CPA-viabilityPrediction-SAVE231002.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Prediction of cell viability from Cell Painting profiles**\n",
    "This notebook is used to build a Random Forest model for the prediction of cell viability from Cell Painting profiles extracted with CellProfiler or Harmony.\n",
    "A data oversampling algorithm can be applied to the training set to balance the number of samples in each class.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Plate</th>\n",
       "      <th>Well</th>\n",
       "      <th>1s luciferase (CPS)</th>\n",
       "      <th>Row</th>\n",
       "      <th>Column</th>\n",
       "      <th>Concentration</th>\n",
       "      <th>Compound</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>aB1</td>\n",
       "      <td>A01</td>\n",
       "      <td>0.420143</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>none</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>aB1</td>\n",
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       "      <td>0.701689</td>\n",
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       "      <td>none</td>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>aB1</td>\n",
       "      <td>A03</td>\n",
       "      <td>115.530466</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>none</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>aB1</td>\n",
       "      <td>A04</td>\n",
       "      <td>117.776712</td>\n",
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       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>none</td>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>aB1</td>\n",
       "      <td>A05</td>\n",
       "      <td>113.804258</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>none</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>107.674235</td>\n",
       "      <td>14</td>\n",
       "      <td>19</td>\n",
       "      <td>10000</td>\n",
       "      <td>ZIDO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9732</th>\n",
       "      <td>260</td>\n",
       "      <td>gT5</td>\n",
       "      <td>N20</td>\n",
       "      <td>103.564534</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>10000</td>\n",
       "      <td>ZIDO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9733</th>\n",
       "      <td>261</td>\n",
       "      <td>gT5</td>\n",
       "      <td>N21</td>\n",
       "      <td>102.288023</td>\n",
       "      <td>14</td>\n",
       "      <td>21</td>\n",
       "      <td>30000</td>\n",
       "      <td>ZIDO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9734</th>\n",
       "      <td>262</td>\n",
       "      <td>gT5</td>\n",
       "      <td>N22</td>\n",
       "      <td>104.278182</td>\n",
       "      <td>14</td>\n",
       "      <td>22</td>\n",
       "      <td>30000</td>\n",
       "      <td>ZIDO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9735</th>\n",
       "      <td>263</td>\n",
       "      <td>gT5</td>\n",
       "      <td>N23</td>\n",
       "      <td>0.163236</td>\n",
       "      <td>14</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>none</td>\n",
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       "<p>9736 rows × 8 columns</p>\n",
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      ],
      "text/plain": [
       "      Unnamed: 0 Plate Well  1s luciferase (CPS)  Row  Column  Concentration   \n",
       "0              0   aB1  A01             0.420143    1       1              0  \\\n",
       "1              1   aB1  A02             0.701689    1       2              0   \n",
       "2              2   aB1  A03           115.530466    1       3              0   \n",
       "3              3   aB1  A04           117.776712    1       4              0   \n",
       "4              4   aB1  A05           113.804258    1       5              0   \n",
       "...          ...   ...  ...                  ...  ...     ...            ...   \n",
       "9731         259   gT5  N19           107.674235   14      19          10000   \n",
       "9732         260   gT5  N20           103.564534   14      20          10000   \n",
       "9733         261   gT5  N21           102.288023   14      21          30000   \n",
       "9734         262   gT5  N22           104.278182   14      22          30000   \n",
       "9735         263   gT5  N23             0.163236   14      23              0   \n",
       "\n",
       "     Compound  \n",
       "0        none  \n",
       "1        none  \n",
       "2        none  \n",
       "3        none  \n",
       "4        none  \n",
       "...       ...  \n",
       "9731     ZIDO  \n",
       "9732     ZIDO  \n",
       "9733     ZIDO  \n",
       "9734     ZIDO  \n",
       "9735     none  \n",
       "\n",
       "[9736 rows x 8 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from toxifate import *\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "import smogn\n",
    "import numpy as np\n",
    "import plotly.express as px\n",
    "\n",
    "SMOGN = 'True'\n",
    "\n",
    "csv_per_well = \"C:\\\\Users\\\\Roman\\\\OneDrive - National University of Ireland, Galway\\\\CellPainting project data\\\\CellProfiler\\\\PerWell\\\\blastsAllByWellProfiler.csv\"\n",
    "viability_file = \"C:\\\\Users\\\\Roman\\\\OneDrive - National University of Ireland, Galway\\\\plate reader\\\\CTG-fullWellNormresults.csv\"\n",
    "\n",
    "fileFormat = 'cp' # 'harmony' or 'cp'\n",
    "viability_table_per_well = pd.read_csv(viability_file)\n",
    "\n",
    "table_per_well = pd.read_csv(csv_per_well)\n",
    "if fileFormat == 'harmony':\n",
    "    table_per_well = table_per_well.drop(columns=['Unnamed: 0'])\n",
    "elif fileFormat == 'cp':\n",
    "    table_per_well = table_per_well.drop(columns=['Unnamed: 0'])\n",
    "viability_table_per_well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Metadata_Well</th>\n",
       "      <th>Compound</th>\n",
       "      <th>Metadata_Concentration (Image)</th>\n",
       "      <th>Count</th>\n",
       "      <th>AreaShape_Area (Cells)</th>\n",
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       "      <th>AreaShape_BoundingBoxMinimum_X (Cells)</th>\n",
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       "      <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",
       "      <th>PlateID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C10</td>\n",
       "      <td>NCAP</td>\n",
       "      <td>30</td>\n",
       "      <td>657.2</td>\n",
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       "      <td>-2.245002</td>\n",
       "      <td>-2.266022</td>\n",
       "      <td>blasts1BR1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C11</td>\n",
       "      <td>NCAP</td>\n",
       "      <td>100</td>\n",
       "      <td>682.6</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>...</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",
       "      <td>blasts1BR1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C12</td>\n",
       "      <td>NCAP</td>\n",
       "      <td>100</td>\n",
       "      <td>672.2</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>...</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",
       "      <td>blasts1BR1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C13</td>\n",
       "      <td>NCAP</td>\n",
       "      <td>300</td>\n",
       "      <td>676.4</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>...</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",
       "      <td>blasts1BR1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C14</td>\n",
       "      <td>NCAP</td>\n",
       "      <td>300</td>\n",
       "      <td>679.6</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>...</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",
       "      <td>blasts1BR1</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>3355</th>\n",
       "      <td>N5</td>\n",
       "      <td>DMSO</td>\n",
       "      <td>0</td>\n",
       "      <td>681.2</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>...</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",
       "      <td>blasts5BR3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3356</th>\n",
       "      <td>N6</td>\n",
       "      <td>DMSO</td>\n",
       "      <td>0</td>\n",
       "      <td>626.6</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>...</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",
       "      <td>blasts5BR3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3357</th>\n",
       "      <td>N7</td>\n",
       "      <td>ZIDO</td>\n",
       "      <td>10</td>\n",
       "      <td>624.8</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>...</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",
       "      <td>blasts5BR3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3358</th>\n",
       "      <td>N8</td>\n",
       "      <td>ZIDO</td>\n",
       "      <td>10</td>\n",
       "      <td>661.4</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>...</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",
       "      <td>blasts5BR3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3359</th>\n",
       "      <td>N9</td>\n",
       "      <td>ZIDO</td>\n",
       "      <td>30</td>\n",
       "      <td>648.2</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>...</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",
       "      <td>blasts5BR3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3360 rows × 1629 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Metadata_Well Compound  Metadata_Concentration (Image)  Count   \n",
       "0              C10     NCAP                              30  657.2  \\\n",
       "1              C11     NCAP                             100  682.6   \n",
       "2              C12     NCAP                             100  672.2   \n",
       "3              C13     NCAP                             300  676.4   \n",
       "4              C14     NCAP                             300  679.6   \n",
