \n",
       "74             0             0             0             0             0   \n",
       "75             0             0             0             0             0   \n",
       "76             0             0             0             0             0   \n",
       "77             0             0             0             0             0   \n",
       "78             0             0             0             0             0   \n",
       "\n",
       "    PubchemFP877  PubchemFP878  PubchemFP879  PubchemFP880  \n",
       "0              0             0             0             0  \n",
       "1              0             0             0             0  \n",
       "2              0             0             0             0  \n",
       "3              0             0             0             0  \n",
       "4              0             0             0             0  \n",
       "..           ...           ...           ...           ...  \n",
       "74             0             0             0             0  \n",
       "75             0             0             0             0  \n",
       "76             0             0             0             0  \n",
       "77             0             0             0             0  \n",
       "78             0             0             0             0  \n",
       "\n",
       "[79 rows x 881 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop('pAC50', axis=1)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     4.522879\n",
       "1     3.522879\n",
       "2     4.522879\n",
       "3     4.522879\n",
       "4     4.522879\n",
       "        ...   \n",
       "74    4.522879\n",
       "75    4.522879\n",
       "76    4.522879\n",
       "77    4.522879\n",
       "78    4.522879\n",
       "Name: pAC50, Length: 79, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = df.pAC50\n",
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(79, 881)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(79,)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Removing Large Variance Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(79, 204)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import VarianceThreshold\n",
    "selection = VarianceThreshold(threshold=(.8 * (1 - .8)))    \n",
    "X = selection.fit_transform(X)\n",
    "\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((63, 204), (63,))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, Y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((16, 204), (16,))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape, Y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.17643911443794713"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestRegressor(n_estimators=100)\n",
    "model.fit(X_train, Y_train)\n",
    "r2 = model.score(X_test, Y_test)\n",
