unsupervised learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ed35440d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import all packages and set plots to be embedded inline\n",
    "import numpy as np\n",
    "import sklearn.gaussian_process as gp\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import os\n",
    "from sklearn.gaussian_process import GaussianProcessRegressor\n",
    "from sklearn.gaussian_process.kernels import RBF\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import LeaveOneOut, cross_val_score\n",
    "import plotly.express as px\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fdb5e9e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "    X = pd.read_csv('descriptor_data.csv') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "63533dad",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Metal</th>\n",
       "      <th>Atom_A</th>\n",
       "      <th>Atom_B</th>\n",
       "      <th>A-M-B Angle</th>\n",
       "      <th>M-A Distance</th>\n",
       "      <th>M-B Distance</th>\n",
       "      <th>Metal_NBO</th>\n",
       "      <th>A_NBO</th>\n",
       "      <th>B_NBO</th>\n",
       "      <th>...</th>\n",
       "      <th>% VBur asymm 2</th>\n",
       "      <th>% VBur asymm 3</th>\n",
       "      <th>% VBur octant 1</th>\n",
       "      <th>% VBur octant 2</th>\n",
       "      <th>% VBur octant 3</th>\n",
       "      <th>% VBur octant 4</th>\n",
       "      <th>% VBur octant 5</th>\n",
       "      <th>% VBur octant 6</th>\n",
       "      <th>% VBur octant 7</th>\n",
       "      <th>% VBur octant 8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ligand-001-n-n-1</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>22</td>\n",
       "      <td>80.01004</td>\n",
       "      <td>2.324</td>\n",
       "      <td>2.324</td>\n",
       "      <td>0.26015</td>\n",
       "      <td>-0.42686</td>\n",
       "      <td>-0.42686</td>\n",
       "      <td>...</td>\n",
       "      <td>0.067555</td>\n",
       "      <td>0.051249</td>\n",
       "      <td>79.276929</td>\n",
       "      <td>75.605665</td>\n",
       "      <td>79.304883</td>\n",
       "      <td>75.750093</td>\n",
       "      <td>4.034663</td>\n",
       "      <td>0.242266</td>\n",
       "      <td>3.866940</td>\n",
       "      <td>0.228289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ligand-002-p-p-1</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>81.08750</td>\n",
       "      <td>2.404</td>\n",
       "      <td>2.406</td>\n",
       "      <td>-0.11874</td>\n",
       "      <td>1.06691</td>\n",
       "      <td>1.06830</td>\n",
       "      <td>...</td>\n",
       "      <td>5.049152</td>\n",
       "      <td>3.262439</td>\n",
       "      <td>81.564480</td>\n",
       "      <td>92.051808</td>\n",
       "      <td>83.060007</td>\n",
       "      <td>82.575475</td>\n",
       "      <td>5.101565</td>\n",
       "      <td>8.190458</td>\n",
       "      <td>6.671636</td>\n",
       "      <td>0.535781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ligand-003-p-p-2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>88.85482</td>\n",
       "      <td>2.393</td>\n",
       "      <td>2.398</td>\n",
       "      <td>-0.12498</td>\n",
       "      <td>1.08763</td>\n",
       "      <td>1.08260</td>\n",
       "      <td>...</td>\n",
       "      <td>0.099003</td>\n",
       "      <td>0.173546</td>\n",
       "      <td>74.021618</td>\n",
       "      <td>74.375699</td>\n",
       "      <td>95.387626</td>\n",
       "      <td>96.706113</td>\n",
       "      <td>8.866008</td>\n",
       "      <td>10.035408</td>\n",
       "      <td>10.860045</td>\n",
       "      <td>10.669027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ligand-004-p-p-3</td>\n",
       "      <td>3</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>95.97953</td>\n",
       "      <td>2.432</td>\n",
       "      <td>2.441</td>\n",
       "      <td>-0.09320</td>\n",
       "      <td>1.06382</td>\n",
       "      <td>1.05890</td>\n",
       "      <td>...</td>\n",
       "      <td>0.822307</td>\n",
       "      <td>1.202013</td>\n",
       "      <td>97.623928</td>\n",
       "      <td>96.230898</td>\n",
       "      <td>70.308423</td>\n",
       "      <td>71.878494</td>\n",