       "...            ...      ...                             ...    ...   \n",
       "3355            N5     DMSO                               0  681.2   \n",
       "3356            N6     DMSO                               0  626.6   \n",
       "3357            N7     ZIDO                              10  624.8   \n",
       "3358            N8     ZIDO                              10  661.4   \n",
       "3359            N9     ZIDO                              30  648.2   \n",
       "\n",
       "      AreaShape_Area (Cells)  AreaShape_BoundingBoxMaximum_X (Cells)   \n",
       "0                   0.484584                                1.550984  \\\n",
       "1                  -0.157705                                0.626174   \n",
       "2                  -0.891750                                1.022521   \n",
       "3                   0.805729                               -0.893158   \n",
       "4                  -0.845872                                0.361942   \n",
       "...                      ...                                     ...   \n",
       "3355               -1.852676                               -0.994764   \n",
       "3356                1.364189                               -0.596858   \n",
       "3357                1.498225                               -0.845549   \n",
       "3358               -0.217436                                0.795811   \n",
       "3359               -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",
       "      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)     PlateID  \n",
       "0                                          -2.266022  blasts1BR1  \n",
       "1                                          -2.854318  blasts1BR1  \n",
       "2                                          -2.488753  blasts1BR1  \n",
       "3                                          -3.272163  blasts1BR1  \n",
       "4                                          -2.833497  blasts1BR1  \n",
       "...                                              ...         ...  \n",
       "3355                                        1.375872  blasts5BR3  \n",
       "3356                                       -1.117168  blasts5BR3  \n",
       "3357                                       -1.328229  blasts5BR3  \n",
       "3358                                       -1.773001  blasts5BR3  \n",
       "3359                                       -3.010833  blasts5BR3  \n",
       "\n",
       "[3360 rows x 1629 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# aggregate viability_table_per_well by Compound and Concentration, median, discard Row, Column, Unnamed: 0,Plate,Well\n",
    "viability_table_per_treatment = viability_table_per_well.groupby(['Compound', 'Concentration']).median(numeric_only=True).reset_index()\n",
    "viability_table_per_treatment = viability_table_per_treatment.drop(columns=['Row', 'Column', 'Unnamed: 0'])\n",
    "table_per_well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Compound</th>\n",
       "      <th>Concentration</th>\n",
       "      <th>Count</th>\n",
       "      <th>Viability</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>...</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>ATOR</td>\n",
       "      <td>10</td>\n",
       "      <td>645.7</td>\n",
       "      <td>98.262090</td>\n",
       "      <td>0.723791</td>\n",
       "      <td>0.305633</td>\n",
       "      <td>0.241893</td>\n",
       "      <td>0.029278</td>\n",
       "      <td>0.207533</td>\n",
       "      <td>0.274138</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.190034</td>\n",
       "      <td>-1.188338</td>\n",
       "      <td>-0.334331</td>\n",
       "      <td>-0.326266</td>\n",
       "      <td>-0.339149</td>\n",
       "      <td>-0.328388</td>\n",
       "      <td>-1.235518</td>\n",
       "      <td>-1.178616</td>\n",
       "      <td>-1.229783</td>\n",
       "      <td>-1.212236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ATOR</td>\n",
       "      <td>30</td>\n",
       "      <td>657.1</td>\n",
       "      <td>94.406498</td>\n",
       "      <td>0.999172</td>\n",
       "      <td>-0.298637</td>\n",
       "      <td>-0.146189</td>\n",
       "      <td>-0.488954</td>\n",
       "      <td>-0.253092</td>\n",
       "      <td>-0.496457</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.006949</td>\n",
       "      <td>-2.013204</td>\n",
       "      <td>-0.274977</td>\n",
       "      <td>-0.289293</td>\n",
       "      <td>-0.308476</td>\n",
       "      <td>-0.254404</td>\n",
       "      <td>-2.078155</td>\n",
       "      <td>-2.088527</td>\n",
       "      <td>-2.090760</td>\n",
       "      <td>-2.067423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ATOR</td>\n",
       "      <td>100</td>\n",
       "      <td>647.6</td>\n",
       "      <td>95.387465</td>\n",
       "      <td>0.628366</td>\n",
       "      <td>-0.518878</td>\n",
       "      <td>-0.295854</td>\n",
       "      <td>-0.743883</td>\n",
       "      <td>-0.127215</td>\n",
       "      <td>-0.441306</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.376642</td>\n",
       "      <td>-2.334488</td>\n",
       "      <td>-0.159984</td>\n",
       "      <td>-0.150268</td>\n",
       "      <td>-0.148988</td>\n",
       "      <td>-0.136117</td>\n",
       "      <td>-2.493095</td>\n",
       "      <td>-2.498927</td>\n",
       "      <td>-2.472790</td>\n",
       "      <td>-2.489648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ATOR</td>\n",
       "      <td>300</td>\n",
       "      <td>657.1</td>\n",
       "      <td>91.679638</td>\n",
       "      <td>0.628307</td>\n",
       "      <td>-0.374398</td>\n",
       "      <td>0.205381</td>\n",
       "      <td>-0.443549</td>\n",
       "      <td>0.354027</td>\n",
       "      <td>-0.282174</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.943113</td>\n",
       "      <td>-2.907084</td>\n",
       "      <td>-0.359665</td>\n",
       "      <td>-0.362409</td>\n",
       "      <td>-0.373531</td>\n",
       "      <td>-0.361484</td>\n",
       "      <td>-3.071297</td>\n",
       "      <td>-3.011046</td>\n",
       "      <td>-3.075821</td>\n",
       "      <td>-3.084995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ATOR</td>\n",
       "      <td>1000</td>\n",
       "      <td>652.8</td>\n",
       "      <td>93.215158</td>\n",
       "      <td>0.818909</td>\n",
       "      <td>-0.430752</td>\n",
       "      <td>0.419207</td>\n",
       "      <td>-0.226666</td>\n",
       "      <td>0.272192</td>\n",
       "      <td>-0.379705</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.457275</td>\n",
       "      <td>-3.449132</td>\n",
       "      <td>-0.524304</td>\n",
       "      <td>-0.526783</td>\n",
       "      <td>-0.506650</td>\n",
       "      <td>-0.506339</td>\n",
       "      <td>-3.996620</td>\n",
       "      <td>-3.978206</td>\n",
       "      <td>-3.919076</td>\n",
       "      <td>-3.980616</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>236</th>\n",
       "      <td>ZIDO</td>\n",
       "      <td>300</td>\n",
       "      <td>608.9</td>\n",
       "      <td>103.726408</td>\n",
       "      <td>4.157989</td>\n",
       "      <td>-0.348167</td>\n",
       "      <td>0.530970</td>\n",
       "      <td>-0.608693</td>\n",
       "      <td>0.365170</td>\n",
       "      <td>-0.395814</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.813843</td>\n",
       "      <td>-2.820691</td>\n",
       "      <td>-1.335125</td>\n",
       "      <td>-1.386905</td>\n",
       "      <td>-1.307800</td>\n",
       "      <td>-1.372935</td>\n",
       "      <td>-3.489625</td>\n",
       "      <td>-3.473026</td>\n",
       "      <td>-3.499415</td>\n",
       "      <td>-3.456903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>ZIDO</td>\n",
       "      <td>1000</td>\n",
       "      <td>602.1</td>\n",
       "      <td>101.589529</td>\n",
       "      <td>5.971247</td>\n",
       "      <td>-0.149339</td>\n",
       "      <td>-0.767850</td>\n",
       "      <td>-0.388816</td>\n",
       "      <td>-0.609040</td>\n",
       "      <td>-0.312922</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.201052</td>\n",
       "      <td>-3.188979</td>\n",
       "      <td>-1.442472</td>\n",
       "      <td>-1.446925</td>\n",
       "      <td>-1.447412</td>\n",
       "      <td>-1.504539</td>\n",
       "      <td>-4.048124</td>\n",
       "      <td>-4.039520</td>\n",
       "      <td>-4.060838</td>\n",
       "      <td>-4.054396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>ZIDO</td>\n",
       "      <td>3000</td>\n",
       "      <td>558.0</td>\n",
       "      <td>98.338109</td>\n",
       "      <td>7.693716</td>\n",
       "      <td>-0.049738</td>\n",
       "      <td>0.743277</td>\n",
       "      <td>-0.250638</td>\n",
       "      <td>0.014930</td>\n",
       "      <td>-0.317881</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.294677</td>\n",
       "      <td>-3.296510</td>\n",
       "      <td>-1.561753</td>\n",
       "      <td>-1.555932</td>\n",
       "      <td>-1.556475</td>\n",
       "      <td>-1.560844</td>\n",
       "      <td>-4.242535</td>\n",
       "      <td>-4.220498</td>\n",
       "      <td>-4.258327</td>\n",
       "      <td>-4.242408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>ZIDO</td>\n",
       "      <td>10000</td>\n",
       "      <td>552.0</td>\n",
       "      <td>99.459198</td>\n",
       "      <td>12.010221</td>\n",
       "      <td>0.567874</td>\n",
       "      <td>0.135141</td>\n",
       "      <td>0.156663</td>\n",
       "      <td>-0.749401</td>\n",
       "      <td>0.402412</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.464383</td>\n",
       "      <td>-3.457420</td>\n",
       "      <td>-0.460019</td>\n",
       "      <td>-0.493190</td>\n",
       "      <td>-0.436575</td>\n",
       "      <td>-0.462047</td>\n",
       "      <td>-4.368712</td>\n",