    "r2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scatter Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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x48aKFElEVFDuUM02M1SpeHkVrH369AHgarE+/fTT2LlzZ55JIkwmE7Zv345+/frhypUrAIC+ffsqXC4R0Z05nRLSjVaYLQ4wU6m4eTWlYceOHdGlSxfs2LEDFy5cwJgxYyAIAsLDwxEQEIDs7GxkZmZ6vKZ9+/bo2rVrkRRNRHQrolNChtECi41rqZJ/eD3z0uzZs/HEE0/I41SdTifS0tJw7do1ZGZmytslSUKnTp3w6aefFmXdRER5iE4JGQaGKvmX15Pw63Q6fPLJJ3jyySexZs0aHD58GGlpafL+kJAQPPDAA3jmmWfQsWPHIimWiOhWRNGJdKMVVjtDlfyrwKvbtG7dGq1btwYAZGVlwWQyITAwkD2AichvHKKTC5RTiVHg1W1yCg4ORlRUVL6heu7cOXz88ceFOTwR0R3ZHQxVKlkKtR5rbllZWdi2bRsSEhJw7NgxAMh3+kMiIiXY7CIyjFbYRYYqlRyKBOuhQ4eQkJCAHTt2wGKxAHCNIRMEQYnDExHlYbWLyDBa4BA5oIZKFp+D9fr161i/fj02bNggj1uVcozCrlixIh577LHCV0hElIvV5uAC5VRiFShYbTYbdu3ahYSEBBw4cABOp+vyiztQBUFAs2bNMGLECLRt21Ze5YaISCkWmwMZDFUqwbxKvuPHjyMhIQFbt26FwWAA4BmmDzzwABITEwEAbdq04XAbIioSZqsdmSYbQ5VKNK+CtW/fvhAEweNS77333ovu3buje/fuqFq1Kho0aFBkRRIRZVtcocoFyqmkK/C12ipVquD9999HmzZtiqIeIqI8ssw2GLLsDFUqFbwex+ru5Xvjxg2MGDECQ4cORUJCgrxWKxFRUTBl22DIYkuVSg+vgjU+Ph5PPvkkgoODIUkSRFHE/v37MXnyZDz88MMYPXp0UddJROWQMcsGQ7YNvKVKpYlXwXr//ffj3Xffxb59+zB79my0adNGvudqs9mwe/dueczqb7/9hn379sk9homIfJFpssJotnEtVSp1BEny7dc2KSkJmzZtwqZNm/DPP/+4DpZjQoiKFSsiLi4OM2bMUKbSItKpUycAwO7du/1cCRG5ZZqsyLKUrwXKtRoVqoQH+buMckvJLPB5ruCoqCi8+OKL2Lp1K9atW4fnnnsOYWFh8tJx6enpWLduXaELJKLyQ5IkpBst5S5UqWwp1CT8bk2aNME777yDX3/9FZ999hk6duzIySGIqEBcoWqF2eJgqFKppmj6abVaPProo3j00UeRmpqK7777TsnDE1EZ5XS6QtVqc4CZSqVdoYLVYDBg165dOH36NLKzsxEaGooGDRqgU6dOiIyMxODBg5Wqk4jKKNEpIcNogcXGBcqpbPA5WNevX493330XZrM5z77Q0FBMmzYNjz/+eKGKI6KyTRSdyDBaYbEzVKns8ClYd+3ahbfffjvPNIduBoMBb7zxBkJCQtCuXbtCF0lEZY8oOl2XfxmqVMb4FKxffPGF/HWvXr3QtWtXREREIC0tDTt37sT69evhdDoxf/58BisR5WF3OJFhtMDm4Hh3Knt8CtZ//vkHgiDgqaeeyjNOtUOHDggICMDq1atx6tQpRYokorLD7hCRYbQyVKnM8mm4TeXKlQG4ZmTKT9OmTQEAlSpV8rEsIiqLbHYR6QaGKpVtPgXrM888A0mSsHfv3nz3//DDDxAEAYMGDSpMbURUhljtItKMFthFhiqVbT5dCu7atSsOHDiAH374AVlZWejatSsqVKiA5ORkbN68GUePHkWDBg1QvXr1PNNDuaeNIqLyw2pzIN1o5QLlVC74NFdww4YNfTuZIODEiRM+vbaocK5goqJlsTmQwVC9I84V7F9KZoFPLVYf5+0nonLGbLUjw2SDk6FK5YhPwcr1V4noTrIsdhhMXKCcyh8GKxEpLstsgyGLC5RT+cQlaIhIUaZsG4zZDFUqvxisRKQYY5YNRrONy75RucZgJSJFZJqsXKCcCAxWIiokSZKQmWVDNkOVCACDlYgKQZIkZJisMFu4QDmRG4OViHwiSRLSjVZYrAxVopy8CtbCzETBKQyJyh6n82ao2hz+LoWoxPEqWEeNGgVBEAp88JI4hSERFY7olJBhsMDCBcqJ8uX1pWBOY0hEouhEutEKK0OV6Ja8Ctb8Zlr64YcfcPbsWVSqVAnt27dHZGQkUlNTsXfvXqSlpaF9+/bo16+f4gUTkX84RCfSDRaupUp0Bz4F6/bt2zF//nw0b94cK1asQEBAgLwvOzsbgwYNwk8//YS+ffsqW20OGzduxFdffYXLly+jZs2aGD16NB577LEiOx9ReWZ3OJFhZKgSecOnhc4XLFgAQRDw9NNPe4QqAAQFBeG5556DJElYuHChIkXmtmnTJkyaNAn9+/fH1q1b0a1bN7z++us4cuRIkZyPqDyzO0S2VIkKwKdgvXDhAgAgKSkp3/2pqakAgH/++ce3qm5DkiR89tlnGDRoEPr374+aNWvi5ZdfxsMPP4xDhw4pfj6i8sxmF5FmsMIuMlSJvOXTONbo6GhcunQJX331FapXr44uXbogICAAFosFO3bswKJFiyAIAqKjo5WuF+fPn8fVq1fRvXt3j+1LlixR/FxE5ZnV5kC6yQpRZMdFooLwqcXaq1cvAIDFYsH48ePRvHlzxMTEICYmBhMmTEBWVhYA4LnnnlOsULfz588DcN3LHTZsGFq3bo2nnnoKe/bsUfxcROWV1eZAupGhSuQLn4L1xRdfRKdOnSBJkvzHbDbLXwNAv379MHjwYEWLBQCTyQQAmDBhArp164alS5fikUcewciRI7F//37Fz0dU3pitdleoct03Ip/4dClYo9FgwYIF2Llzpzzsxmw2IyQkBA0bNkSvXr3QsmVLpWsFAGi1WgDAsGHD0Lt3bwBAw4YNceLECSxbtgytW7cukvMSlQdmqx0ZRhucHLdO5LNCzRUcFxeHuLg4pWrxSlRUFACgXr16Htvr1q2LvXv3FmstRGVJlsUOg4mhSlRYhZ6E/8qVK/jjjz+QnJyMDh06IDw8HHq9Ps8wHKU0btwYwcHBOHbsGB544AF5++nTp1GzZs0iOSdRWWcy22DMsoFXf4kKz+dgvXz5MqZOnepxX7NGjRpIT0/HnDlzMHPmTMTGxipSZE56vR7Dhw/HggULEBUVhaZNm2Lr1q3Yt28fli9frvj5iMo6Y5YNJjNDlUgpPgVrUlISnn32WaSmpsqdldyT9J85cwapqakYM2YMVq1ahebNmytWrNvIkSMRGBiIOXPmICkpCffccw/mzZuHBx98UPFzEZVlBpMNJouNC5QTKcinXsHz589HSkoKgoODMXHiRI99derUgU6ngyiKWLRokSJF5mfIkCHYvXs3/vrrL2zatAmdO3cusnMRlUWZJitDlagI+BSsP//8MwRBwPDhw/MMqenXrx9Gjx4NSZLw119/KVIkESlHkiRkmKzIstgZqkRFwKdLwWlpaQBc91TzU6VKFQCA0Wj0sSwiKgruUDVbHGCmEhUNn1qs7uD87bff8uwTRREbNmwAgCKZ0pCIfON0Skg3MlSJippPwdq1a1dIkoT169dj6NCh8vb4+Hh0794dhw4dgiAIvO9JVEK4QtUCs5WhSlTUfArWkSNHol69epAkCfv375d7BO/fvx/nz5+HJEmoVasWXnrpJUWLJaKCE50S0g0WWGyiv0shKhd8Ctbg4GB88803GDBgAIKCgjzmDA4ICEDfvn2xevVqhIaGKl0vERWAKDpdoWpnqBIVF58niAgODsbkyZMxceJEnD9/HpmZmQgODsbdd98NnU6nZI1E5APHzVDlAuVExcunYHWPXR00aBAaNmyIunXreuzftWsXvvnmG9SpUweTJ08ufJVEVCB2hxMZRoYqkT/4dCl4w4YN2LhxI/7999989xsMBuzbtw9bt24tVHFEVHB2h8iWKpEfedViXbZsGTZv3pxn+6xZszB//nyPbZIk4dKlSwAgL3hORMXDaheRYbTAwQXKifzGq2Dt2bMnvvjiC3mRcXcv4KtXr972dY0aNSpkeUTkLavNgXSTFSJDlcivvLoUHBERIU9TKOWYAy1nb+Cc+/R6Pe6//35Mnz69aKomIg8WmwPpRoYqUUngdeelwYMHy/MCN2jQAIIgYP78+ZwEgsjPzFY7Mk02iFz3jahE8KlX8Ndffw0AqFevnqLFEFHBZFtcoerkbPpEJYZPvYJbtWqFxo0bY9WqVfj222899vXt2xeffvopzGazIgUSlXSSJCE104x/U0xIzTR73C4pSllmG0OVqATyqcWamZmJ559/Hn///Te6dOmCp59+GoBrmM3x48dx4sQJ/PLLL1i5ciVCQkIULZioJLmemoU/z6bg35Qs2O0itFo1qlUKRpO6lRAdGVxk5zVl22DMtoFXf4lKHp9arIsXL8bJkychSRIuX74sb09LS0OlSpUgSRL+/vtvLFu2TLFCiUqa66lZ2Pv7FZy/lonQIC2qVg5BaJAW568ZsPf3K7ieWjTDzYxZNhgYqkQllk/BumvXLgiCgDZt2mDNmjXy9tq1a+PHH39Eu3btIEkSduzYoVihRCWJJEn482wKDFlW1KgSiiC9FmqVgCC9FjWqhMCQZcOfZ1MUvyxsMNlgNNu4QDlRCeZTsF67dg0A0KtXrzzzAmu1WnTv3h0AcOXKlUKWR1QypRks+DclC5UqBMrjut0EQUClCnr8m5KFNINFsXNmmqwwWRiqRCWdT8EaEBAAALhw4UK++8+ePQvAFbJEZZHVLsJuFxGgy7+bQoBOA7tDhFWBVWUkybWWapbFzlAlKgV86rzUrFkz/Pzzz1i8eDEqVqyINm3aIDw8HBkZGfjpp5+wdOlSCIKApk2bKl0vUYkQoFVDq1XDanMgSJ/3A6TV5oBWo0aAVl2o80iShAyTFWYLFygnKi18Ctbhw4fj119/hdVqxXvvvZdnvyRJUKlUGD58eKELJCqJIsL0qFYpGOevGVAjQONxOViSJKRkWlCnahgiwvQ+n8PplJButMJqY6gSlSY+j2OdPn06NBpNvtMaarVaTJ06FQ899JDS9RKVCIIgoEndSggL1uFSkhEpGdlIM1iQkpGNS0lGhAXr0KRupTz3X70lOl2Xfy0MVaJSx+eFzp966im0bdsWmzZtwvHjx2E0GhEaGopGjRqhR48eqFatmpJ1EpU40ZHBaHx3JHYfuoQzlzNgdzih1ahQvXIIHm5azedxrKJTQobBAosC92eJqPj5HKwAEB0djREjRihVC1Gpcj01C8fPpSJIr0HzeytDpRLgdErIttpx/FwqKlcMLHC4iqLTdfmXoUpUahUqWInKK49xrFGhue6x6nE52YQ/z6YgKiLI68vBDtHJBcqJygCvgrVly5ZQqVT46KOP0L59e7Rq1cqrgwuCgIMHDxaqQKKSqCDjWCMrBN7xeHaHExlGhipRWeBVsBqNRgiCAIfDAcA1J7AgCHecVcbXjhtEJd1/41jzD80AnQZpBotXl3RtdhEZRivsIkOVqCzwKljdHZECAwM9HhOVV0qNY7XaRaQbLVygnKgM8SpY9+zZc9vHROWNEuNYrTYH0o1WLlBOVMb4NI6VqLzLOY71crIJ2RY7RKeEbIsdl5NNdxzHamGoEpVZXrVYv/76a59PMGjQIJ9fS1SSRUcGo0OL6jh6OhnHzqQgy2xHcKAWze6thOb1qtxyqE2WxQ5jlo2hSlRGeRWs77//vk8dkQRBYLBSmXbqUjp2H76E66nZcIhOaNQqpGRkI1CvzROsTqcEQ7YN2ZxMn6hM83ocq9LrShIpzWy1QyUIgCBAJQhQqQSohKLrnf7TkStYue0ksi2ulqpWo4Ld4cTVG1lYue0kAKB9THUArk5KBpOVw2mIygGvgnXWrFl5ti1duhRnzpxBw4YNERcXh8jISKSmpuKHH37A6dOn0aFDB4wbN07xgoluJdNkg/PmB0DB/V/X/6BSCRAEV9DKX0OASgUIN8PXFcSe/38roihi54ELsDtEVAjWwSFKsFhFCJAQEqiBxSZi98GLePi+aFhsTpjMdrk2IirbvArW3r17ezz+5ptvcObMGbRv3x5ffvmlx76XX34Zw4cPx08//YTY2Fjce++9ylVLdBsSIF9ildz/vfn4dvczhVxfCDkC2R3G7uAVBEAlCDh1MQ3ZVhERYXqoBAFqtSuMnZIE0SkhOFCCyWzHgePXcM9d4ZxIn6gc8alX8PLlyyEIAh577LE8+wRBQI8ePSBJElasWFHoAonuRJIkpBnMSM0wI8NoKfBtCwn/hbIkAU5JgtPpCkiH6ITN4YTVLsJsdSDb4oDJbMfF6wZcTjLgeloWUjLNSE4343paFlIzLcgwWnEjw4yLSUZcT81mqBKVMz7NFXzt2jUAwJkzZ/Ldf+7cOQDAlStXfCyLyDvXU7Pw27F/8ec/qci22OCUgAohOlSrFILwMD00ahU0GhU0asH1tVoFrUYFtVqA9uZj1373H8Gre7JqlQCnBDidgNUmQhQlSHC1ctVqQQ5T9W0uJxNR2eRTsNasWRNnz57FihUrEBoairi4OISHhyM9PR07duzAkiVLIAgCatWqpXS9dAeSJJWbqSSvp2Zh7+9XsPGnf5Bltufam+TzcdUqwSNstWrXY7XKFcJatQDRKUEUJdjsDqhc14zlS8o2hwSnBGg1KlxOMsGYfenm6/KGvPtr7c3zqdXu5wn/fa1W3fZ+L5VukiQh02SFSiVAoxYQHqovN/+GyyqfgvXZZ5/FzJkzIYoiPvvsM3z22Wce+91v7sOHD1ekSPLe7f5BFlXo+iPMc64u41R4PKjolCDaRFjh3dJtzpw3d3OwO5zYdfiyIjW533T/C2RXKGs1amjUOUL4ZnDnfpzzdVrNf6/3fF6u5+bT0s95bncnMPLdjfRsnL6UjuR0M4ICNAgO1CIiTI8mdSv5vJ4v+Z9Pwdq/f39cvHjxlhNHaDQavPrqq+jRo0ehiiNlFdWbYFEd132vNL/j51xd5vGHa+PUxXSYzHbYHaLr3qjDCYcoIThQI7cuHaITDtEJUZRgF51wOJyl5v6n0ynB5pRgs5ec4ToC4BnIGhU0cqtedbOVL3gEsnwJPldI53mcO8jz+YDg3qbNcWxTtg12hwSdVoUKIQElOvhvpGfj0IkkmLJtCA8NQESFQGhUAs5fMyDVYEGHFtUZrqWUz+uxvv322+jduzd++OEHnDlzBmazGaGhoWjQoAG6d++OGjVqKFknlUO3e1PMubpMtF6LWtFhMGTZYLa6VmASnRJupGehfYsaqBIRlO8xpJudlOyiK4RF0Qm7wwnRKcHuEOEQ3QHt/vNfOKcZLPj59yswmXNO9uC6yypBggABAVoV6txVARq1Cg4xxzHdx3PkOKb7vKJT8RZ4UZHgapXbS9jYXOHmkCqNSoUAnRo6rfq/AM51SV9z29b6rVvx+X44yLUt5yV9Va7fZUmScPpSOkzZNlSNDIYgCFCrgMAADWpUCfFpPV8qOQq10HnDhg3RsGFDpWoh8tqdVpex2R3QaNTQaW/d8V24OUxGrS545/gL1zLw27F/EajXwCk6YRclSE4BggrQqlVQqVUI0KjRvW0d1K5asUDHdjr/C1/7zWD/rxXu3pYjoN2h73DKLXFXy9xVl3jzOTaH8+bXucPd9TzPDxF5Q7+0kCRAFCWIoujVsn3FQa0SkLN1rhIE2Owi1O7WtkpAcKAGD91XFZEVAgu8ni+VLIUKVgA4dOgQjh49iuTkZDz55JMIDw8HAERFRRW6OKJbyb26TE6SJCHdaEX1KiGoEBJQJOe3O/4LHbVKgF6nhkpwtVWdzpvbBQF2R8EDSaUSoFO5WlolhVO6GdAOV6vaHdb23C1v96X2HB8CROctPgzk3pbjsft18oeFm9vFXOcsLUSnBNEpwpq7j10ONzIAU7Ydz8TVL9B6vlTy+Bysx44dw4QJE3Dx4kV5W+vWrXHkyBHMmjULb731Fp577jlFiiTKzb26TKrBgsvJJlSvHAzRKcFstSPdaEVIkA71aoYX2WU0tUqF8NAAiKITWq0GWs1/5xGdrpmZtFoNQgN10OvUefo2SYDc4SnnrpzPk9x7bpUfAnLOgXH75+ba4X4k3PIZntSCALVKDV3eiwPey+97UJjDSRJSM8348X9XoNe6PoiITtflffc4ZLPVjmyrA/fdHYkAnSZPS9yeI9RdtwDyXgXIfW/e7sgR8Pm8tjATbLk/CHq7ni+VTD4F6z///IMhQ4bAbDbn6WBy9uxZ2Gw2zJw5E9WrV0e7du2Uq5YoB/fqMn+eTUG60YpMkwVWuxPVq4SgXs1wVA7P/95qYek0KlSuqEeATo1si4Qsix1WqwOiU4Ig4OY9PRUkSUDFUF2xXMqTJCnXrFPwSOn/Oi5Lt33j9wxpyXXDMoc8QSzlH9j5TdIhSZ613SzH88OElOMDhUft0n/7b/5dJbi+12GBOkSGu2bAks+RY8atlIws1KsZgUoVA+Uj5rwtrmS7V5IkOCUJDkf+neXcrXy7Q8SfZ1OQnG5GhWAdnBIQEqhBvRrhXq/nSyWXT8G6YMECZGdno1KlSpg2bRpGjx4t73vwwQfx3XffITMzE0uWLGGwUpGKjgxGVEQQ0o0W/HsjC4KAIusNKgAI0KlRMSQANocIu+jEjQwLHA4nVGpBDiKzRYRao0K1yhroAwp9t8W72gQhdwbCMwbLJrUKCA7SQqNSIVCvwc0JKV0EINtsR8VQPapVDkbFUD0gucYYuwPQ6ZRudmJzXe52h3jOlu9/QX3nQBYEwdW61wEBuH1rs1KFwDy9gkWnhKspWXdcz5dKNp/+1R84cEAep9q5c2ePfV26dEFSUhLee+89nDp1SpEiiW5HEAREhAXCai+6HrUCgEC9BhVvhnaAVg21SgVIEgSVqy+wK9kkSCoAkgS1SsVLeUUsPFSPiDC96157lRCPIJIkV0i5W36ufcId4i4vd+9xZ47/d099Kd0MZNc96JxhDdwpiCuHB6FVoyh5HKskmREcqEWdqmEcx1rK+RSsRqMRABAZGZnv/sBA1yUXq9XqY1lEJYcgAEF6LSoE6+Q3bklyTWWo1aoRolHBYhfhdAIqFaDXqmF1OGG1iVx3tYjlvtdeqYIeAToNrDYHUjItirT85N7jBXjNfy1e583gBZzu4JX+aw1HV3JdcTFkWSEBqBIexJmXygCfgrV69eq4cOECtm/fju7du3