       "      <td>11.675363</td>\n",
       "      <td>12.486023</td>\n",
       "      <td>18.496086</td>\n",
       "      <td>13.054417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ligand-005-c-n-1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>59</td>\n",
       "      <td>86.57124</td>\n",
       "      <td>2.143</td>\n",
       "      <td>2.295</td>\n",
       "      <td>0.17756</td>\n",
       "      <td>0.22924</td>\n",
       "      <td>-0.40636</td>\n",
       "      <td>...</td>\n",
       "      <td>15.149786</td>\n",
       "      <td>30.368291</td>\n",
       "      <td>64.340291</td>\n",
       "      <td>88.292024</td>\n",
       "      <td>40.775252</td>\n",
       "      <td>96.850540</td>\n",
       "      <td>44.781960</td>\n",
       "      <td>9.821096</td>\n",
       "      <td>17.815878</td>\n",
       "      <td>11.330600</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>372</th>\n",
       "      <td>ligand-407-p-o-138</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>89.32942</td>\n",
       "      <td>2.459</td>\n",
       "      <td>2.263</td>\n",
       "      <td>0.18682</td>\n",
       "      <td>1.04370</td>\n",
       "      <td>-0.96597</td>\n",
       "      <td>...</td>\n",
       "      <td>8.995294</td>\n",
       "      <td>0.404165</td>\n",
       "      <td>98.401975</td>\n",
       "      <td>85.571189</td>\n",
       "      <td>63.939620</td>\n",
       "      <td>73.788669</td>\n",
       "      <td>9.793142</td>\n",
       "      <td>0.843272</td>\n",
       "      <td>26.481551</td>\n",
       "      <td>30.833023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>ligand-408-p-o-139</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>89.17718</td>\n",
       "      <td>2.456</td>\n",
       "      <td>2.264</td>\n",
       "      <td>0.18774</td>\n",
       "      <td>1.04308</td>\n",
       "      <td>-0.96762</td>\n",
       "      <td>...</td>\n",
       "      <td>8.058843</td>\n",
       "      <td>0.998183</td>\n",
       "      <td>98.541744</td>\n",
       "      <td>85.613120</td>\n",
       "      <td>64.480060</td>\n",
       "      <td>73.467201</td>\n",
       "      <td>9.900298</td>\n",
       "      <td>0.773388</td>\n",
       "      <td>30.506895</td>\n",
       "      <td>31.699590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>ligand-409-p-o-140</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>89.05786</td>\n",
       "      <td>2.465</td>\n",
       "      <td>2.263</td>\n",
       "      <td>0.19732</td>\n",
       "      <td>1.03890</td>\n",
       "      <td>-0.96986</td>\n",
       "      <td>...</td>\n",
       "      <td>8.174152</td>\n",
       "      <td>4.679929</td>\n",
       "      <td>96.706113</td>\n",
       "      <td>86.311964</td>\n",
       "      <td>66.912039</td>\n",
       "      <td>81.387439</td>\n",
       "      <td>11.894335</td>\n",
       "      <td>0.661573</td>\n",
       "      <td>30.138837</td>\n",
       "      <td>26.733135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>ligand-411-p-o-95</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>77.82640</td>\n",
       "      <td>2.418</td>\n",
       "      <td>2.292</td>\n",
       "      <td>0.19136</td>\n",
       "      <td>1.04202</td>\n",
       "      <td>-1.08516</td>\n",
       "      <td>...</td>\n",
       "      <td>9.620760</td>\n",
       "      <td>14.170239</td>\n",
       "      <td>82.389117</td>\n",
       "      <td>89.116660</td>\n",
       "      <td>64.591875</td>\n",
       "      <td>97.414275</td>\n",
       "      <td>22.218599</td>\n",
       "      <td>7.459001</td>\n",
       "      <td>19.166977</td>\n",
       "      <td>16.795565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>ligand-412-p-o-97</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>91.74673</td>\n",
       "      <td>2.407</td>\n",
       "      <td>2.310</td>\n",
       "      <td>0.15940</td>\n",
       "      <td>1.01806</td>\n",
       "      <td>-1.06287</td>\n",
       "      <td>...</td>\n",
       "      <td>12.444092</td>\n",
       "      <td>8.896291</td>\n",
       "      <td>85.328923</td>\n",
       "      <td>53.340477</td>\n",
       "      <td>90.486396</td>\n",
       "      <td>93.943347</td>\n",
       "      <td>11.405143</td>\n",