       "      <td>-4.366521</td>\n",
       "      <td>-4.409481</td>\n",
       "      <td>-4.381157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>ZIDO</td>\n",
       "      <td>30000</td>\n",
       "      <td>520.0</td>\n",
       "      <td>98.473119</td>\n",
       "      <td>15.781234</td>\n",
       "      <td>-0.765602</td>\n",
       "      <td>0.909573</td>\n",
       "      <td>-1.364005</td>\n",
       "      <td>0.359397</td>\n",
       "      <td>-1.073192</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.477557</td>\n",
       "      <td>-3.479984</td>\n",
       "      <td>-0.333055</td>\n",
       "      <td>-0.327345</td>\n",
       "      <td>-0.271849</td>\n",
       "      <td>-0.306039</td>\n",
       "      <td>-4.261987</td>\n",
       "      <td>-4.252684</td>\n",
       "      <td>-4.263886</td>\n",
       "      <td>-4.270743</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>241 rows × 1578 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Compound  Concentration  Count   Viability  AreaShape_Area (Cells)   \n",
       "0       ATOR             10  645.7   98.262090                0.723791  \\\n",
       "1       ATOR             30  657.1   94.406498                0.999172   \n",
       "2       ATOR            100  647.6   95.387465                0.628366   \n",
       "3       ATOR            300  657.1   91.679638                0.628307   \n",
       "4       ATOR           1000  652.8   93.215158                0.818909   \n",
       "..       ...            ...    ...         ...                     ...   \n",
       "236     ZIDO            300  608.9  103.726408                4.157989   \n",
       "237     ZIDO           1000  602.1  101.589529                5.971247   \n",
       "238     ZIDO           3000  558.0   98.338109                7.693716   \n",
       "239     ZIDO          10000  552.0   99.459198               12.010221   \n",
       "240     ZIDO          30000  520.0   98.473119               15.781234   \n",
       "\n",
       "     AreaShape_BoundingBoxMaximum_X (Cells)   \n",
       "0                                  0.305633  \\\n",
       "1                                 -0.298637   \n",
       "2                                 -0.518878   \n",
       "3                                 -0.374398   \n",
       "4                                 -0.430752   \n",
       "..                                      ...   \n",
       "236                               -0.348167   \n",
       "237                               -0.149339   \n",
       "238                               -0.049738   \n",
       "239                                0.567874   \n",
       "240                               -0.765602   \n",
       "\n",
       "     AreaShape_BoundingBoxMaximum_Y (Cells)   \n",
       "0                                  0.241893  \\\n",
       "1                                 -0.146189   \n",
       "2                                 -0.295854   \n",
       "3                                  0.205381   \n",
       "4                                  0.419207   \n",
       "..                                      ...   \n",
       "236                                0.530970   \n",
       "237                               -0.767850   \n",
       "238                                0.743277   \n",
       "239                                0.135141   \n",
       "240                                0.909573   \n",
       "\n",
       "     AreaShape_BoundingBoxMinimum_X (Cells)   \n",
       "0                                  0.029278  \\\n",
       "1                                 -0.488954   \n",
       "2                                 -0.743883   \n",
       "3                                 -0.443549   \n",
       "4                                 -0.226666   \n",
       "..                                      ...   \n",
       "236                               -0.608693   \n",
       "237                               -0.388816   \n",
       "238                               -0.250638   \n",
       "239                                0.156663   \n",
       "240                               -1.364005   \n",
       "\n",
       "     AreaShape_BoundingBoxMinimum_Y (Cells)  AreaShape_Center_X (Cells)  ...   \n",
       "0                                  0.207533                    0.274138  ...  \\\n",
       "1                                 -0.253092                   -0.496457  ...   \n",
       "2                                 -0.127215                   -0.441306  ...   \n",
       "3                                  0.354027                   -0.282174  ...   \n",
       "4                                  0.272192                   -0.379705  ...   \n",
       "..                                      ...                         ...  ...   \n",
       "236                                0.365170                   -0.395814  ...   \n",
       "237                               -0.609040                   -0.312922  ...   \n",
       "238                                0.014930                   -0.317881  ...   \n",
       "239                               -0.749401                    0.402412  ...   \n",
       "240                                0.359397                   -1.073192  ...   \n",
       "\n",
       "     Texture_Variance_ER_3_02_256 (FilteredNuclei)   \n",
       "0                                        -1.190034  \\\n",
       "1                                        -2.006949   \n",
       "2                                        -2.376642   \n",
       "3                                        -2.943113   \n",
       "4                                        -3.457275   \n",
       "..                                             ...   \n",
       "236                                      -2.813843   \n",
       "237                                      -3.201052   \n",
       "238                                      -3.294677   \n",
       "239                                      -3.464383   \n",
       "240                                      -3.477557   \n",
       "\n",
       "     Texture_Variance_ER_3_03_256 (FilteredNuclei)   \n",
       "0                                        -1.188338  \\\n",
       "1                                        -2.013204   \n",
       "2                                        -2.334488   \n",
       "3                                        -2.907084   \n",
       "4                                        -3.449132   \n",
       "..                                             ...   \n",
       "236                                      -2.820691   \n",
       "237                                      -3.188979   \n",
       "238                                      -3.296510   \n",
       "239                                      -3.457420   \n",
       "240                                      -3.479984   \n",
       "\n",
       "     Texture_Variance_MITO_3_00_256 (FilteredNuclei)   \n",
       "0                                          -0.334331  \\\n",
       "1                                          -0.274977   \n",
       "2                                          -0.159984   \n",
       "3                                          -0.359665   \n",
       "4                                          -0.524304   \n",
       "..                                               ...   \n",
       "236                                        -1.335125   \n",
       "237                                        -1.442472   \n",
       "238                                        -1.561753   \n",
       "239                                        -0.460019   \n",
       "240                                        -0.333055   \n",
       "\n",
       "     Texture_Variance_MITO_3_01_256 (FilteredNuclei)   \n",
       "0                                          -0.326266  \\\n",
       "1                                          -0.289293   \n",
       "2                                          -0.150268   \n",
       "3                                          -0.362409   \n",
       "4                                          -0.526783   \n",
       "..                                               ...   \n",
       "236                                        -1.386905   \n",
       "237                                        -1.446925   \n",
       "238                                        -1.555932   \n",
       "239                                        -0.493190   \n",
       "240                                        -0.327345   \n",
       "\n",
       "     Texture_Variance_MITO_3_02_256 (FilteredNuclei)   \n",
       "0                                          -0.339149  \\\n",
       "1                                          -0.308476   \n",
       "2                                          -0.148988   \n",
       "3                                          -0.373531   \n",
       "4                                          -0.506650   \n",
       "..                                               ...   \n",
       "236                                        -1.307800   \n",
       "237                                        -1.447412   \n",
       "238                                        -1.556475   \n",
       "239                                        -0.436575   \n",
       "240                                        -0.271849   \n",
       "\n",
       "     Texture_Variance_MITO_3_03_256 (FilteredNuclei)   \n",
       "0                                          -0.328388  \\\n",
       "1                                          -0.254404   \n",
       "2                                          -0.136117   \n",
       "3                                          -0.361484   \n",
       "4                                          -0.506339   \n",
       "..                                               ...   \n",
       "236                                        -1.372935   \n",
       "237                                        -1.504539   \n",
       "238                                        -1.560844   \n",