vsM5lMWL16tfw8otJMEIBgvTZP7+IbGWZAAIL0apgtomsNWJWr1WpzOBGkV0MQXM8rqnu95JLzXrt7iIpWo/Zry0+tEm7OE+3dUK7oyGA4Jc4tXVb4FKy9evXCnDlzsHv3bnTt2lXe/vnnnyMpKQmZmZkQBAFPPPGEYoUSFTdBAEL0OoSF6PLfLwGA696mWgIkleDaJgDl4f5mSeK+1+4eohKgVee4/Fvyue7N+rsKUopPwTp06FAcOHAA+/fvx4ULF+Rf3tOnT8u992JiYjB06FDlKiUqRoIAhAbqEBqcf6hWrqiHBMBmE1Gpoh5OSYDTKd1cJF1CmsGKAK0alSuyV2dxEQSBkylQieDTeqxarRaLFy/GhAkTcPfdd9/s/u76U716dYwZMwbLly+HTpf/mxJRSaYSgLCgW4cqAAiCCmFBOqjVAsxW1yB+rcb1z8lsFaFSCQgL0kEQfPonRkSlmE8t1p07d6Jx48YYMmQIhgwZguzsbBiNRgQHByMkJETpGm/p/Pnz6NOnD6ZMmYI+ffoU23mp7FIJQFiwDsGBt/9QaHOIiIoMhqAC0gxWWG0OuGdsEAQB1SoFo0pEMGwOzpxDVN74FKyTJ0+GwWDAiy++iLFjxyIoKAhBQcXbQcNut+ONN95AdnZ2sZ6Xyi6VICAsRIfgfOYezi1Aq0bF0ABUDNUhNdOClAwz7A4ntBoVKlUMRGQFPQCBw22IyiGfgtU9jKZu3bqKFlMQ8+bNK9bWMZVtKsE1S1JggHdz9uWcq7hB7XCYLWGwi05o1SoE6tW4kpzFmXOIyimfbgA9+uijkCQJe/fuhdNZ/EtGHT58GGvXrsUHH3xQ7OemsketKlioAv+NnwwL1uFKsmvGp9AgHQQBuJLMmXOIyjOfWqwtW7bEwYMHsW3bNhw8eBAxMTEIDw9HQEAAVCrPrJ44caIihboZDAaMHz8ekydPRtWqVRU9NpU/apWA8NAABOgK/k+hJI6fJCL/8/keq/uTeEpKCnbt2nXL5yodrNOmTUNMTEyeiSmICqowoepW2sdPEpHyfH5Hybl6RX4rWQBQ/M1l48aNSExMxJYtWxQ9LpU/arWA8FC9Ip2LOH6SiHLyKVi//vprpevwSkJCAlJTU9GhQweP7VOnTsW2bduwePFiv9RFpYtG7VpLtSQtJE5EZYdPwdqqVSul6/DK7NmzYbFYPLZ16dIFY8aMQY8ePfxSE5UuWrUK4WEB0GoYqkRUNAoUrGlpafjtt99w7do1hIaGolWrVrj77ruLqrY8oqKi8t0eGRl5y31EbjqNChVD9fIMSURERcHrYF2xYgXmzp2bp8XYt29fzJgxI09vYKKSRKdRITxMD42av6dEVLS8Ctbt27dj1qxZ+e5LSEhAhQoV8OabbypamLe4mDrdSYBWjfDQAKgZqkRUDLx6p1m+fDmAm4PimzTB8OHD0bVrV6hUKkiShPj4+DwtWaKSQK9VIzxMz1AlomLjVYv1/PnzEAQBDz/8MBYvXiwPo1m5ciXee+89WK1WnD9/Hg0bNizSYokKQq9To2KonotHE1Gx8upjfFZWFgCgW7duHmNTcy5knpaWpnBpRL7T6zQIZ6gSkR941WJ1OBwAkGfS+/DwcPlrm82mYFlEvhEA6AM0CA8N4OxHROQXXrVY3TMr5e75m/ONyx+T8RPlJAAI1DNUici/CjSO9cCBAzAajQXa16tXL58KIyoofYAGFYJ1DFUi8qsCBeuqVavybHO/id1qH4OVikvFkAB/l0BE5H2w3mqifSIiIvqPV8E6evTooq6DiIioTGCwEhERKYjT0RARESmIwUpERKQgBisREZGCGKxEREQKYrASEREpiMFKRESkIAYrERGRghisRERECmKwEhERKYjBSkREpCAGKxERkYIYrERERApisBIRESmIwUpERKQgBisREZGCGKxEREQKYrASEREpiMFKRESkIAYrERGRghisRERECmKwEhERKYjBSkREpCAGKxERkYIYrERERApisBIRESmIwUpERKQgBisREZGCGKxEREQKYrASEREpiMFKRESkIAYrERGRghisRERECmKwEhERKYjBSkREpCAGKxERkYIYrERERApisBIRESmIwUpERKQgBisREZGCGKxEREQKYrASEREpiMFKRESkoFIZrBkZGXjnnXfQrl07tGjRAs8++ywSExP9XRYREVHpDNbXX38dR44cwaeffoqEhAQ0bNgQw4YNw7lz5/xdGhERlXOlLlgvXryIffv2Ydq0aXjggQdQp04dTJkyBVWqVMGWLVv8XR4REZVzpS5Yw8PD8dVXX6FJkybyNkEQIAgCDAaDHysjIiIqhcEaFhaG9u3bQ6fTydu2b9+Oixcvom3btn6sjIiIqBQGa26///47Jk6ciC5duqBDhw7+LoeIiMq5Uh2su3btwtChQ9G8eXPMnj3b3+UQERGV3mBdtWoVXnnlFXTs2BGLFi1CQECAv0siIiIqncG6evVqzJw5E/3798enn37qcb+ViIjInzT+LqCgzp8/j/fffx9xcXEYMWIEUlJS5H16vR6hoaF+rI6IiMq7Uhes27dvh91ux86dO7Fz506Pfb1798YHH3zgp8qIiIhKYbC+9NJLeOmll/xdBhERUb5K5T1WIiKikorBSkREpCAGKxERkYIYrERERApisBIRESmIwUpERKQgBisREZGCGKxEREQKYrASEREpiMFKRESkIAYrERGRghisRERECmKwEhERKYjBSkREpCAGKxERkYIYrERERApisBIRESmIwUpERKQgBisREZGCGKxEREQKYrASEREpiMFKRESkIAYrERGRghisRERECmKwEhERKYjBSkREpCAGKxERkYIYrERERApisBIRESmIwUpERKQgBisREZGCGKxEREQKYrASEREpiMFKRESkIAYrERGRghisRERECmKwEhERKYjBSkREpCAGKxERkYIYrERERApisBIRESmIwUpERKQgBisREZGCGKxEREQKYrASEREpiMFKRESkIAYrERGRghisRERECmKwEhERKYjBSkREpCAGKxERkYIYrERERAoqlcHqdDrx+eefo23btmjevDleeOEFXL582d9lERERlc5g/eKLL7B69WrMnDkTa9asgdPpxPDhw2Gz2fxdGhERlXOlLlhtNhuWLl2KMWPGoEOHDmjQoAHmzJmD69evY8eOHf4uj4iIyrlSF6x///03srKy0Lp1a3lbWFgYGjVqhMOHD/uxMiIiIkDj7wIK6vr16wCAqlWremyvUqWKvK8gkpOTIYoiOnXqpEh9RERU+ly7dg1qtVqRY5W6FqvZbAYA6HQ6j+0BAQGwWq0FPl5AQAA0mlL3+YKIiBSk0WgQEBCgzLEUOUox0uv1AFz3Wt1fA4DVakVgYGCBj5eYmKhYbURERKWuxeq+BJycnOyxPTk5GVFRUf4oiYiISFbqgrVBgwYICQnBwYMH5W0GgwEnTpxAy5Yt/VgZERFRKbwUrNPpMGDAAMyePRsRERG466678PHHHyM6OhpdunTxd3lERFTOlbpgBYAxY8bA4XBg8uTJsFgsaNmyJZYsWQKtVuvv0oiIqJwTJEmS/F0EERFRWVHq7rESERGVZAxWIiIiBTFYiYiIFMRgJSIiUhCDlYiISEEMViIiIgUxWImIiBRUboPV6XTi888/R9u2bdG8eXO88MILuHz5sr/LKlYZGRl455130K5dO7Ro0QLPPvtsuV2U4Pz584iJicH69ev9XUqx27hxIx5//HE0adIETzzxBL7//nt/l1RsHA4HPvvsM3Ts2BExMTHo378/jh496u+yis2XX36JgQMHemw7efIkBgwYgObNmyM2NhZff/21n6orHvl9D/bs2YO+ffsiJiYGsbGx+PDDD2GxWLw+ZrkN1i+++AKrV6/GzJkzsWbNGjidTgwfPhw2m83fpRWb119/HUeOHMGnn36KhIQENGzYEMOGDcO5c+f8XVqxstvteOONN5Cdne3vUordpk2bMGnSJPTv3x9bt25Ft27d5N+L8mDhwoVYt24dZs6ciY0bN6JOnToYPnx4nkU+yqL4+HjMnTvXY1t6ejqGDBmCmjVrIiEhAaNGjcLs2bORkJDgnyKLWH7fg8TERIwePRpxcXHYsGEDpk6dim3btmH69OneH1gqh6xWqxQTEyPFx8fL2zIzM6WmTZtKW7Zs8WNlxefChQtSvXr1pMTERHmb0+mUOnfuLM2dO9ePlRW/Tz75RBo0aJBUr149KSEhwd/lFBun0yl17NhR+uCDDzy2Dx06VFq0aJGfqipePXr0kGbNmiU/NhqNUr169aTt27f7saqidf36dWnEiBFS8+bNpa5du0oDBgyQ9y1atEhq06aNZLfb5W2ffPKJ1KVLF3+UWmRu9z0YN26c9Pzzz3s8f8OGDVLjxo0lq9Xq1fHLZYv177//RlZWFlq3bi1vCwsLQ6NGjXD48GE/VlZ8wsPD8dVXX6FJkybyNkEQIAgCDAaDHysrXocPH8batWvxwQcf+LuUYnf+/HlcvXoV3bt399i+ZMkSjBgxwk9VFa/IyEj8+OOPuHLlCkRRxNq1a6HT6dCgQQN/l1Zkjh8/Dq1Wi82bN6NZs2Ye+xITE9GqVStoNP9NI//QQw/hwoULSElJKe5Si8ztvgdDhw7FhAkTPLapVCrY7XaYTCavjl8qJ+EvrOvXrwP4b21XtypVqsj7yrqwsDC0b9/eY9v27dtx8eJFvP32236qqngZDAaMHz8ekydPzvO7UB6cP38eAJCdnY1hw4bhxIkTqF69Ol5++WXExsb6ubriMWnSJLz66qvo1KkT1Go1VCoV5s2bh5o1a/q7tCITGxt7y5/v9evXUa9ePY9tVapUAQBcu3YNlSpVKvL6isPtvgeNGjXyeGy327F8+XLcd999iIiI8Or45bLFajabAbiWoMspICAAVqvVHyX53e+//46JEyeiS5cu6NChg7/LKRbTpk1DTExMnhZbeeH+9D1hwgR069YNS5cuxSOPPIKRI0di//79fq6ueJw9exahoaFYsGAB1q5diz59+uCNN97AyZMn/V2aX1gslnzfFwGUy/dGh8OB8ePH48yZM5g6darXryuXLVa9Xg8AsNls8teA6xcnMDDQX2X5za5du/DGG2+gRYsWmD17tr/LKRYbN25EYmIitmzZ4u9S/Ma9zOKwYcPQu3dvAEDDhg1x4sQJLFu2zONWSVl07do1jBs3DsuXL8cDDzwAAGjSpAnOnj2LefPm4YsvvvBzhcVPr9fn6cDpDtSgoCB/lOQ3JpMJr732Gg4dOoT58+ejadOmXr+2XLZY3Zf9cvf8S05ORlRUlD9K8ptVq1bhlVdeQceOHbFo0SL502lZl5CQgNTUVHTo0AExMTGIiYkBAEydOhXDhw/3c3XFw/27nvvSX926dXHlyhV/lFSsjh07Brvd7tHPAACaNWuGixcv+qkq/4qOjs73fRFAuXpvTE5OlodeLVmyJM9tszsply3WBg0aICQkBAcPHpTvpRgMBpw4cQIDBgzwc3XFxz3caODAgZg0aRIEQfB3ScVm9uzZecaldenSBWPGjEGPHj38VFXxaty4MYKDg3Hs2DG5xQYAp0+fLtP3GN2io6MBAKdOnfJojZw+fRq1a9f2U1X+1bJlS6xZswaiKEKtVgMADhw4gDp16iAyMtLP1RWPzMxMDB48GCaTCfHx8ahfv36Bj1Eug1Wn02HAgAGYPXs2IiIicNddd+Hjjz9GdHQ0unTp4u/yisX58+fx/vvvIy4uDiNGjPDo8afX6xEaGurH6orerT59R0ZGlptP5nq9HsOHD8eCBQsQFRWFpk2bYuvWrdi3bx+WL1/u7/KKXNOmTXH//fdjwoQJmDp1KqKjo7Fx40bs378f33zzjb/L84u+ffti8eLFmDRpEoYPH44//vgDy5cvL9gYzlJu1qxZuHz5MhYvXoyIiAjcuHFD3hcRESF/4LidchmsADBmzBg4HA5MnjwZFosFLVu2xJIlS+T7TmXd9u3bYbfbsXPnTuzcudNjX+/evcvl8JPyaOTIkQgMDMScOXOQlJSEe+65B/PmzcODDz7o79KKnEqlwsKFCzF37lxMnDgRmZmZqFevHpYvX55nCEZ5ERkZicWLF+O9995D7969UblyZYwfP16+B1/WiaKIbdu2wW63Y/DgwXn27969G9WrV7/jcQRJkqSiKJCIiKg8Kpedl4iIiIoKg5WIiEhBDFYiIiIFMViJiIgUxGAlIiJSEIOViIhIQQxWIiIiBZXbCSKoZJk3bx7mz5/v1XO9HaRdksTGxuLq1asIDQ1FYmKiv8spsKysLKxYsQIjR4706fUHDx7EoEGDAACDBg3CpEmTlCwvjy5dusjz/fbv3x/vvPPOHV9z9epVLFu2DPv27cO1a9cAADVq1ECHDh0wePDgfJdMe/3117F169Z8j9epUyePifxNJhPmzp2L7du3Iz09HTVr1kS/fv0wcODAcjWdaHnAFisR3dZ3332HRx99FEuXLvV3KV753//+5zGJ/nfffZdnxZbcNm/ejK5du2LlypU4d+4czGYzzGYzTp8+ja+++grdu3fHsWPH8rzu77//9qomh8OBYcOGYeXKlUhOTobdbsc///yD9957r9ysKFWesMVKJc7AgQPx0EMP3XJ/aZwMfMaMGbBYLNBoSt8/uU8//RQ3btwoNfNHb9y40eNxZmYmdu3ahccffzzf5//yyy8YP348JEmCRqNB37590apVK2RkZGDt2rU4ffo00tLSMHr0aHz33XeoUKECANdyahcuXAAA9OrVC3FxcR7HrVy5svx1QkICjh49CgCIi4tDXFwc/u///g9nzpzBsmXL0KdPH9xzzz3KfAPI70rfv3Iq8xo1aoTOnTv7uwxFtWnTxt8llAtWqxXff/89AFewuSdQT0hIyDdYRVHE1KlT4Z7Zdf78+ejYsaO8/+mnn8bAgQNx9OhRJCcnY/369RgyZAgA4MyZMxBFEQDQoUOH2/7OusM+JCQEs2fPhl6vx1133YX+/ftDFEVs2bIFr732WqH//lQy8FIwlUqpqal46KGHUL9+fdSvXx979uyR982aNUvePm7cOADAlStX5G3z5s1DYmIi+vfvj2bNmqFt27b4+OOP8ywjBwAnTpzAyy+/jJYtW6Jp06bo2bMnVqxYIb+hug0cOBD169dHXFwc9u/fj86dO+O+++5Dv379ALjusdavX99jebb169fLNR09ehRr1qxB165d0aRJE/Ts2RM//fQTAGDHjh3o2bMnmjRpgri4uHxXnrHZbPjyyy/RtWtX3HfffWjdujXGjh2Lf/75x+N5Bw8elM+5adMmHDp0CAMGDEDz5s3x8MMPY/r06TCZTB7fs6tXrwIAjEYj6tevj4EDB8rHS0pKwvTp0xEXF4dmzZqhRYsW6NatG+bPn3/Hy6+34uvPCgB27doFo9EIwHUvt1GjRgCA3377Tb5vmtMvv/wi//06dOjgEaqAayWs1157DXFxcZg0aZLH/lOnTslfR0dH48CBAzh48CCys7M9juFwOPDnn38CcK2oo9frAQAtWrSQ1z/+/fffvf8GUYnHFiuVSpGRkXjnnXcwduxYAMB7772HRx55BKdOncLKlSsBuFosU6ZMyfPaw4cPY9GiRXA4HAAAi8WCxYsX488//8SyZcvkZaF+/fVXvPzyyx4B8ffff+P999/H//73P3z++ed5jp2eno6RI0fKb6516tTx6u8zZ84cHDhwwOM8o0aNwnPPPYcVK1bI2y9duoRZs2ZBq9Wif//+AFxv3CNGjMBvv/0mPy8tLQ3btm3D3r17sXTpUnkh95x+/PFH7NixQ/6QYDabsXr1aly5cgX/93//d8eaMzMzMXDgwDyLgp85cwZnzpzBxYsX8fHHH3v1978Vb39WbuvXrwcACIKAbt26QaPR4MSJE3A6ndiwYUOezleHDh2Sv27btm2+NbRu3RqtW7fOsz3n/dXnn39eDvuQkBBMnDgRTz75JADXBwW73Q7Ac7lClUqFSpUq4erVq+V2YfWyii1WKnEmTpwot1hy/3nvvffk5z3++ON49NFHAbjevBYsWIApU6bIQfHuu++iYsWKeY5/8OBBVK1aFdOmTcPEiRPl5xw8eBDr1q0D4HoDHz9+PGw2GypWrIhx48Zh9uzZeOyxxwC4lt3btGlTnmMbjUbodDpMmzYN48aNQ58+fbz6Ox84cAB9+vTBhx9+KC9ZZrfbsWLFCjRt2hQffvghevXqJT9/w4YN8tcrV66UQzU2NhYfffQR3nrrLVSqVAnZ2dl466234HQ685zz+++/R4sWLfDRRx/hxRdflHum/vzzz0hJSUFkZCQWLFiAiIgIAEBgYCAWLFiAV199FQCwbt06ORC6d++OOXPmYMqUKXLv2e+//14OFF9587NyS0pKwv79+wEAMTExqFatGp544gmoVCr5e5Z7Ma/Lly/LX9eoUaNAteVsseZsQZtMJkyaNAnbtm0DABgMBnlfcHCwxzGCgoLyPIdKP7ZYqVSbNm0aDh8+jLS0NHz55Zfy9j59+qBDhw75vkar1WLFihW46667AAANGjSQ117csmUL+vXrhz179iA1NRUA8Pbbb6Nnz54AXAFy4cIFnDx5EuvWrZO35/TKK6/g2WefLdDf46GHHsKsWbMAADVr1pRfHxQUhMWLF6NChQro1q0bvv/+e1itVly/fl1+7bfffgsAqF27NhYsWCAHSY0aNTBq1ChcuHABhw4dytMhrFq1ali6dCl0Oh0A4K+//pID+urVq2jWrBk6d+6M999/HwCg0Wg87iM+9thjqFatGi5duoQRI0bIwfzvv/9iyZIlsNvtSEtLK9TC8d78rNw2b94sf6jq1q0bAFcLsVWrVjhw4AAuXbqEw4cPo1WrVvJr3Je9AcjfB29JkoSAgADUrVsX7777LmrVqoXNmzdj2rRpAICPPvoIXbt29bji4f7Z5H58q0vbVDoxWKnEuV2v4NytioiICEybNg1jxoyRt1WtWhVvv/32LY/frFkz+Y0acIVaxYoVkZGRgdOnTwOAfE8MAMaPH4/x48fnOc6ff/4Jp9OZ580y531Ub91///3y1znHS9avX1/uharRaBAeHo7r16/DarUCcI0vPXfuHADgwoULaNiwYb7HP