       "      <td>7.766493</td>\n",
       "      <td>0.633619</td>\n",
       "      <td>11.325941</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>377 rows × 87 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Name  Metal  Atom_A  Atom_B  A-M-B Angle  M-A Distance  \\\n",
       "0      ligand-001-n-n-1      2      23      22     80.01004         2.324   \n",
       "1      ligand-002-p-p-1      2      16      17     81.08750         2.404   \n",
       "2      ligand-003-p-p-2      3       1      12     88.85482         2.393   \n",
       "3      ligand-004-p-p-3      3      45       1     95.97953         2.432   \n",
       "4      ligand-005-c-n-1      1       2      59     86.57124         2.143   \n",
       "..                  ...    ...     ...     ...          ...           ...   \n",
       "372  ligand-407-p-o-138     34       1       5     89.32942         2.459   \n",
       "373  ligand-408-p-o-139     34       1       5     89.17718         2.456   \n",
       "374  ligand-409-p-o-140     34       1       5     89.05786         2.465   \n",
       "375   ligand-411-p-o-95     13       1       4     77.82640         2.418   \n",
       "376   ligand-412-p-o-97     13       1       4     91.74673         2.407   \n",
       "\n",
       "     M-B Distance  Metal_NBO    A_NBO    B_NBO  ...  % VBur asymm 2  \\\n",
       "0           2.324    0.26015 -0.42686 -0.42686  ...        0.067555   \n",
       "1           2.406   -0.11874  1.06691  1.06830  ...        5.049152   \n",
       "2           2.398   -0.12498  1.08763  1.08260  ...        0.099003   \n",
       "3           2.441   -0.09320  1.06382  1.05890  ...        0.822307   \n",
       "4           2.295    0.17756  0.22924 -0.40636  ...       15.149786   \n",
       "..            ...        ...      ...      ...  ...             ...   \n",
       "372         2.263    0.18682  1.04370 -0.96597  ...        8.995294   \n",
       "373         2.264    0.18774  1.04308 -0.96762  ...        8.058843   \n",
       "374         2.263    0.19732  1.03890 -0.96986  ...        8.174152   \n",
       "375         2.292    0.19136  1.04202 -1.08516  ...        9.620760   \n",
       "376         2.310    0.15940  1.01806 -1.06287  ...       12.444092   \n",
       "\n",
       "     % VBur asymm 3  % VBur octant 1  % VBur octant 2  % VBur octant 3  \\\n",
       "0          0.051249        79.276929        75.605665        79.304883   \n",
       "1          3.262439        81.564480        92.051808        83.060007   \n",
       "2          0.173546        74.021618        74.375699        95.387626   \n",
       "3          1.202013        97.623928        96.230898        70.308423   \n",
       "4         30.368291        64.340291        88.292024        40.775252   \n",
       "..              ...              ...              ...              ...   \n",
       "372        0.404165        98.401975        85.571189        63.939620   \n",
       "373        0.998183        98.541744        85.613120        64.480060   \n",
       "374        4.679929        96.706113        86.311964        66.912039   \n",
       "375       14.170239        82.389117        89.116660        64.591875   \n",
       "376        8.896291        85.328923        53.340477        90.486396   \n",
       "\n",
       "     % VBur octant 4  % VBur octant 5  % VBur octant 6  % VBur octant 7  \\\n",
       "0          75.750093         4.034663         0.242266         3.866940   \n",
       "1          82.575475         5.101565         8.190458         6.671636   \n",
       "2          96.706113         8.866008        10.035408        10.860045   \n",
       "3          71.878494        11.675363        12.486023        18.496086   \n",
       "4          96.850540        44.781960         9.821096        17.815878   \n",
       "..               ...              ...              ...              ...   \n",
       "372        73.788669         9.793142         0.843272        26.481551   \n",
       "373        73.467201         9.900298         0.773388        30.506895   \n",
       "374        81.387439        11.894335         0.661573        30.138837   \n",
       "375        97.414275        22.218599         7.459001        19.166977   \n",
