compound_prediction.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "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</th>\n",
       "      <th>AreaShape_Area (Cells)</th>\n",
       "      <th>AreaShape_BoundingBoxMaximum_X (Cells)</th>\n",
       "      <th>AreaShape_BoundingBoxMaximum_Y (Cells)</th>\n",
       "      <th>AreaShape_BoundingBoxMinimum_X (Cells)</th>\n",
       "      <th>AreaShape_BoundingBoxMinimum_Y (Cells)</th>\n",
       "      <th>AreaShape_Center_X (Cells)</th>\n",
       "      <th>AreaShape_Center_Y (Cells)</th>\n",
       "      <th>AreaShape_Compactness (Cells)</th>\n",
       "      <th>AreaShape_ConvexArea (Cells)</th>\n",
       "      <th>...</th>\n",
       "      <th>Texture_Variance_ER_3_02_256 (FilteredNuclei)</th>\n",
       "      <th>Texture_Variance_ER_3_03_256 (FilteredNuclei)</th>\n",
       "      <th>Texture_Variance_MITO_3_00_256 (FilteredNuclei)</th>\n",
       "      <th>Texture_Variance_MITO_3_01_256 (FilteredNuclei)</th>\n",
       "      <th>Texture_Variance_MITO_3_02_256 (FilteredNuclei)</th>\n",
       "      <th>Texture_Variance_MITO_3_03_256 (FilteredNuclei)</th>\n",
       "      <th>Texture_Variance_RNA_3_00_256 (FilteredNuclei)</th>\n",
       "      <th>Texture_Variance_RNA_3_01_256 (FilteredNuclei)</th>\n",
       "      <th>Texture_Variance_RNA_3_02_256 (FilteredNuclei)</th>\n",
       "      <th>Texture_Variance_RNA_3_03_256 (FilteredNuclei)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NCAP</td>\n",
       "      <td>0.484584</td>\n",
       "      <td>1.550984</td>\n",
       "      <td>1.081673</td>\n",
       "      <td>1.458567</td>\n",
       "      <td>0.683639</td>\n",
       "      <td>1.580247</td>\n",
       "      <td>1.136553</td>\n",
       "      <td>0.966164</td>\n",
       "      <td>0.461308</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.572775</td>\n",
       "      <td>-2.561085</td>\n",
       "      <td>-0.017531</td>\n",
       "      <td>0.024250</td>\n",
       "      <td>0.012477</td>\n",
       "      <td>-0.002019</td>\n",
       "      <td>-2.246122</td>\n",
       "      <td>-2.237506</td>\n",
       "      <td>-2.245002</td>\n",
       "      <td>-2.266022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NCAP</td>\n",
       "      <td>-0.157705</td>\n",
       "      <td>0.626174</td>\n",
       "      <td>-0.159741</td>\n",
       "      <td>0.724417</td>\n",
       "      <td>-0.195325</td>\n",
       "      <td>0.461126</td>\n",
       "      <td>-0.122063</td>\n",
       "      <td>-0.223185</td>\n",
       "      <td>-0.465019</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.488494</td>\n",
       "      <td>-3.488014</td>\n",
       "      <td>0.548434</td>\n",
       "      <td>0.555000</td>\n",
       "      <td>0.550023</td>\n",
       "      <td>0.551534</td>\n",
       "      <td>-2.841466</td>\n",
       "      <td>-2.849422</td>\n",
       "      <td>-2.832790</td>\n",
       "      <td>-2.854318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NCAP</td>\n",
       "      <td>-0.891750</td>\n",
       "      <td>1.022521</td>\n",
       "      <td>-1.474179</td>\n",
       "      <td>0.991381</td>\n",
       "      <td>-1.367278</td>\n",
       "      <td>0.729151</td>\n",
       "      <td>-1.464682</td>\n",
       "      <td>0.806011</td>\n",
       "      <td>-0.607531</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.909896</td>\n",
       "      <td>-2.929025</td>\n",
       "      <td>0.550452</td>\n",
       "      <td>0.545393</td>\n",
       "      <td>0.559650</td>\n",
       "      <td>0.545094</td>\n",
       "      <td>-2.475362</td>\n",
       "      <td>-2.475448</td>\n",
       "      <td>-2.478485</td>\n",
       "      <td>-2.488753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NCAP</td>\n",
       "      <td>0.805729</td>\n",
       "      <td>-0.893158</td>\n",
       "      <td>-0.597887</td>\n",
       "      <td>-0.476920</td>\n",
       "      <td>-1.074290</td>\n",
       "      <td>-0.821232</td>\n",
       "      <td>-0.801403</td>\n",
       "      <td>-0.148099</td>\n",
       "      <td>0.372238</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.807343</td>\n",
       "      <td>-3.766243</td>\n",
       "      <td>-0.531848</td>\n",
       "      <td>-0.522584</td>\n",
       "      <td>-0.506716</td>\n",
       "      <td>-0.504337</td>\n",
       "      <td>-3.265408</td>\n",
       "      <td>-3.266243</td>\n",
       "      <td>-3.265458</td>\n",