       "239                                        -0.462047   \n",
       "240                                        -0.306039   \n",
       "\n",
       "     Texture_Variance_RNA_3_00_256 (FilteredNuclei)   \n",
       "0                                         -1.235518  \\\n",
       "1                                         -2.078155   \n",
       "2                                         -2.493095   \n",
       "3                                         -3.071297   \n",
       "4                                         -3.996620   \n",
       "..                                              ...   \n",
       "236                                       -3.489625   \n",
       "237                                       -4.048124   \n",
       "238                                       -4.242535   \n",
       "239                                       -4.368712   \n",
       "240                                       -4.261987   \n",
       "\n",
       "     Texture_Variance_RNA_3_01_256 (FilteredNuclei)   \n",
       "0                                         -1.178616  \\\n",
       "1                                         -2.088527   \n",
       "2                                         -2.498927   \n",
       "3                                         -3.011046   \n",
       "4                                         -3.978206   \n",
       "..                                              ...   \n",
       "236                                       -3.473026   \n",
       "237                                       -4.039520   \n",
       "238                                       -4.220498   \n",
       "239                                       -4.366521   \n",
       "240                                       -4.252684   \n",
       "\n",
       "     Texture_Variance_RNA_3_02_256 (FilteredNuclei)   \n",
       "0                                         -1.229783  \\\n",
       "1                                         -2.090760   \n",
       "2                                         -2.472790   \n",
       "3                                         -3.075821   \n",
       "4                                         -3.919076   \n",
       "..                                              ...   \n",
       "236                                       -3.499415   \n",
       "237                                       -4.060838   \n",
       "238                                       -4.258327   \n",
       "239                                       -4.409481   \n",
       "240                                       -4.263886   \n",
       "\n",
       "     Texture_Variance_RNA_3_03_256 (FilteredNuclei)  \n",
       "0                                         -1.212236  \n",
       "1                                         -2.067423  \n",
       "2                                         -2.489648  \n",
       "3                                         -3.084995  \n",
       "4                                         -3.980616  \n",
       "..                                              ...  \n",
       "236                                       -3.456903  \n",
       "237                                       -4.054396  \n",
       "238                                       -4.242408  \n",
       "239                                       -4.381157  \n",
       "240                                       -4.270743  \n",
       "\n",
       "[241 rows x 1578 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if fileFormat == 'cp':\n",
    "    table_per_well.rename(columns={'Metadata_Compound':'Compound', 'Metadata_Concentration (Image)':'Concentration','Metadata_Well':'Well'}, inplace=True)\n",
    "    table_per_well.drop(columns=['PlateID','Well'], inplace=True)\n",
    "\n",
    "# aggregate table_per_well by Compound and Concentration, median, discard Row and Column\n",
    "table_per_treatment = table_per_well.groupby(['Compound', 'Concentration']).median(numeric_only=True).reset_index()\n",
    "table_per_treatment.drop(columns=['Row', 'Column'], inplace=True, errors='ignore')\n",
    "table_per_treatment.insert(loc=3, column='Viability', value=viability_table_per_treatment['1s luciferase (CPS)'])\n",
    "table_per_treatment.head()\n",
    "#remove columns with too many missing values\n",
    "table_per_treatment = table_per_treatment.dropna(axis=1)\n",
    "table_per_treatment\n",
    "\n",
    "\n",
    "#plot the distribution of Viability values with a displot (kde), by Compound\n",
    "\n",
    "#sns.displot(data=table_per_treatment.loc[table_per_treatment['Compound'] != 'DMSO'], x=\"Viability\", hue=\"Compound\", kind=\"kde\", fill=True, col='Compound', col_wrap=6,facet_kws=dict(sharey=False),cut=0, legend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "dist_matrix: 100%|##########| 33/33 [00:18<00:00,  1.80it/s]\n",
      "synth_matrix: 100%|##########| 33/33 [00:12<00:00,  2.54it/s]\n",
      "r_index: 100%|##########| 20/20 [00:04<00:00,  4.90it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x27d2286cb10>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#remove correlated features from table_per_treatment\n",
    "#table_per_treatment = Drop_correlated_columns(table_per_treatment, 0.9)\n",
    "\n",
    "#oversample the data to balance the Viability values between 0 and 100\n",
    "augmentedData = smogn.smoter(data = table_per_treatment, y = \"Viability\", rel_xtrm_type = \"low\", samp_method='balance',\n",
    "                             rel_coef = 5)\n",
    "sns.kdeplot(table_per_treatment['Viability'], label = \"Original\", cut=0)\n",
    "sns.kdeplot(augmentedData['Viability'], label = \"SMOGN\",cut=0)\n",
    "#add minor ticks to x axis \n",
    "plt.minorticks_on()\n",
    "plt.legend()"
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  {
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   "execution_count": 87,
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   "outputs": [],
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    "if SMOGN == 'False':\n",
    "    augmentedData = table_per_treatment\n",
    "\n",
    "\n",
    "\n",
    "#train a random forest regressor to predict viability from the other features\n",
    "#split the data into train and test sets\n",
    "augmentedData.reset_index(drop=True, inplace=True)\n",
    "augmentedData = augmentedData.sample(frac=1).reset_index(drop=True) # shuffle rows to avoid chunks of same drugs with stratified cv\n",
    "X = augmentedData.drop(columns=['Compound', 'Concentration', 'Viability','Count'])\n",
    "y = augmentedData['Viability']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "#train the model\n",
    "rf = RandomForestRegressor(n_estimators=100, random_state=72)\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "#predict the viability values for the test set\n",
    "y_pred = rf.predict(X_test)\n",
    "\n",
    "#calculate the mean squared error and the R2 score\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# merge y_test and y_pred into a dataframe, and add the Compound and Concentration columns using merge\n",
    "\n",
    "y_test = pd.DataFrame(y_test)\n",
    "y_test['Predicted Viability'] = y_pred\n",
    "y_test = y_test.merge(augmentedData[['Compound', 'Concentration']], left_index=True, right_index=True)\n",
    "\n"
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      "Mean squared error:  154.46989342529196\n",
      "R2 score:  0.8747729989489617\n"
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    "#plot the actual and predicted viability values for each compound\n",
    "fig = px.scatter(y_test, x=\"Viability\", y=\"Predicted Viability\", color=\"Compound\", hover_data=['Concentration'], trendline=\"ols\", trendline_scope='overall',height=1000, width=1200)\n",
    "fig.update_traces(marker={'size': 10})\n",
    "fig.update_layout(\n",
    "    font=dict(\n",
    "        size=18,\n",
    "        color=\"black\"\n",
    "    )\n",
    ")\n",
    "fig.show()\n",
    "\n",
    "print('Mean squared error: ', mse)\n",
    "print('R2 score: ', r2)\n"
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   "source": [
    "\n",
    "fig2 = px.scatter(y_train, x=\"Viability\", y=\"Predicted Viability\", color=\"Compound\", hover_data=['Concentration'], trendline=\"ols\", trendline_scope='overall',height=1000, width=1200)\n",
    "fig2.update_traces(marker={'size': 8})\n",
    "\n",
    "fig2.update_traces(marker={'size': 10})\n",
    "fig2.update_layout(\n",
    "    font=dict(\n",
    "        size=18,\n",
    "        color=\"black\"\n",
    "    )\n",
    ")\n",
    "fig2.show()\n",
    "r2_score(y_train['Viability'], y_train['Predicted Viability'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Granularity_2_ER (FilteredNuclei)</th>\n",
       "      <td>0.358767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Granularity_4_ER (Cytoplasm)</th>\n",
       "      <td>0.125132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Granularity_2_RNA (FilteredNuclei)</th>\n",
       "      <td>0.096380</td>\n",
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       "      <th>Texture_InverseDifferenceMoment_DNA_3_00_256 (Cytoplasm)</th>\n",
       "      <td>0.057619</td>\n",
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       "      <th>Granularity_3_RNA (Cells)</th>\n",
       "      <td>0.044074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Granularity_3_ER (Cells)</th>\n",
       "      <td>0.029807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RadialDistribution_FracAtD_DNA_2of4 (FilteredNuclei)</th>\n",
       "      <td>0.024309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AreaShape_Eccentricity (Cytoplasm)</th>\n",
       "      <td>0.018494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RadialDistribution_MeanFrac_DNA_1of4 (Cells)</th>\n",
       "      <td>0.015521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Granularity_4_RNA (Cytoplasm)</th>\n",
       "      <td>0.014902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RadialDistribution_MeanFrac_DNA_3of4 (FilteredNuclei)</th>\n",