       "376        93.943347        11.405143         7.766493         0.633619   \n",
       "\n",
       "     % VBur octant 8  \n",
       "0           0.228289  \n",
       "1           0.535781  \n",
       "2          10.669027  \n",
       "3          13.054417  \n",
       "4          11.330600  \n",
       "..               ...  \n",
       "372        30.833023  \n",
       "373        31.699590  \n",
       "374        26.733135  \n",
       "375        16.795565  \n",
       "376        11.325941  \n",
       "\n",
       "[377 rows x 87 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "190130d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Rows with missing values:\n",
      "Empty DataFrame\n",
      "Columns: [Name, Metal, Atom_A, Atom_B, A-M-B Angle, M-A Distance, M-B Distance, Metal_NBO, A_NBO, B_NBO, HOMO_LUMO, A-B_NBO, NBO-dist-2-avg, NBO-dist-3-avg, NBO-dist-4-avg, NBO-dist-2-a-avg, NBO-dist-3-a-avg, NBO-dist-4-a-avg, NBO-dist-2-b-avg, NBO-dist-3-b-avg, NBO-dist-4-b-avg, NBO-dist-2-a-b-diff, NBO-dist-3-a-b-diff, NBO-dist-4-a-b-diff, NBO-dist-2-min, NBO-dist-3-min, NBO-dist-4-min, NBO-dist-2-max, NBO-dist-3-max, NBO-dist-4-max, NBO-dist-2-a-min, NBO-dist-3-a-min, NBO-dist-4-a-min, NBO-dist-2-b-min, NBO-dist-3-b-min, NBO-dist-4-b-min, NBO-dist-2-a-max, NBO-dist-3-a-max, NBO-dist-4-a-max, NBO-dist-2-b-max, NBO-dist-3-b-max, NBO-dist-4-b-max, NBO-metal-donor-diff, NBO-metal-a-diff, NBO-metal-b-diff, NBO-dist-2-1-avg-diff, NBO-dist-3-2-avg-diff, NBO-dist-4-3-avg-diff, NBO-dist-2-1-a-avg-diff, NBO-dist-3-2-a-avg-diff, NBO-dist-4-3-a-avg-diff, NBO-dist-2-1-b-avg-diff, NBO-dist-3-2-b-avg-diff, NBO-dist-4-3-b-avg-diff, Metal_mullkien, A_mulliken, B_mulliken, A-B_mulliken, Mulliken-dist-2-avg, Mulliken-dist-3-avg, Mulliken-dist-4-avg, Total internal angle deviation %, Total % VBur, % VBur SE, % VBur SW, % VBur NE, % VBur NW, % VBur max, % VBur min, % VBur max min diff, % VBur NE NW diff, % VBur SE SW diff, % VBur NE SE diff, % VBur NW SW diff, % VBur NE SW diff, % VBur NW SE diff, % VBur asymm 1, % VBur asymm 2, % VBur asymm 3, % VBur octant 1, % VBur octant 2, % VBur octant 3, % VBur octant 4, % VBur octant 5, % VBur octant 6, % VBur octant 7, % VBur octant 8]\n",
      "Index: []\n",
      "\n",
      "[0 rows x 87 columns]\n"
     ]
    }
   ],
   "source": [
    "# Create a boolean mask where any column contains the string 'NaN'\n",
    "mask = X.apply(lambda x: x.astype(str).str.contains('NaN', na=False)).any(axis=1)\n",
    "\n",
    "# Get the indices of the rows where the mask is True\n",
    "nan_indices = X[mask].index\n",
    "missing_values = X.isnull()\n",
    "\n",
    "# Get the indices of rows with missing values\n",
    "rows_with_missing = X[X.isnull().any(axis=1)]\n",
    "\n",
    "# Print the rows with missing values\n",
    "print(\"\\nRows with missing values:\")\n",
    "print(rows_with_missing)\n",
    "\n",
    "# Get the indices of rows with missing values\n",
    "missing_indices = rows_with_missing.index.tolist()\n",
    "\n",
    "# Remove that indices by dropping\n",
    "X = X.dropna()\n",
    "\n",
    "# Add two columns, one with ligand_id, one with donor_atom_combination\n",
    "# Extract the 3-digit ligand ID and the donor atom combination\n",
    "X['ligand_id'] = X['Name'].str.extract(r'ligand-(\\d{3})')\n",
    "X['donor_atom_comb'] = X['Name'].str.extract(r'ligand-\\d{3}-([a-z]-[a-z])')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "24a5590e",
   "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>Name</th>\n",
       "      <th>Metal</th>\n",
       "      <th>Atom_A</th>\n",
       "      <th>Atom_B</th>\n",
       "      <th>A-M-B Angle</th>\n",
       "      <th>M-A Distance</th>\n",
       "      <th>M-B Distance</th>\n",
       "      <th>Metal_NBO</th>\n",
       "      <th>A_NBO</th>\n",
       "      <th>B_NBO</th>\n",
       "      <th>...</th>\n",
       "      <th>% VBur octant 1</th>\n",
       "      <th>% VBur octant 2</th>\n",
       "      <th>% VBur octant 3</th>\n",
       "      <th>% VBur octant 4</th>\n",
       "      <th>% VBur octant 5</th>\n",
       "      <th>% VBur octant 6</th>\n",
       "      <th>% VBur octant 7</th>\n",
       "      <th>% VBur octant 8</th>\n",
       "      <th>ligand_id</th>\n",