       "      <td>-3.272163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCAP</td>\n",
       "      <td>-0.845872</td>\n",
       "      <td>0.361942</td>\n",
       "      <td>-0.378814</td>\n",
       "      <td>-0.076474</td>\n",
       "      <td>-0.122078</td>\n",
       "      <td>0.049733</td>\n",
       "      <td>-0.194490</td>\n",
       "      <td>-0.002600</td>\n",
       "      <td>-0.767856</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.000074</td>\n",
       "      <td>-2.979573</td>\n",
       "      <td>0.046328</td>\n",
       "      <td>0.088113</td>\n",
       "      <td>0.065387</td>\n",
       "      <td>0.071783</td>\n",
       "      <td>-2.828945</td>\n",
       "      <td>-2.840122</td>\n",
       "      <td>-2.832672</td>\n",
       "      <td>-2.833497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3355</th>\n",
       "      <td>DMSO</td>\n",
       "      <td>-1.852676</td>\n",
       "      <td>-0.994764</td>\n",
       "      <td>-0.596370</td>\n",
       "      <td>-0.536708</td>\n",
       "      <td>-0.212692</td>\n",
       "      <td>-0.767186</td>\n",
       "      <td>-0.608554</td>\n",
       "      <td>-0.702304</td>\n",
       "      <td>-1.637105</td>\n",
       "      <td>...</td>\n",
       "      <td>0.670891</td>\n",
       "      <td>0.693776</td>\n",
       "      <td>0.208117</td>\n",
       "      <td>0.217832</td>\n",
       "      <td>0.158043</td>\n",
       "      <td>0.177694</td>\n",
       "      <td>1.386461</td>\n",
       "      <td>1.362714</td>\n",
       "      <td>1.368679</td>\n",
       "      <td>1.375872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3356</th>\n",
       "      <td>DMSO</td>\n",
       "      <td>1.364189</td>\n",
       "      <td>-0.596858</td>\n",
       "      <td>0.775472</td>\n",
       "      <td>-1.038565</td>\n",
       "      <td>1.125411</td>\n",
       "      <td>-0.960687</td>\n",
       "      <td>1.038982</td>\n",
       "      <td>1.491866</td>\n",
       "      <td>1.475083</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.269918</td>\n",
       "      <td>-1.253214</td>\n",
       "      <td>-1.173570</td>\n",
       "      <td>-1.142655</td>\n",
       "      <td>-1.190879</td>\n",
       "      <td>-1.169022</td>\n",
       "      <td>-1.124650</td>\n",
       "      <td>-1.124888</td>\n",
       "      <td>-1.110066</td>\n",
       "      <td>-1.117168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3357</th>\n",
       "      <td>ZIDO</td>\n",
       "      <td>1.498225</td>\n",
       "      <td>-0.845549</td>\n",
       "      <td>-0.939331</td>\n",
       "      <td>-0.938193</td>\n",
       "      <td>-0.510049</td>\n",
       "      <td>-0.950975</td>\n",
       "      <td>-0.743367</td>\n",
       "      <td>0.838250</td>\n",
       "      <td>1.351420</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.470872</td>\n",
       "      <td>-1.439188</td>\n",
       "      <td>-2.232664</td>\n",
       "      <td>-2.199772</td>\n",
       "      <td>-2.131945</td>\n",
       "      <td>-2.141157</td>\n",
       "      <td>-1.336880</td>\n",
       "      <td>-1.341865</td>\n",
       "      <td>-1.356299</td>\n",
       "      <td>-1.328229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3358</th>\n",
       "      <td>ZIDO</td>\n",
       "      <td>-0.217436</td>\n",
       "      <td>0.795811</td>\n",
       "      <td>0.226735</td>\n",
       "      <td>0.968863</td>\n",
       "      <td>-0.807405</td>\n",
       "      <td>0.824993</td>\n",
       "      <td>-0.324558</td>\n",
       "      <td>-0.084436</td>\n",
       "      <td>-0.173758</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.784842</td>\n",
       "      <td>-1.788869</td>\n",
       "      <td>-2.272898</td>\n",
       "      <td>-2.184796</td>\n",
       "      <td>-2.197992</td>\n",
       "      <td>-2.173862</td>\n",
       "      <td>-1.799917</td>\n",
       "      <td>-1.751446</td>\n",
       "      <td>-1.758930</td>\n",
       "      <td>-1.773001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3359</th>\n",
       "      <td>ZIDO</td>\n",
       "      <td>-0.297858</td>\n",
       "      <td>-0.298429</td>\n",
       "      <td>-0.184818</td>\n",
       "      <td>-0.335965</td>\n",
       "      <td>-0.510049</td>\n",
       "      <td>-0.207033</td>\n",
       "      <td>-0.250519</td>\n",
       "      <td>-0.660791</td>\n",
       "      <td>-0.256200</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.404408</td>\n",
       "      <td>-2.395806</td>\n",