       "      <td>0.013432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intensity_IntegratedIntensity_DNA (Cells)</th>\n",
       "      <td>0.012178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intensity_IntegratedIntensity_DNA (FilteredNuclei)</th>\n",
       "      <td>0.011693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texture_DifferenceEntropy_MITO_3_01_256 (Cytoplasm)</th>\n",
       "      <td>0.007668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Granularity_3_ER (FilteredNuclei)</th>\n",
       "      <td>0.007197</td>\n",
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      "text/plain": [
       "                                                    importance\n",
       "Granularity_2_ER (FilteredNuclei)                     0.358767\n",
       "Granularity_4_ER (Cytoplasm)                          0.125132\n",
       "Granularity_2_RNA (FilteredNuclei)                    0.096380\n",
       "Texture_InverseDifferenceMoment_DNA_3_00_256 (C...    0.057619\n",
       "Granularity_3_RNA (Cells)                             0.044074\n",
       "Granularity_3_ER (Cells)                              0.029807\n",
       "RadialDistribution_FracAtD_DNA_2of4 (FilteredNu...    0.024309\n",
       "AreaShape_Eccentricity (Cytoplasm)                    0.018494\n",
       "RadialDistribution_MeanFrac_DNA_1of4 (Cells)          0.015521\n",
       "Granularity_4_RNA (Cytoplasm)                         0.014902\n",
       "RadialDistribution_MeanFrac_DNA_3of4 (FilteredN...    0.013432\n",
       "Intensity_IntegratedIntensity_DNA (Cells)             0.012178\n",
       "Intensity_IntegratedIntensity_DNA (FilteredNuclei)    0.011693\n",
       "Texture_DifferenceEntropy_MITO_3_01_256 (Cytopl...    0.007668\n",
       "Granularity_3_ER (FilteredNuclei)                     0.007197"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate the feature importance\n",
    "feature_importances = pd.DataFrame(rf.feature_importances_, index = X_train.columns, columns=['importance']).sort_values('importance', ascending=False)\n",
    "feature_importances.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 scores:  [0.87188091 0.91649292 0.89186148 0.91639581 0.90902414]\n",
      "Mean R2 score:  0.9011310506106159\n"
     ]
    }
   ],
   "source": [
    "#now perform cross-validation to get a better estimate of the model performance, and plot the predicted vs actual values\n",
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(rf, X, y, cv=5, scoring='r2')\n",
    "print('R2 scores: ', scores)\n",
    "print('Mean R2 score: ', scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
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     "text": [
      "Cross-validated R2 score:  0.9063935283331807\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#plot the predicted vs actual values, and display the regression line and R2 score\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "import plotly.express as px\n",
    "augmentedData = augmentedData.reset_index(drop=True)\n",
    "#randomize row order\n",
    "X = augmentedData.drop(columns=['Compound', 'Concentration', 'Viability','Count'])\n",
    "y = augmentedData['Viability']\n",
    "\n",
    "y_pred = cross_val_predict(rf, X, y, cv=5)\n",
    "# add back the compound and concentration columns\n",
    "y_pred = pd.DataFrame(y_pred, columns=['Viability'])\n",
    "y_pred = pd.concat([augmentedData[['Compound', 'Concentration']], y_pred], axis=1)\n",
    "augmentedData['Prediction'] = y_pred['Viability']\n",
    "\n",
    "fig = px.scatter(augmentedData, x=\"Viability\", y=\"Prediction\", trendline=\"ols\", title='Treat level (Cross-Validation)', hover_data=['Compound', 'Concentration'],\n",
    "                 height=1000, width=1000, trendline_scope = 'overall', color='Compound')\n",
    "fig.update_layout(title='Well level (Cross-Validation)',\n",
    "                     xaxis_title='Actual Viability',\n",
    "                     yaxis_title='Predicted Viability')\n",
    "\n",
    "\n",
    "fig.show()\n",
    "r2_cv = r2_score(augmentedData['Viability'], augmentedData['Prediction'])\n",
    "print('Cross-validated R2 score: ', r2_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
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       "      <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",
       "      <th>Viability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NCAP</td>\n",
       "      <td>30</td>\n",
       "      <td>657.2</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>...</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",
       "      <td>96.597931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NCAP</td>\n",
       "      <td>30</td>\n",
       "      <td>678.4</td>\n",
       "      <td>0.163440</td>\n",
       "      <td>-1.884026</td>\n",
       "      <td>1.008649</td>\n",
       "      <td>-1.811738</td>\n",
       "      <td>0.756886</td>\n",
       "      <td>-1.909827</td>\n",
       "      <td>1.086644</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.671342</td>\n",
       "      <td>-0.385313</td>\n",
       "      <td>-0.344760</td>\n",
       "      <td>-0.351456</td>\n",
       "      <td>-0.351843</td>\n",
       "      <td>-2.402328</td>\n",
       "      <td>-2.397610</td>\n",
       "      <td>-2.414436</td>\n",
       "      <td>-2.418905</td>\n",
       "      <td>96.597931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NCAP</td>\n",
       "      <td>30</td>\n",
       "      <td>653.6</td>\n",
       "      <td>1.585652</td>\n",
       "      <td>0.229826</td>\n",
       "      <td>-0.159741</td>\n",
       "      <td>0.724417</td>\n",
       "      <td>-0.341819</td>\n",
       "      <td>0.508658</td>\n",
       "      <td>-0.308365</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.972640</td>\n",
       "      <td>-0.798323</td>\n",
       "      <td>-0.780569</td>\n",
       "      <td>-0.789076</td>\n",
       "      <td>-0.805890</td>\n",
       "      <td>-1.666951</td>\n",
       "      <td>-1.688319</td>\n",
       "      <td>-1.653058</td>\n",
       "      <td>-1.656130</td>\n",
       "      <td>96.597931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NCAP</td>\n",
       "      <td>30</td>\n",
       "      <td>678.8</td>\n",
       "      <td>-0.088888</td>\n",
       "      <td>-0.496810</td>\n",
       "      <td>0.351430</td>\n",
       "      <td>-0.209956</td>\n",
       "      <td>-0.195325</td>\n",
       "      <td>-0.285052</td>\n",
       "      <td>0.082756</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.983882</td>\n",
       "      <td>-0.272515</td>\n",
       "      <td>-0.281053</td>\n",
       "      <td>-0.261342</td>\n",
       "      <td>-0.237133</td>\n",
       "      <td>-1.710806</td>\n",
       "      <td>-1.736869</td>\n",
       "      <td>-1.722915</td>\n",
       "      <td>-1.720094</td>\n",
       "      <td>96.597931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCAP</td>\n",
       "      <td>30</td>\n",
       "      <td>688.4</td>\n",
       "      <td>-1.406359</td>\n",
       "      <td>-2.269490</td>\n",
       "      <td>0.179625</td>\n",
       "      <td>-2.115838</td>\n",
       "      <td>0.544482</td>\n",
       "      <td>-2.251701</td>\n",
       "      <td>0.249832</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.903040</td>\n",
       "      <td>-0.101623</td>\n",
       "      <td>-0.147450</td>\n",
       "      <td>-0.117662</td>\n",
       "      <td>-0.062600</td>\n",
       "      <td>-2.288013</td>\n",
       "      <td>-2.290764</td>\n",
       "      <td>-2.365198</td>\n",
       "      <td>-2.329030</td>\n",
       "      <td>96.597931</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>3275</th>\n",
       "      <td>ZIDO</td>\n",
       "      <td>10</td>\n",
       "      <td>635.4</td>\n",
       "      <td>0.979674</td>\n",
       "      <td>0.297763</td>\n",
       "      <td>0.135141</td>\n",
       "      <td>0.589736</td>\n",
       "      <td>0.364955</td>\n",
       "      <td>0.513444</td>\n",
       "      <td>0.368862</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.707316</td>\n",
       "      <td>-0.998949</td>\n",
       "      <td>-1.043873</td>\n",
       "      <td>-1.021137</td>\n",
       "      <td>-1.048704</td>\n",
       "      <td>-1.536208</td>\n",
       "      <td>-1.549852</td>\n",
       "      <td>-1.536072</td>\n",
       "      <td>-1.525473</td>\n",
       "      <td>101.228239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3276</th>\n",
       "      <td>ZIDO</td>\n",
       "      <td>10</td>\n",
       "      <td>635.8</td>\n",
       "      <td>1.069310</td>\n",
       "      <td>0.596858</td>\n",
       "      <td>-0.184818</td>\n",
       "      <td>0.567377</td>\n",
       "      <td>-0.510049</td>\n",
       "      <td>0.450926</td>\n",
       "      <td>-0.368965</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.186714</td>\n",
       "      <td>-2.597369</td>\n",
       "      <td>-2.622536</td>\n",
       "      <td>-2.619828</td>\n",
       "      <td>-2.578199</td>\n",
       "      <td>-0.855171</td>\n",
       "      <td>-0.826253</td>\n",
       "      <td>-0.851512</td>\n",
       "      <td>-0.885756</td>\n",
       "      <td>101.228239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3277</th>\n",
       "      <td>ZIDO</td>\n",
       "      <td>10</td>\n",
       "      <td>638.2</td>\n",
       "      <td>0.318708</td>\n",
       "      <td>-0.198953</td>\n",
       "      <td>-1.145107</td>\n",
       "      <td>0.266263</td>\n",
       "      <td>-1.253440</td>\n",
       "      <td>0.030010</td>\n",
       "      <td>-1.335544</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.666207</td>\n",
       "      <td>-1.973629</td>\n",
       "      <td>-2.059334</td>\n",
       "      <td>-2.027878</td>\n",
       "      <td>-1.933142</td>\n",
       "      <td>-1.493846</td>\n",
       "      <td>-1.499028</td>\n",