       "      <th>donor_atom_comb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ligand-002-p-p-1</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>81.08750</td>\n",
       "      <td>2.404</td>\n",
       "      <td>2.406</td>\n",
       "      <td>-0.11874</td>\n",
       "      <td>1.06691</td>\n",
       "      <td>1.06830</td>\n",
       "      <td>...</td>\n",
       "      <td>81.564480</td>\n",
       "      <td>92.051808</td>\n",
       "      <td>83.060007</td>\n",
       "      <td>82.575475</td>\n",
       "      <td>5.101565</td>\n",
       "      <td>8.190458</td>\n",
       "      <td>6.671636</td>\n",
       "      <td>0.535781</td>\n",
       "      <td>002</td>\n",
       "      <td>p-p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ligand-003-p-p-2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>88.85482</td>\n",
       "      <td>2.393</td>\n",
       "      <td>2.398</td>\n",
       "      <td>-0.12498</td>\n",
       "      <td>1.08763</td>\n",
       "      <td>1.08260</td>\n",
       "      <td>...</td>\n",
       "      <td>74.021618</td>\n",
       "      <td>74.375699</td>\n",
       "      <td>95.387626</td>\n",
       "      <td>96.706113</td>\n",
       "      <td>8.866008</td>\n",
       "      <td>10.035408</td>\n",
       "      <td>10.860045</td>\n",
       "      <td>10.669027</td>\n",
       "      <td>003</td>\n",
       "      <td>p-p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ligand-004-p-p-3</td>\n",
       "      <td>3</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>95.97953</td>\n",
       "      <td>2.432</td>\n",
       "      <td>2.441</td>\n",
       "      <td>-0.09320</td>\n",
       "      <td>1.06382</td>\n",
       "      <td>1.05890</td>\n",
       "      <td>...</td>\n",
       "      <td>97.623928</td>\n",
       "      <td>96.230898</td>\n",
       "      <td>70.308423</td>\n",
       "      <td>71.878494</td>\n",
       "      <td>11.675363</td>\n",
       "      <td>12.486023</td>\n",
       "      <td>18.496086</td>\n",
       "      <td>13.054417</td>\n",
       "      <td>004</td>\n",
       "      <td>p-p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ligand-005-c-n-1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>59</td>\n",
       "      <td>86.57124</td>\n",
       "      <td>2.143</td>\n",
       "      <td>2.295</td>\n",
       "      <td>0.17756</td>\n",
       "      <td>0.22924</td>\n",
       "      <td>-0.40636</td>\n",
       "      <td>...</td>\n",
       "      <td>64.340291</td>\n",
       "      <td>88.292024</td>\n",
       "      <td>40.775252</td>\n",
       "      <td>96.850540</td>\n",
       "      <td>44.781960</td>\n",
       "      <td>9.821096</td>\n",
       "      <td>17.815878</td>\n",
       "      <td>11.330600</td>\n",
       "      <td>005</td>\n",
       "      <td>c-n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ligand-006-c-n-2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>44</td>\n",
       "      <td>86.23337</td>\n",
       "      <td>2.145</td>\n",
       "      <td>2.253</td>\n",
       "      <td>0.17159</td>\n",
       "      <td>0.23504</td>\n",
       "      <td>-0.40535</td>\n",
       "      <td>...</td>\n",
       "      <td>68.766306</td>\n",
       "      <td>82.636042</td>\n",
       "      <td>51.118151</td>\n",
       "      <td>87.183190</td>\n",
       "      <td>25.987700</td>\n",
       "      <td>0.475214</td>\n",
       "      <td>14.018822</td>\n",
       "      <td>9.122251</td>\n",
       "      <td>006</td>\n",
       "      <td>c-n</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>372</th>\n",
       "      <td>ligand-407-p-o-138</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>89.32942</td>\n",
       "      <td>2.459</td>\n",
       "      <td>2.263</td>\n",
       "      <td>0.18682</td>\n",
       "      <td>1.04370</td>\n",
       "      <td>-0.96597</td>\n",
       "      <td>...</td>\n",
       "      <td>98.401975</td>\n",
       "      <td>85.571189</td>\n",
       "      <td>63.939620</td>\n",
       "      <td>73.788669</td>\n",
       "      <td>9.793142</td>\n",
       "      <td>0.843272</td>\n",
       "      <td>26.481551</td>\n",
       "      <td>30.833023</td>\n",
       "      <td>407</td>\n",
       "      <td>p-o</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>ligand-408-p-o-139</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>89.17718</td>\n",