       "      <td>-1.210246</td>\n",
       "      <td>-1.237065</td>\n",
       "      <td>-1.262051</td>\n",
       "      <td>-1.192353</td>\n",
       "      <td>-3.029732</td>\n",
       "      <td>-3.004298</td>\n",
       "      <td>-3.033795</td>\n",
       "      <td>-3.010833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3360 rows × 1625 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Compound  AreaShape_Area (Cells)  AreaShape_BoundingBoxMaximum_X (Cells)  \\\n",
       "0        NCAP                0.484584                                1.550984   \n",
       "1        NCAP               -0.157705                                0.626174   \n",
       "2        NCAP               -0.891750                                1.022521   \n",
       "3        NCAP                0.805729                               -0.893158   \n",
       "4        NCAP               -0.845872                                0.361942   \n",
       "...       ...                     ...                                     ...   \n",
       "3355     DMSO               -1.852676                               -0.994764   \n",
       "3356     DMSO                1.364189                               -0.596858   \n",
       "3357     ZIDO                1.498225                               -0.845549   \n",
       "3358     ZIDO               -0.217436                                0.795811   \n",
       "3359     ZIDO               -0.297858                               -0.298429   \n",
       "\n",
       "      AreaShape_BoundingBoxMaximum_Y (Cells)  \\\n",
       "0                                   1.081673   \n",
       "1                                  -0.159741   \n",
       "2                                  -1.474179   \n",
       "3                                  -0.597887   \n",
       "4                                  -0.378814   \n",
       "...                                      ...   \n",
       "3355                               -0.596370   \n",
       "3356                                0.775472   \n",
       "3357                               -0.939331   \n",
       "3358                                0.226735   \n",
       "3359                               -0.184818   \n",
       "\n",
       "      AreaShape_BoundingBoxMinimum_X (Cells)  \\\n",
       "0                                   1.458567   \n",
       "1                                   0.724417   \n",
       "2                                   0.991381   \n",
       "3                                  -0.476920   \n",
       "4                                  -0.076474   \n",
       "...                                      ...   \n",
       "3355                               -0.536708   \n",
       "3356                               -1.038565   \n",
       "3357                               -0.938193   \n",
       "3358                                0.968863   \n",
       "3359                               -0.335965   \n",
       "\n",
       "      AreaShape_BoundingBoxMinimum_Y (Cells)  AreaShape_Center_X (Cells)  \\\n",
       "0                                   0.683639                    1.580247   \n",
       "1                                  -0.195325                    0.461126   \n",
       "2                                  -1.367278                    0.729151   \n",
       "3                                  -1.074290                   -0.821232   \n",
       "4                                  -0.122078                    0.049733   \n",
       "...                                      ...                         ...   \n",
       "3355                               -0.212692                   -0.767186   \n",
       "3356                                1.125411                   -0.960687   \n",
       "3357                               -0.510049                   -0.950975   \n",
       "3358                               -0.807405                    0.824993   \n",
       "3359                               -0.510049                   -0.207033   \n",
       "\n",
       "      AreaShape_Center_Y (Cells)  AreaShape_Compactness (Cells)  \\\n",
       "0                       1.136553                       0.966164   \n",
       "1                      -0.122063                      -0.223185   \n",
       "2                      -1.464682                       0.806011   \n",
       "3                      -0.801403                      -0.148099   \n",
       "4                      -0.194490                      -0.002600   \n",