3bsWJ7v6QMPPOARJjVr1pSD1X3827nrrrtw1113ISMjAz/++COOHTuG33//3eN+ofsSrq+8+Vm5uTsIaTQa+coC4Pow5L7MnpCQ4BGs7vudgOtSeEGsXLkSTqcTDodD/j4+++yz2L17N3755Rdcu3YNp0+f9vge575y4H6csw4q/RisVOIUtFdwhw4dEB4ejvT0dABAw4YNbzs0xB1UOUVERCAjI0O+N+rNpTmLxYLMzEyEh4d7bM/92BshISHy1zmDOnet7nuK7kua7o46d5KcnJxnW1hYmMfjnAGQ+5JpfkwmE6ZPn45t27bJAVq7dm3UqlVL7jTlzXFux5ufFeD6kHP27FkArjDP754o4LqEP2XKFPn7nTO0c14WzikjIwOJiYlo27at3NnITaVS5Wnp1qtXD7/88gsA1+XpnB8Gc4e3+++Q+2dBpRuDlUq9zz77TA5VANizZw+2b98u33/NLb+QSUtLAwA5kHPeCxs7dizq1q2b77ECAwPzbMv95uuN3K1et9ydc3LLWWe9evXk+5+5VatWzetzemvmzJnYvHkz1Go1Jk+ejCeeeAIRERGYO3cuFi5cWKhju3nzswI87znfjtlsxvfff4+nnnoKgKvV7u7stm/fPvkyc047duzAlClTEBQUhJEjR+KFF16A2WzGmTNncP36dVSvXl3ufQzAI/CDg4NRvXp1qFQqOJ1OefgP4GqtpqSkAABq1arlVf1UOjBYqVT7448/5OEnlStXRkpKCiRJwvTp09GqVat8W4/Hjx/HP//8Iw/IT0xMREZGBgDXPTwAHm+UoijKLWiHw4EpU6bgrrvuQuPGjfO9hFfYwCqI0NBQVK9eHVeuXEFycjJatWolt362b9+OgwcPom7dumjcuLHP53DfO83d+ty5cycA11CTnENwTp486fO5cvPmZ2Wz2eRpBQMDAzFs2LA8x7lx4wbWrl0LwHU52B2ssbGx8njXn3/+GT///DPatWsnvy4zM1OeljA7OxtVq1YFAKSkpMjHePjhh7Fs2TIArt8VdweqgIAANGjQADqdDg0bNsTx48dx9OhRWK1WBAQE4K+//pIvuefXa5tKLwYrlTgnTpy47aWxRo0aoVq1arDZbHj77bflDisff/wxtmzZgoSEBKSmpmLmzJn49NNP87ze6XRiyJAheOGFFwDAo9OTu+dt586dUaFCBWRmZmLhwoUwmUxo3LgxfvjhBzlQnnvuuTzjHv2hT58++Pzzz5GRkYHBgwfjueeeg8Viwbx585CZmQmtVovY2Fifj+9ulWdlZSE+Ph4hISHo2bMnQkJCkJWVhatXr2LGjBmIiYnBrl27sHfvXvm1vo5ldfPmZ7V37145bDt27IhXXnklz3EcDgd27dqF1NRUHDlyBOfOncPdd98NnU6Hd955B6+88gokScLIkSPxzDPPoEWLFkhOTsbKlSvl8a+NGzeW793WqFED9913n9zh680338QjjzyCbdu2ybMx9e3bV77k3K1bNxw/fhwGgwFvvvkmOnXqhMWLFwNwfRDr3r17ob5PVLIwWKnEWblypXx5Lj+zZs1Cnz598MUXX+DMmTMAgB49eqB169Zo0KAB9uzZg/T0dGzduhWPP/54nvu1tWrVQnJyMt59912P7e3atZN7+YaEhODdd9/Fq6++Crvdnmee3LvvvjvfN3B/GDZsGH755RccOXIEJ06cwOTJkz32v/XWW4iOjvb5+A0bNsSZM2cgSRJmzJiB2rVro2fPnujbt6/cmouPj0d8fDwAV09e9zCbpKQk3H333T6f25ufVc7LwLeatlCj0eDxxx+Xf68SEhLw5ptvAnBN2D9jxgzMmDEDdrsdq1atwqpVqzxeX7NmTXz++ecel+Y/+ugjPPXUU8jKysLmzZuxefNmeV/jxo3xxhtvyI8HDBiArVu34q+//sL27duxfft2ed/AgQNveauBSieOY6VS6eTJk/IkBhUrVsTEiRMBuDoOTZgwQX7e1KlT5daMW926dbFy5Uq0aNECOp0OUVFReOmll7BgwQKPVUa6dOmCNWvWoFOnTggPD4dOp0OtWrUwZMgQxMfHy+M7/U2v12P58uV49dVXce+990Kv16NixYpo1aoVvvzySwwYMKBQxx87diweeeQRBAQEICwsTL4sO2rUKIwbNw61a9dGQEAAatasiT59+mDNmjXy9zHnjFi+uNPPKi0tTe4oFBwc7HEZN7cePXrIX2/atMlj9qxnnnkGW7ZswdNPP43q1atDq9UiKCgIjRs3xtixY7Fp06Y8Kyrdc889WLdunXxvWavVokaNGnjppZcQHx/vcf9bp9NhxYoVeP755xEVFQWtVos6depg4sSJ8u8ulR2CVNhue0SlwJUrV9CpUycAeZfzopKFPysq7dhiJSIiUhCDlYiISEEMViIiIgXxHisREZGC2GIlIiJSEIOViIhIQQxWIiIiBTFYiYiIFMRgJSIiUhCDlYiISEEMViIiIgUxWImIiBTEYCUiIlLQ/wM1rYaEtZhy7AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sns.set(color_codes=True)\n",
    "sns.set_style(\"white\")\n",
    "\n",
    "ax = sns.regplot(x=Y_test, y=Y_pred, scatter_kws={'alpha':0.4})\n",
    "ax.set_xlabel('Experimental pAC50', fontsize='large', fontweight='bold')\n",
    "ax.set_ylabel('Predicted pAC50', fontsize='large', fontweight='bold')\n",
    "ax.set_xlim(0, 12)\n",
    "ax.set_ylim(0, 12)\n",
    "ax.figure.set_size_inches(5, 5)\n",
    "plt.show"
   ]
  }
 ],
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