       "      <td>2.456</td>\n",
       "      <td>2.264</td>\n",
       "      <td>0.18774</td>\n",
       "      <td>1.04308</td>\n",
       "      <td>-0.96762</td>\n",
       "      <td>...</td>\n",
       "      <td>98.541744</td>\n",
       "      <td>85.613120</td>\n",
       "      <td>64.480060</td>\n",
       "      <td>73.467201</td>\n",
       "      <td>9.900298</td>\n",
       "      <td>0.773388</td>\n",
       "      <td>30.506895</td>\n",
       "      <td>31.699590</td>\n",
       "      <td>408</td>\n",
       "      <td>p-o</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>ligand-409-p-o-140</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>89.05786</td>\n",
       "      <td>2.465</td>\n",
       "      <td>2.263</td>\n",
       "      <td>0.19732</td>\n",
       "      <td>1.03890</td>\n",
       "      <td>-0.96986</td>\n",
       "      <td>...</td>\n",
       "      <td>96.706113</td>\n",
       "      <td>86.311964</td>\n",
       "      <td>66.912039</td>\n",
       "      <td>81.387439</td>\n",
       "      <td>11.894335</td>\n",
       "      <td>0.661573</td>\n",
       "      <td>30.138837</td>\n",
       "      <td>26.733135</td>\n",
       "      <td>409</td>\n",
       "      <td>p-o</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>ligand-411-p-o-95</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>77.82640</td>\n",
       "      <td>2.418</td>\n",
       "      <td>2.292</td>\n",
       "      <td>0.19136</td>\n",
       "      <td>1.04202</td>\n",
       "      <td>-1.08516</td>\n",
       "      <td>...</td>\n",
       "      <td>82.389117</td>\n",
       "      <td>89.116660</td>\n",
       "      <td>64.591875</td>\n",
       "      <td>97.414275</td>\n",
       "      <td>22.218599</td>\n",
       "      <td>7.459001</td>\n",
       "      <td>19.166977</td>\n",
       "      <td>16.795565</td>\n",
       "      <td>411</td>\n",
       "      <td>p-o</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>ligand-412-p-o-97</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>91.74673</td>\n",
       "      <td>2.407</td>\n",
       "      <td>2.310</td>\n",
       "      <td>0.15940</td>\n",
       "      <td>1.01806</td>\n",
       "      <td>-1.06287</td>\n",
       "      <td>...</td>\n",
       "      <td>85.328923</td>\n",
       "      <td>53.340477</td>\n",
       "      <td>90.486396</td>\n",
       "      <td>93.943347</td>\n",
       "      <td>11.405143</td>\n",
       "      <td>7.766493</td>\n",
       "      <td>0.633619</td>\n",
       "      <td>11.325941</td>\n",
       "      <td>412</td>\n",
       "      <td>p-o</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>376 rows × 89 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Name  Metal  Atom_A  Atom_B  A-M-B Angle  M-A Distance  \\\n",
       "1      ligand-002-p-p-1      2      16      17     81.08750         2.404   \n",
       "2      ligand-003-p-p-2      3       1      12     88.85482         2.393   \n",
       "3      ligand-004-p-p-3      3      45       1     95.97953         2.432   \n",
       "4      ligand-005-c-n-1      1       2      59     86.57124         2.143   \n",
       "5      ligand-006-c-n-2      1       2      44     86.23337         2.145   \n",
       "..                  ...    ...     ...     ...          ...           ...   \n",
       "372  ligand-407-p-o-138     34       1       5     89.32942         2.459   \n",
       "373  ligand-408-p-o-139     34       1       5     89.17718         2.456   \n",
       "374  ligand-409-p-o-140     34       1       5     89.05786         2.465   \n",
       "375   ligand-411-p-o-95     13       1       4     77.82640         2.418   \n",
       "376   ligand-412-p-o-97     13       1       4     91.74673         2.407   \n",
       "\n",
       "     M-B Distance  Metal_NBO    A_NBO    B_NBO  ...  % VBur octant 1  \\\n",
       "1           2.406   -0.11874  1.06691  1.06830  ...        81.564480   \n",
       "2           2.398   -0.12498  1.08763  1.08260  ...        74.021618   \n",
       "3           2.441   -0.09320  1.06382  1.05890  ...        97.623928   \n",
       "4           2.295    0.17756  0.22924 -0.40636  ...        64.340291   \n",