       "...                          ...                            ...   \n",
       "3355                   -0.608554                      -0.702304   \n",
       "3356                    1.038982                       1.491866   \n",
       "3357                   -0.743367                       0.838250   \n",
       "3358                   -0.324558                      -0.084436   \n",
       "3359                   -0.250519                      -0.660791   \n",
       "\n",
       "      AreaShape_ConvexArea (Cells)  ...  \\\n",
       "0                         0.461308  ...   \n",
       "1                        -0.465019  ...   \n",
       "2                        -0.607531  ...   \n",
       "3                         0.372238  ...   \n",
       "4                        -0.767856  ...   \n",
       "...                            ...  ...   \n",
       "3355                     -1.637105  ...   \n",
       "3356                      1.475083  ...   \n",
       "3357                      1.351420  ...   \n",
       "3358                     -0.173758  ...   \n",
       "3359                     -0.256200  ...   \n",
       "\n",
       "      Texture_Variance_ER_3_02_256 (FilteredNuclei)  \\\n",
       "0                                         -2.572775   \n",
       "1                                         -3.488494   \n",
       "2                                         -2.909896   \n",
       "3                                         -3.807343   \n",
       "4                                         -3.000074   \n",
       "...                                             ...   \n",
       "3355                                       0.670891   \n",
       "3356                                      -1.269918   \n",
       "3357                                      -1.470872   \n",
       "3358                                      -1.784842   \n",
       "3359                                      -2.404408   \n",
       "\n",
       "      Texture_Variance_ER_3_03_256 (FilteredNuclei)  \\\n",
       "0                                         -2.561085   \n",
       "1                                         -3.488014   \n",
       "2                                         -2.929025   \n",
       "3                                         -3.766243   \n",
       "4                                         -2.979573   \n",
       "...                                             ...   \n",
       "3355                                       0.693776   \n",
       "3356                                      -1.253214   \n",
       "3357                                      -1.439188   \n",
       "3358                                      -1.788869   \n",
       "3359                                      -2.395806   \n",
       "\n",
       "      Texture_Variance_MITO_3_00_256 (FilteredNuclei)  \\\n",
       "0                                           -0.017531   \n",
       "1                                            0.548434   \n",
       "2                                            0.550452   \n",
       "3                                           -0.531848   \n",
       "4                                            0.046328   \n",
       "...                                               ...   \n",
       "3355                                         0.208117   \n",
       "3356                                        -1.173570   \n",
       "3357                                        -2.232664   \n",
       "3358                                        -2.272898   \n",
       "3359                                        -1.210246   \n",
       "\n",
       "      Texture_Variance_MITO_3_01_256 (FilteredNuclei)  \\\n",
       "0                                            0.024250   \n",
       "1                                            0.555000   \n",
       "2                                            0.545393   \n",
       "3                                           -0.522584   \n",
       "4                                            0.088113   \n",
       "...                                               ...   \n",
       "3355                                         0.217832   \n",
       "3356                                        -1.142655   \n",
       "3357                                        -2.199772   \n",
       "3358                                        -2.184796   \n",
       "3359                                        -1.237065   \n",
       "\n",
       "      Texture_Variance_MITO_3_02_256 (FilteredNuclei)  \\\n",