       "      <td>-1.465447</td>\n",
       "      <td>-1.482572</td>\n",
       "      <td>101.228239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3278</th>\n",
       "      <td>ZIDO</td>\n",
       "      <td>10</td>\n",
       "      <td>624.8</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>...</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",
       "      <td>101.228239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3279</th>\n",
       "      <td>ZIDO</td>\n",
       "      <td>10</td>\n",
       "      <td>661.4</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>...</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",
       "      <td>101.228239</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3280 rows × 1628 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Compound  Concentration  Count  AreaShape_Area (Cells)   \n",
       "0        NCAP             30  657.2                0.484584  \\\n",
       "1        NCAP             30  678.4                0.163440   \n",
       "2        NCAP             30  653.6                1.585652   \n",
       "3        NCAP             30  678.8               -0.088888   \n",
       "4        NCAP             30  688.4               -1.406359   \n",
       "...       ...            ...    ...                     ...   \n",
       "3275     ZIDO             10  635.4                0.979674   \n",
       "3276     ZIDO             10  635.8                1.069310   \n",
       "3277     ZIDO             10  638.2                0.318708   \n",
       "3278     ZIDO             10  624.8                1.498225   \n",
       "3279     ZIDO             10  661.4               -0.217436   \n",
       "\n",
       "      AreaShape_BoundingBoxMaximum_X (Cells)   \n",
       "0                                   1.550984  \\\n",
       "1                                  -1.884026   \n",
       "2                                   0.229826   \n",
       "3                                  -0.496810   \n",
       "4                                  -2.269490   \n",
       "...                                      ...   \n",
       "3275                                0.297763   \n",
       "3276                                0.596858   \n",
       "3277                               -0.198953   \n",
       "3278                               -0.845549   \n",
       "3279                                0.795811   \n",
       "\n",
       "      AreaShape_BoundingBoxMaximum_Y (Cells)   \n",
       "0                                   1.081673  \\\n",
       "1                                   1.008649   \n",
       "2                                  -0.159741   \n",
       "3                                   0.351430   \n",
       "4                                   0.179625   \n",
       "...                                      ...   \n",
       "3275                                0.135141   \n",
       "3276                               -0.184818   \n",
       "3277                               -1.145107   \n",
       "3278                               -0.939331   \n",
       "3279                                0.226735   \n",
       "\n",
       "      AreaShape_BoundingBoxMinimum_X (Cells)   \n",
       "0                                   1.458567  \\\n",
       "1                                  -1.811738   \n",
       "2                                   0.724417   \n",
       "3                                  -0.209956   \n",
       "4                                  -2.115838   \n",
       "...                                      ...   \n",
       "3275                                0.589736   \n",
       "3276                                0.567377   \n",
       "3277                                0.266263   \n",
       "3278                               -0.938193   \n",
       "3279                                0.968863   \n",
       "\n",
       "      AreaShape_BoundingBoxMinimum_Y (Cells)  AreaShape_Center_X (Cells)   \n",
       "0                                   0.683639                    1.580247  \\\n",
       "1                                   0.756886                   -1.909827   \n",
       "2                                  -0.341819                    0.508658   \n",
       "3                                  -0.195325                   -0.285052   \n",
       "4                                   0.544482                   -2.251701   \n",
       "...                                      ...                         ...   \n",
       "3275                                0.364955                    0.513444   \n",
       "3276                               -0.510049                    0.450926   \n",
       "3277                               -1.253440                    0.030010   \n",
       "3278                               -0.510049                   -0.950975   \n",
       "3279                               -0.807405                    0.824993   \n",
       "\n",
       "      AreaShape_Center_Y (Cells)  ...   \n",
       "0                       1.136553  ...  \\\n",
       "1                       1.086644  ...   \n",
       "2                      -0.308365  ...   \n",
       "3                       0.082756  ...   \n",
       "4                       0.249832  ...   \n",
       "...                          ...  ...   \n",
       "3275                    0.368862  ...   \n",
       "3276                   -0.368965  ...   \n",
       "3277                   -1.335544  ...   \n",
       "3278                   -0.743367  ...   \n",
       "3279                   -0.324558  ...   \n",
       "\n",
       "      Texture_Variance_ER_3_03_256 (FilteredNuclei)   \n",
       "0                                         -2.561085  \\\n",
       "1                                         -2.671342   \n",
       "2                                         -1.972640   \n",
       "3                                         -1.983882   \n",
       "4                                         -0.903040   \n",
       "...                                             ...   \n",
       "3275                                      -1.707316   \n",
       "3276                                      -1.186714   \n",
       "3277                                      -1.666207   \n",
       "3278                                      -1.439188   \n",
       "3279                                      -1.788869   \n",
       "\n",
       "      Texture_Variance_MITO_3_00_256 (FilteredNuclei)   \n",
       "0                                           -0.017531  \\\n",
       "1                                           -0.385313   \n",
       "2                                           -0.798323   \n",
       "3                                           -0.272515   \n",
       "4                                           -0.101623   \n",
       "...                                               ...   \n",
       "3275                                        -0.998949   \n",
       "3276                                        -2.597369   \n",
       "3277                                        -1.973629   \n",
       "3278                                        -2.232664   \n",
       "3279                                        -2.272898   \n",
       "\n",
       "      Texture_Variance_MITO_3_01_256 (FilteredNuclei)   \n",
       "0                                            0.024250  \\\n",
       "1                                           -0.344760   \n",
       "2                                           -0.780569   \n",
       "3                                           -0.281053   \n",
       "4                                           -0.147450   \n",
       "...                                               ...   \n",
       "3275                                        -1.043873   \n",
       "3276                                        -2.622536   \n",
       "3277                                        -2.059334   \n",
       "3278                                        -2.199772   \n",
       "3279                                        -2.184796   \n",
       "\n",
       "      Texture_Variance_MITO_3_02_256 (FilteredNuclei)   \n",
       "0                                            0.012477  \\\n",
       "1                                           -0.351456   \n",
       "2                                           -0.789076   \n",
       "3                                           -0.261342   \n",
       "4                                           -0.117662   \n",
       "...                                               ...   \n",
       "3275                                        -1.021137   \n",
       "3276                                        -2.619828   \n",
       "3277                                        -2.027878   \n",
       "3278                                        -2.131945   \n",
       "3279                                        -2.197992   \n",
       "\n",
       "      Texture_Variance_MITO_3_03_256 (FilteredNuclei)   \n",
       "0                                           -0.002019  \\\n",
       "1                                           -0.351843   \n",
       "2                                           -0.805890   \n",
       "3                                           -0.237133   \n",
       "4                                           -0.062600   \n",
       "...                                               ...   \n",
       "3275                                        -1.048704   \n",
       "3276                                        -2.578199   \n",
       "3277                                        -1.933142   \n",
       "3278                                        -2.141157   \n",
       "3279                                        -2.173862   \n",
       "\n",
       "      Texture_Variance_RNA_3_00_256 (FilteredNuclei)   \n",
       "0                                          -2.246122  \\\n",
       "1                                          -2.402328   \n",
       "2                                          -1.666951   \n",