       "5           2.253    0.17159  0.23504 -0.40535  ...        68.766306   \n",
       "..            ...        ...      ...      ...  ...              ...   \n",
       "372         2.263    0.18682  1.04370 -0.96597  ...        98.401975   \n",
       "373         2.264    0.18774  1.04308 -0.96762  ...        98.541744   \n",
       "374         2.263    0.19732  1.03890 -0.96986  ...        96.706113   \n",
       "375         2.292    0.19136  1.04202 -1.08516  ...        82.389117   \n",
       "376         2.310    0.15940  1.01806 -1.06287  ...        85.328923   \n",
       "\n",
       "     % VBur octant 2  % VBur octant 3  % VBur octant 4  % VBur octant 5  \\\n",
       "1          92.051808        83.060007        82.575475         5.101565   \n",
       "2          74.375699        95.387626        96.706113         8.866008   \n",
       "3          96.230898        70.308423        71.878494        11.675363   \n",
       "4          88.292024        40.775252        96.850540        44.781960   \n",
       "5          82.636042        51.118151        87.183190        25.987700   \n",
       "..               ...              ...              ...              ...   \n",
       "372        85.571189        63.939620        73.788669         9.793142   \n",
       "373        85.613120        64.480060        73.467201         9.900298   \n",
       "374        86.311964        66.912039        81.387439        11.894335   \n",
       "375        89.116660        64.591875        97.414275        22.218599   \n",
       "376        53.340477        90.486396        93.943347        11.405143   \n",
       "\n",
       "     % VBur octant 6  % VBur octant 7  % VBur octant 8  ligand_id  \\\n",
       "1           8.190458         6.671636         0.535781        002   \n",
       "2          10.035408        10.860045        10.669027        003   \n",
       "3          12.486023        18.496086        13.054417        004   \n",
       "4           9.821096        17.815878        11.330600        005   \n",
       "5           0.475214        14.018822         9.122251        006   \n",
       "..               ...              ...              ...        ...   \n",
       "372         0.843272        26.481551        30.833023        407   \n",
       "373         0.773388        30.506895        31.699590        408   \n",
       "374         0.661573        30.138837        26.733135        409   \n",
       "375         7.459001        19.166977        16.795565        411   \n",
       "376         7.766493         0.633619        11.325941        412   \n",
       "\n",
       "     donor_atom_comb  \n",
       "1                p-p  \n",
       "2                p-p  \n",
       "3                p-p  \n",
       "4                c-n  \n",
       "5                c-n  \n",
       "..               ...  \n",
       "372              p-o  \n",
       "373              p-o  \n",
       "374              p-o  \n",
       "375              p-o  \n",
       "376              p-o  \n",
       "\n",
       "[376 rows x 89 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95a3ddb2-bb3a-476e-9b04-2144522fa986",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "86e94e7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "print(type(X['A-B_NBO']))\n",
    "A_B_NBO_values = X['A-B_NBO'].tolist()\n",
    "float_values = [float(value) for value in A_B_NBO_values]\n",
    "names = X['Name'].tolist()\n",
    "ligand_id = X['ligand_id'].tolist()\n",
    "donor_atom_comb = X['donor_atom_comb'].tolist()\n",
    "X_2 = X.drop(['Name', 'Metal','Atom_A', 'Atom_B','ligand_id','donor_atom_comb'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d8b9cb2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "percent_buried_volume = X['Total % VBur']\n",
    "percent_buried_volume = [float(value) for value in percent_buried_volume]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba5e11d8-ac7b-467c-a5a6-d6124df50aa7",
   "metadata": {},
   "source": [
    "### Generate PCA map with all descriptors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "733a836e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "color=p-p<br>PC 1=%{x}<br>PC 2=%{y}<br>PC 3=%{z}<br>text=%{text}<extra></extra>",
         "legendgroup": "p-p",
         "marker": {