       "0                                            0.012477   \n",
       "1                                            0.550023   \n",
       "2                                            0.559650   \n",
       "3                                           -0.506716   \n",
       "4                                            0.065387   \n",
       "...                                               ...   \n",
       "3355                                         0.158043   \n",
       "3356                                        -1.190879   \n",
       "3357                                        -2.131945   \n",
       "3358                                        -2.197992   \n",
       "3359                                        -1.262051   \n",
       "\n",
       "      Texture_Variance_MITO_3_03_256 (FilteredNuclei)  \\\n",
       "0                                           -0.002019   \n",
       "1                                            0.551534   \n",
       "2                                            0.545094   \n",
       "3                                           -0.504337   \n",
       "4                                            0.071783   \n",
       "...                                               ...   \n",
       "3355                                         0.177694   \n",
       "3356                                        -1.169022   \n",
       "3357                                        -2.141157   \n",
       "3358                                        -2.173862   \n",
       "3359                                        -1.192353   \n",
       "\n",
       "      Texture_Variance_RNA_3_00_256 (FilteredNuclei)  \\\n",
       "0                                          -2.246122   \n",
       "1                                          -2.841466   \n",
       "2                                          -2.475362   \n",
       "3                                          -3.265408   \n",
       "4                                          -2.828945   \n",
       "...                                              ...   \n",
       "3355                                        1.386461   \n",
       "3356                                       -1.124650   \n",
       "3357                                       -1.336880   \n",
       "3358                                       -1.799917   \n",
       "3359                                       -3.029732   \n",
       "\n",
       "      Texture_Variance_RNA_3_01_256 (FilteredNuclei)  \\\n",
       "0                                          -2.237506   \n",
       "1                                          -2.849422   \n",
       "2                                          -2.475448   \n",
       "3                                          -3.266243   \n",
       "4                                          -2.840122   \n",
       "...                                              ...   \n",
       "3355                                        1.362714   \n",
       "3356                                       -1.124888   \n",
       "3357                                       -1.341865   \n",
       "3358                                       -1.751446   \n",
       "3359                                       -3.004298   \n",
       "\n",
       "      Texture_Variance_RNA_3_02_256 (FilteredNuclei)  \\\n",
       "0                                          -2.245002   \n",
       "1                                          -2.832790   \n",
       "2                                          -2.478485   \n",
       "3                                          -3.265458   \n",
       "4                                          -2.832672   \n",
       "...                                              ...   \n",
       "3355                                        1.368679   \n",
       "3356                                       -1.110066   \n",
       "3357                                       -1.356299   \n",
       "3358                                       -1.758930   \n",
       "3359                                       -3.033795   \n",
       "\n",
       "      Texture_Variance_RNA_3_03_256 (FilteredNuclei)  \n",
       "0                                          -2.266022  \n",
       "1                                          -2.854318  \n",
       "2                                          -2.488753  \n",
       "3                                          -3.272163  \n",
       "4                                          -2.833497  \n",
       "...                                              ...  \n",