       "3                                          -1.710806   \n",
       "4                                          -2.288013   \n",
       "...                                              ...   \n",
       "3275                                       -1.536208   \n",
       "3276                                       -0.855171   \n",
       "3277                                       -1.493846   \n",
       "3278                                       -1.336880   \n",
       "3279                                       -1.799917   \n",
       "\n",
       "      Texture_Variance_RNA_3_01_256 (FilteredNuclei)   \n",
       "0                                          -2.237506  \\\n",
       "1                                          -2.397610   \n",
       "2                                          -1.688319   \n",
       "3                                          -1.736869   \n",
       "4                                          -2.290764   \n",
       "...                                              ...   \n",
       "3275                                       -1.549852   \n",
       "3276                                       -0.826253   \n",
       "3277                                       -1.499028   \n",
       "3278                                       -1.341865   \n",
       "3279                                       -1.751446   \n",
       "\n",
       "      Texture_Variance_RNA_3_02_256 (FilteredNuclei)   \n",
       "0                                          -2.245002  \\\n",
       "1                                          -2.414436   \n",
       "2                                          -1.653058   \n",
       "3                                          -1.722915   \n",
       "4                                          -2.365198   \n",
       "...                                              ...   \n",
       "3275                                       -1.536072   \n",
       "3276                                       -0.851512   \n",
       "3277                                       -1.465447   \n",
       "3278                                       -1.356299   \n",
       "3279                                       -1.758930   \n",
       "\n",
       "      Texture_Variance_RNA_3_03_256 (FilteredNuclei)   Viability  \n",
       "0                                          -2.266022   96.597931  \n",
       "1                                          -2.418905   96.597931  \n",
       "2                                          -1.656130   96.597931  \n",
       "3                                          -1.720094   96.597931  \n",
       "4                                          -2.329030   96.597931  \n",
       "...                                              ...         ...  \n",
       "3275                                       -1.525473  101.228239  \n",
       "3276                                       -0.885756  101.228239  \n",
       "3277                                       -1.482572  101.228239  \n",
       "3278                                       -1.328229  101.228239  \n",
       "3279                                       -1.773001  101.228239  \n",
       "\n",
       "[3280 rows x 1628 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run the same analysis without aggregation\n",
    "table_per_well2 = table_per_well.drop(columns=['Row', 'Column', 'PlateName'], errors='ignore')\n",
    "\n",
    "#join viability data to the table_per_well, on Compound and Concentration columns\n",
    "per_well_withviability = table_per_well2.merge(viability_table_per_treatment, on=['Compound', 'Concentration'])\n",
    "#drop Unnamed: 0, Plate, Well, all columns ending with _y, and rename 1s luciferase (CPS) to Viability\n",
    "per_well_withviability.rename(columns={'1s luciferase (CPS)': 'Viability'}, inplace=True)\n",
    "per_well_withviability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "dist_matrix:   8%|7         | 47/622 [07:59<1:37:46, 10.20s/it]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\Roman\\OneDrive - National University of Ireland, Galway\\Scripts\\CPA-viabilityPrediction.ipynb Cell 14\u001b[0m line \u001b[0;36m1\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/Roman/OneDrive%20-%20National%20University%20of%20Ireland%2C%20Galway/Scripts/CPA-viabilityPrediction.ipynb#X15sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m augmentedData2 \u001b[39m=\u001b[39m smogn\u001b[39m.\u001b[39;49msmoter(data \u001b[39m=\u001b[39;49m per_well_withviability, y \u001b[39m=\u001b[39;49m \u001b[39m\"\u001b[39;49m\u001b[39mViability\u001b[39;49m\u001b[39m\"\u001b[39;49m, rel_xtrm_type \u001b[39m=\u001b[39;49m \u001b[39m\"\u001b[39;49m\u001b[39mlow\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Roman/OneDrive%20-%20National%20University%20of%20Ireland%2C%20Galway/Scripts/CPA-viabilityPrediction.ipynb#X15sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m sns\u001b[39m.\u001b[39mkdeplot(per_well_withviability[\u001b[39m'\u001b[39m\u001b[39mViability\u001b[39m\u001b[39m'\u001b[39m], label \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mOriginal\u001b[39m\u001b[39m\"\u001b[39m, cut\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Roman/OneDrive%20-%20National%20University%20of%20Ireland%2C%20Galway/Scripts/CPA-viabilityPrediction.ipynb#X15sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m sns\u001b[39m.\u001b[39mkdeplot(augmentedData2[\u001b[39m'\u001b[39m\u001b[39mViability\u001b[39m\u001b[39m'\u001b[39m], label \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mSMOGN\u001b[39m\u001b[39m\"\u001b[39m,cut\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\Roman\\anaconda3\\envs\\bigfile\\Lib\\site-packages\\smogn\\smoter.py:240\u001b[0m, in \u001b[0;36msmoter\u001b[1;34m(data, y, k, pert, samp_method, under_samp, drop_na_col, drop_na_row, replace, rel_thres, rel_method, rel_xtrm_type, rel_coef, rel_ctrl_pts_rg)\u001b[0m\n\u001b[0;32m    234\u001b[0m \u001b[39m## over-sampling\u001b[39;00m\n\u001b[0;32m    235\u001b[0m \u001b[39mif\u001b[39;00m s_perc[i] \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[0;32m    236\u001b[0m     \n\u001b[0;32m    237\u001b[0m     \u001b[39m## generate synthetic observations in training set\u001b[39;00m\n\u001b[0;32m    238\u001b[0m     \u001b[39m## considered 'minority'\u001b[39;00m\n\u001b[0;32m    239\u001b[0m     \u001b[39m## (see 'over_sampling()' function for details)\u001b[39;00m\n\u001b[1;32m--> 240\u001b[0m     synth_obs \u001b[39m=\u001b[39m over_sampling(\n\u001b[0;32m    241\u001b[0m         data \u001b[39m=\u001b[39;49m data,\n\u001b[0;32m    242\u001b[0m         index \u001b[39m=\u001b[39;49m \u001b[39mlist\u001b[39;49m(b_index[i]\u001b[39m.\u001b[39;49mindex),\n\u001b[0;32m    243\u001b[0m         perc \u001b[39m=\u001b[39;49m s_perc[i],\n\u001b[0;32m    244\u001b[0m         pert \u001b[39m=\u001b[39;49m pert,\n\u001b[0;32m    245\u001b[0m         k \u001b[39m=\u001b[39;49m k\n\u001b[0;32m    246\u001b[0m     )\n\u001b[0;32m    248\u001b[0m     \u001b[39m## concatenate over-sampling\u001b[39;00m\n\u001b[0;32m    249\u001b[0m     \u001b[39m## results to modified training set\u001b[39;00m\n\u001b[0;32m    250\u001b[0m     data_new \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mconcat([synth_obs, data_new])\n",
      "File \u001b[1;32mc:\\Users\\Roman\\anaconda3\\envs\\bigfile\\Lib\\site-packages\\smogn\\over_sampling.py:179\u001b[0m, in \u001b[0;36mover_sampling\u001b[1;34m(data, index, perc, pert, k)\u001b[0m\n\u001b[0;32m    175\u001b[0m \u001b[39m## utilize heom distance given that \u001b[39;00m\n\u001b[0;32m    176\u001b[0m \u001b[39m## data contains both numeric / continuous \u001b[39;00m\n\u001b[0;32m    177\u001b[0m \u001b[39m## and nominal / categorical\u001b[39;00m\n\u001b[0;32m    178\u001b[0m \u001b[39mif\u001b[39;00m feat_count_nom \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m \u001b[39mand\u001b[39;00m feat_count_num \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[1;32m--> 179\u001b[0m     dist_matrix[i][j] \u001b[39m=\u001b[39m heom_dist(\n\u001b[0;32m    180\u001b[0m         \n\u001b[0;32m    181\u001b[0m         \u001b[39m## numeric inputs\u001b[39;49;00m\n\u001b[0;32m    182\u001b[0m         a_num \u001b[39m=\u001b[39;49m data_num\u001b[39m.\u001b[39;49miloc[i],\n\u001b[0;32m    183\u001b[0m         b_num \u001b[39m=\u001b[39;49m data_num\u001b[39m.\u001b[39;49miloc[j],\n\u001b[0;32m    184\u001b[0m         d_num \u001b[39m=\u001b[39;49m feat_count_num,\n\u001b[0;32m    185\u001b[0m         ranges_num \u001b[39m=\u001b[39;49m feat_ranges_num,\n\u001b[0;32m    186\u001b[0m         \n\u001b[0;32m    187\u001b[0m         \u001b[39m## nominal inputs\u001b[39;49;00m\n\u001b[0;32m    188\u001b[0m         a_nom \u001b[39m=\u001b[39;49m data_nom\u001b[39m.\u001b[39;49miloc[i],\n\u001b[0;32m    189\u001b[0m         b_nom \u001b[39m=\u001b[39;49m data_nom\u001b[39m.\u001b[39;49miloc[j],\n\u001b[0;32m    190\u001b[0m         d_nom \u001b[39m=\u001b[39;49m feat_count_nom\n\u001b[0;32m    191\u001b[0m     )\n\u001b[0;32m    193\u001b[0m \u001b[39m## utilize hamming distance given that \u001b[39;00m\n\u001b[0;32m    194\u001b[0m \u001b[39m## data is all nominal / categorical\u001b[39;00m\n\u001b[0;32m    195\u001b[0m \u001b[39mif\u001b[39;00m feat_count_num \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m:\n",