       "3355                                        1.375872  \n",
       "3356                                       -1.117168  \n",
       "3357                                       -1.328229  \n",
       "3358                                       -1.773001  \n",
       "3359                                       -3.010833  \n",
       "\n",
       "[3360 rows x 1625 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find important features for each drug\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "from toxifate import *\n",
    "os.environ[\"PATH\"] += os.pathsep + \"C:/Program Files/Graphviz/bin/\"\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "path = r\"C:\\Users\\Roman\\OneDrive - National University of Ireland, Galway\\CellPainting project data\\CellProfiler\\PerWell\\blastsAllByWellProfiler.csv\"\n",
    "\n",
    "perwell = pd.read_csv(path)\n",
    "perwell.drop(columns=['Unnamed: 0', 'Metadata_Well', 'PlateID', 'Metadata_Concentration (Image)', 'Count'], inplace=True, errors='ignore')\n",
    "perwell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation filter : 1415 features dropped (94% of total)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(2784, 91)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perwell.dropna(axis=1, how='any', inplace=True)\n",
    "\n",
    "#drop Compound=='DMSO'\n",
    "perwell = perwell[perwell.Compound != 'DMSO']\n",
    "perwell = drop_correlated_columns(perwell, 0.8, True)\n",
    "perwell.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of random forest classifier on test set: 0.41\n",
      "ROC AUC score of random forest classifier on test set: 0.91\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        ATOR       0.50      0.37      0.42        19\n",
      "        CERI       0.57      0.68      0.62        19\n",
      "        CISP       1.00      0.05      0.10        19\n",
      "        CLEV       0.15      0.32      0.20        19\n",
      "        CLOF       0.15      0.37      0.21        19\n",
      "        COLC       1.00      0.79      0.88        19\n",
      "        DAPT       0.35      0.40      0.37        20\n",
      "        DEXA       0.83      0.94      0.88        16\n",
      "        DOXO       0.75      0.75      0.75        16\n",
      "        ETHF       0.30      0.53      0.38        19\n",
      "        ETOP       0.72      0.68      0.70        19\n",
      "        ETRE       0.62      0.42      0.50        19\n",
      "        EZET       1.00      0.12      0.22        16\n",
      "        FIAL       0.83      0.25      0.38        20\n",
      "        GEMF       0.00      0.00      0.00        19\n",
      "         HCQ       0.31      0.50      0.38        16\n",
      "        IBIP       0.33      0.05      0.09        19\n",
      "        IMAT       0.64      0.47      0.55        19\n",
      "        LEFL       0.00      0.00      0.00        16\n",
      "        MCPP       0.25      0.68      0.37        19\n",
      "        NCAP       0.15      0.21      0.18        19\n",
      "        NELA       0.32      0.74      0.44        19\n",
      "        OLAN       0.25      0.16      0.19        19\n",
      "        SELU       0.71      0.31      0.43        16\n",
      "        SIMV       1.00      0.15      0.26        20\n",
      "        SUNI       0.61      0.58      0.59        19\n",
      "        TEBU       0.36      0.25      0.29        20\n",
      "        VORI       0.19      0.26      0.22        19\n",
      "        WURS       0.60      0.63      0.62        19\n",
      "        ZIDO       0.63      0.60      0.62        20\n",
      "\n",
      "    accuracy                           0.41       557\n",
      "   macro avg       0.50      0.41      0.40       557\n",
      "weighted avg       0.50      0.41      0.39       557\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Roman\\anaconda3\\envs\\py310\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\Users\\Roman\\anaconda3\\envs\\py310\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\Users\\Roman\\anaconda3\\envs\\py310\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AreaShape_MedianRadius (Cytoplasm)</td>\n",
       "      <td>0.036082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RadialDistribution_FracAtD_ER_1of4 (Cells)</td>\n",
       "      <td>0.033385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RadialDistribution_MeanFrac_ER_1of4 (Cytoplasm)</td>\n",
       "      <td>0.031993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Neighbors_NumberOfNeighbors_Adjacent (Cells)</td>\n",
       "      <td>0.030077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AreaShape_Compactness (FilteredNuclei)</td>\n",
       "      <td>0.028131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>AreaShape_BoundingBoxMaximum_X (Cells)</td>\n",
       "      <td>0.000405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>AreaShape_BoundingBoxMinimum_Y (Cells)</td>\n",
       "      <td>0.000404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>AreaShape_Orientation (Cells)</td>\n",
       "      <td>0.000354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>AreaShape_BoundingBoxMaximum_Y (Cells)</td>\n",
       "      <td>0.000320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Granularity_14_AGP (Cells)</td>\n",
       "      <td>0.000271</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            feature  importance\n",
       "0                AreaShape_MedianRadius (Cytoplasm)    0.036082\n",
       "1        RadialDistribution_FracAtD_ER_1of4 (Cells)    0.033385\n",
       "2   RadialDistribution_MeanFrac_ER_1of4 (Cytoplasm)    0.031993\n",
       "3      Neighbors_NumberOfNeighbors_Adjacent (Cells)    0.030077\n",
       "4            AreaShape_Compactness (FilteredNuclei)    0.028131\n",
       "..                                              ...         ...\n",
       "85           AreaShape_BoundingBoxMaximum_X (Cells)    0.000405\n",
       "86           AreaShape_BoundingBoxMinimum_Y (Cells)    0.000404\n",
       "87                    AreaShape_Orientation (Cells)    0.000354\n",
       "88           AreaShape_BoundingBoxMaximum_Y (Cells)    0.000320\n",
       "89                       Granularity_14_AGP (Cells)    0.000271\n",
       "\n",
       "[90 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = perwell.drop(columns=['Compound'])\n",
    "y = perwell['Compound']\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,random_state=42, stratify=y, test_size=0.2)\n",
    "\n",
    "rf = RandomForestClassifier(n_estimators=500, random_state=42, max_depth=5)\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "y_pred = pd.Series(rf.predict(X_test))\n",
    "y_pred_proba = rf.predict_proba(X_test)\n",
    "\n",
    "\n",
    "print('Accuracy of random forest classifier on test set: {:.2f}'.format(rf.score(X_test, y_test)))\n",
    "print('ROC AUC score of random forest classifier on test set: {:.2f}'.format(roc_auc_score(y_test, y_pred_proba, multi_class='ovr')))\n",
    "\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "# feature importance\n",
    "feature_importance = pd.DataFrame({'feature': X_train.columns, 'importance': rf.feature_importances_})\n",
    "feature_importance.sort_values(by='importance', ascending=False, inplace=True)\n",
    "feature_importance.reset_index(drop=True, inplace=True)\n",
    "feature_importance\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot roc curve, and sort by class alphabetical order\n",
    "y_pred_proba = pd.DataFrame(y_pred_proba, columns=rf.classes_)\n",
    "y_pred_proba = y_pred_proba.sort_index(axis=1)\n",
    "fpr = dict()\n",
    "tpr = dict()\n",
    "roc_auc = dict()\n",
    "for i in range(len(rf.classes_)):\n",
    "    fpr[i], tpr[i], _ = roc_curve(y_test, y_pred_proba.iloc[:, i], pos_label=rf.classes_[i])\n",
    "    roc_auc[i] = roc_auc_score(y_test, y_pred_proba, multi_class='ovr')\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "for i in range(len(rf.classes_)):\n",
    "    linestyle = 10*['solid'] + 10*['dashed'] + 10*['dashdot'] + 10*['dotted']\n",
    "    plt.plot(fpr[i], tpr[i], label='{0}'.format(rf.classes_[i]), linestyle=linestyle[i], linewidth=2, alpha=0.7)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlabel('False Positive Rate (1 - specificity)')\n",
    "plt.ylabel('True Positive Rate (sensitivity)')\n",
    "plt.title('ROC curve for multi-class')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'drug_tree.pdf'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import graphviz\n",
    "from sklearn import tree\n",
    "from sklearn.tree import export_graphviz\n",
    "\n",
    "# Visualize the tree\n",
    "tree_data = tree.export_graphviz(rf.estimators_[0], out_file=None, feature_names=X_train.columns, class_names=rf.classes_, filled=True, rounded=True, special_characters=True)\n",
    "graph = graphviz.Source(tree_data)\n",
    "graph.render(\"drug_tree\", )\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py310",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}