      "File \u001b[1;32mc:\\Users\\Roman\\anaconda3\\envs\\bigfile\\Lib\\site-packages\\smogn\\dist_metrics.py:64\u001b[0m, in \u001b[0;36mheom_dist\u001b[1;34m(a_num, b_num, d_num, ranges_num, a_nom, b_nom, d_nom)\u001b[0m\n\u001b[0;32m     56\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(d_num):\n\u001b[0;32m     57\u001b[0m     \n\u001b[0;32m     58\u001b[0m     \u001b[39m## epsilon utilized to avoid division by zero\u001b[39;00m\n\u001b[0;32m     59\u001b[0m     \u001b[39mif\u001b[39;00m ranges_num[i] \u001b[39m>\u001b[39m eps:\n\u001b[0;32m     60\u001b[0m     \n\u001b[0;32m     61\u001b[0m         \u001b[39m## the absolute value of the differences between values in\u001b[39;00m\n\u001b[0;32m     62\u001b[0m         \u001b[39m## vectors a and b of equal length, divided by their range, squared\u001b[39;00m\n\u001b[0;32m     63\u001b[0m         \u001b[39m## (division by range conducted for normalization)\u001b[39;00m\n\u001b[1;32m---> 64\u001b[0m         dist[i] \u001b[39m=\u001b[39m (\u001b[39mabs\u001b[39m(a_num\u001b[39m.\u001b[39;49miloc[i] \u001b[39m-\u001b[39m b_num\u001b[39m.\u001b[39miloc[i]) \u001b[39m/\u001b[39m ranges_num[i]) \u001b[39m*\u001b[39m\u001b[39m*\u001b[39m \u001b[39m2\u001b[39m\n\u001b[0;32m     66\u001b[0m \u001b[39m## loop through columns to calculate hamming\u001b[39;00m\n\u001b[0;32m     67\u001b[0m \u001b[39m## distance for nominal / categorical features\u001b[39;00m\n\u001b[0;32m     68\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(d_nom):\n\u001b[0;32m     69\u001b[0m     \n\u001b[0;32m     70\u001b[0m     \u001b[39m## distance equals 0 for values that are equal\u001b[39;00m\n\u001b[0;32m     71\u001b[0m     \u001b[39m## in two vectors a and b of equal length\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\Roman\\anaconda3\\envs\\bigfile\\Lib\\site-packages\\pandas\\core\\indexing.py:1103\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1100\u001b[0m axis \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maxis \u001b[39mor\u001b[39;00m \u001b[39m0\u001b[39m\n\u001b[0;32m   1102\u001b[0m maybe_callable \u001b[39m=\u001b[39m com\u001b[39m.\u001b[39mapply_if_callable(key, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mobj)\n\u001b[1;32m-> 1103\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_getitem_axis(maybe_callable, axis\u001b[39m=\u001b[39;49maxis)\n",
      "File \u001b[1;32mc:\\Users\\Roman\\anaconda3\\envs\\bigfile\\Lib\\site-packages\\pandas\\core\\indexing.py:1641\u001b[0m, in \u001b[0;36m_iLocIndexer._getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1638\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(key, \u001b[39mlist\u001b[39m):\n\u001b[0;32m   1639\u001b[0m     key \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39masarray(key)\n\u001b[1;32m-> 1641\u001b[0m \u001b[39mif\u001b[39;00m com\u001b[39m.\u001b[39;49mis_bool_indexer(key):\n\u001b[0;32m   1642\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_validate_key(key, axis)\n\u001b[0;32m   1643\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_getbool_axis(key, axis\u001b[39m=\u001b[39maxis)\n",
      "File \u001b[1;32mc:\\Users\\Roman\\anaconda3\\envs\\bigfile\\Lib\\site-packages\\pandas\\core\\common.py:123\u001b[0m, in \u001b[0;36mis_bool_indexer\u001b[1;34m(key)\u001b[0m\n\u001b[0;32m     95\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mis_bool_indexer\u001b[39m(key: Any) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mbool\u001b[39m:\n\u001b[0;32m     96\u001b[0m     \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m     97\u001b[0m \u001b[39m    Check whether `key` is a valid boolean indexer.\u001b[39;00m\n\u001b[0;32m     98\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    121\u001b[0m \u001b[39m        and convert to an ndarray.\u001b[39;00m\n\u001b[0;32m    122\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 123\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(key, (ABCSeries, np\u001b[39m.\u001b[39mndarray, ABCIndex)) \u001b[39mor\u001b[39;00m (\n\u001b[0;32m    124\u001b[0m         is_array_like(key) \u001b[39mand\u001b[39;00m is_extension_array_dtype(key\u001b[39m.\u001b[39mdtype)\n\u001b[0;32m    125\u001b[0m     ):\n\u001b[0;32m    126\u001b[0m         \u001b[39mif\u001b[39;00m key\u001b[39m.\u001b[39mdtype \u001b[39m==\u001b[39m np\u001b[39m.\u001b[39mobject_:\n\u001b[0;32m    127\u001b[0m             key_array \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39masarray(key)\n",
      "File \u001b[1;32mc:\\Users\\Roman\\anaconda3\\envs\\bigfile\\Lib\\site-packages\\pandas\\core\\dtypes\\generic.py:44\u001b[0m, in \u001b[0;36mcreate_pandas_abc_type.<locals>._instancecheck\u001b[1;34m(cls, inst)\u001b[0m\n\u001b[0;32m     42\u001b[0m \u001b[39m@classmethod\u001b[39m  \u001b[39m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m     43\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_instancecheck\u001b[39m(\u001b[39mcls\u001b[39m, inst) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mbool\u001b[39m:\n\u001b[1;32m---> 44\u001b[0m     \u001b[39mreturn\u001b[39;00m _check(inst) \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39misinstance\u001b[39m(inst, \u001b[39mtype\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\Roman\\anaconda3\\envs\\bigfile\\Lib\\site-packages\\pandas\\core\\dtypes\\generic.py:37\u001b[0m, in \u001b[0;36mcreate_pandas_abc_type.<locals>._check\u001b[1;34m(inst)\u001b[0m\n\u001b[0;32m     36\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mcreate_pandas_abc_type\u001b[39m(name, attr, comp):\n\u001b[1;32m---> 37\u001b[0m     \u001b[39mdef\u001b[39;00m \u001b[39m_check\u001b[39m(inst) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mbool\u001b[39m:\n\u001b[0;32m     38\u001b[0m         \u001b[39mreturn\u001b[39;00m \u001b[39mgetattr\u001b[39m(inst, attr, \u001b[39m\"\u001b[39m\u001b[39m_typ\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39min\u001b[39;00m comp\n\u001b[0;32m     40\u001b[0m     \u001b[39m# https://github.com/python/mypy/issues/1006\u001b[39;00m\n\u001b[0;32m     41\u001b[0m     \u001b[39m# error: 'classmethod' used with a non-method\u001b[39;00m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "augmentedData2 = smogn.smoter(data = per_well_withviability, y = \"Viability\", rel_xtrm_type = \"low\")\n",
    "sns.kdeplot(per_well_withviability['Viability'], label = \"Original\", cut=0)\n",
    "sns.kdeplot(augmentedData2['Viability'], label = \"SMOGN\",cut=0)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error:  13.086567522067755\n",
      "R2 score:  0.8928685729193934\n"
     ]
    }
   ],
   "source": [
    "#train a random forest regressor to predict viability from the other features\n",
    "#split the data into train and test sets\n",
    "curatedData = drop_correlated_columns(per_well_withviability.query('Viability < 97'), 0.9, numeric=True)\n",
    "X2 = curatedData.drop(columns=['Compound', 'Concentration', 'Viability','Count']).dropna(axis=1)\n",
    "y2 = curatedData['Viability']\n",
    "X_train2, X_test2, y_train2, y_test2 = train_test_split(X2, y2, test_size=0.1, random_state=111)\n",
    "\n",
    "#train the model\n",
    "rf2 = RandomForestRegressor(n_estimators=100, random_state=72)\n",
    "rf2.fit(X_train2, y_train2)\n",
    "\n",
    "#predict the viability values for the test set\n",
    "y_pred2 = rf2.predict(X_test2)\n",
    "\n",
    "#calculate the mean squared error and the R2 score\n",
    "mse2 = mean_squared_error(y_test2, y_pred2)\n",
    "r22 = r2_score(y_test2, y_pred2)\n",
    "print('Mean squared error: ', mse)\n",
    "print('R2 score: ', r22)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#add the regression line to the plot, make the plot square\n",
    "plt.scatter(y_test2, y_pred2 )\n",
    "plt.xlabel('Actual Viability')\n",
    "plt.ylabel('Predicted Viability')\n",
    "plt.title('Predicted Viability vs Actual Viability')\n",
    "plt.plot([0, 1], [0, 1], transform=plt.gca().transAxes, ls=\"--\", c=\".3\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cytoplasm 488 Profile 4/5 SER-Edge</th>\n",
       "      <td>0.350247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nucleus 33342 Profile 5/5</th>\n",
       "      <td>0.178366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cytoplasm Mito Profile 4/5 SER-Bright</th>\n",
       "      <td>0.123103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cytoplasm 33342 Profile 3/5 SER-Hole</th>\n",
       "      <td>0.016715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cytoplasm 33342 Profile 3/5 SER-Edge</th>\n",
       "      <td>0.012692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cytoplasm 488 Profile 3/5 SER-Dark</th>\n",
       "      <td>0.011226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell 33342 Radial Mean</th>\n",
       "      <td>0.011040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell 488 Radial Mean Ratio SER-Dark</th>\n",
       "      <td>0.009542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intensity Cytoplasm Alexa 568 Mean</th>\n",
       "      <td>0.008704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nucleus 33342 Radial Mean Ratio SER-Bright</th>\n",
       "      <td>0.008264</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            importance\n",
       "Cytoplasm 488 Profile 4/5 SER-Edge            0.350247\n",
       "Nucleus 33342 Profile 5/5                     0.178366\n",
       "Cytoplasm Mito Profile 4/5 SER-Bright         0.123103\n",
       "Cytoplasm 33342 Profile 3/5 SER-Hole          0.016715\n",
       "Cytoplasm 33342 Profile 3/5 SER-Edge          0.012692\n",
       "Cytoplasm 488 Profile 3/5 SER-Dark            0.011226\n",
       "Cell 33342 Radial Mean                        0.011040\n",
       "Cell 488 Radial Mean Ratio SER-Dark           0.009542\n",
       "Intensity Cytoplasm Alexa 568 Mean            0.008704\n",
       "Nucleus 33342 Radial Mean Ratio SER-Bright    0.008264"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances2 = pd.DataFrame(rf2.feature_importances_, index = X_train2.columns, columns=['importance']).sort_values('importance', ascending=False)\n",
    "feature_importances2.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation scores:  [0.88944951 0.83283827 0.87190141 0.89354614 0.86880426]\n",
      "Mean score:  0.8713079173678405\n",
      "Standard deviation:  0.021495068675252357\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "cv_scores = cross_val_score(rf2, X2, y2, cv=ShuffleSplit(n_splits=5, test_size=0.1, random_state=0))\n",
    "\n",
    "print(\"Cross-validation scores: \", cv_scores)\n",
    "print(\"Mean score: \", np.mean(cv_scores))\n",
    "print(\"Standard deviation: \", np.std(cv_scores))\n"
   ]
  }
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