          "color": "#636efa",
          "symbol": "circle"
         },
         "mode": "markers+text",
         "name": "p-p",
         "scene": "scene",
         "showlegend": true,
         "text": [
          "002",
          "003",
          "004",
          "013",
          "027",
          "029",
          "030",
          "031",
          "032",
          "033",
          "035",
          "036",
          "042",
          "043",
          "044",
          "046",
          "047",
          "048",
          "121",
          "125",
          "126",
          "127",
          "128",
          "129"
         ],
         "type": "scatter3d",
         "x": [
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   "source": [
    "\n",
    "# Generate PCA map with ALL descriptors - find out cluster combinations through this\n",
    "\n",
    "pca = PCA(n_components=3)\n",
    "components = pca.fit_transform(X_2)\n",
    "\n",
    "total_var = pca.explained_variance_ratio_.sum() * 100\n",
    "\n",
    "fig = px.scatter_3d(\n",
    "    components,\n",
    "    x=0,\n",
    "    y=1,\n",
    "    z=2,\n",
    "    text=ligand_id,\n",
    "    color = donor_atom_comb,\n",
    "    title=f'Total Explained Variance: {total_var:.2f}%',\n",
    "    labels={'0': 'PC 1', '1': 'PC 2', '2': 'PC 3'}\n",
    ")\n",
    "\n",
    "fig.update_layout(width=800, \n",
    "                  height=600,\n",
    "                  coloraxis_colorbar=dict(\n",
    "                  title=dict(text=\"Percent Buried<br>Volume\",\n",
    "                             side=\"top\"),\n",
    "                  x=0.85,  # Position the legend closer to the scatter plot\n",
    "                  y=0.5,\n",
    "                  xanchor='left',\n",
    "                  yanchor='middle'),\n",
    "                  title=dict(\n",
    "                  x=0.5,          # Horizontal position (centered)\n",
    "                  y=0.9,          # Vertical position (closer to the plot)\n",
    "                  xanchor='center',\n",
    "                  yanchor='top'\n",
    "                    ) \n",
    "                 )\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "dbe8b261-7ea6-4a30-a97d-72453edf1311",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 2, the average silhouette score is 0.332912449346667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 3, the average silhouette score is 0.3642389911195937\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 4, the average silhouette score is 0.346057909975104\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 5, the average silhouette score is 0.3642408839023856\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 6, the average silhouette score is 0.33420773688670613\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 7, the average silhouette score is 0.3099752906252201\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 8, the average silhouette score is 0.30651448478245\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
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     "name": "stdout",
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     "text": [
      "For n_clusters = 9, the average silhouette score is 0.29730398842965866\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
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     "output_type": "stream",
     "text": [
      "For n_clusters = 10, the average silhouette score is 0.3082241785876624\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 11, the average silhouette score is 0.3153758151234942\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 12, the average silhouette score is 0.2878691989807568\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 13, the average silhouette score is 0.3315326308324646\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 14, the average silhouette score is 0.30586309357820335\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 15, the average silhouette score is 0.29600869757342396\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 16, the average silhouette score is 0.30621342310965366\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 17, the average silhouette score is 0.32745068471836786\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 18, the average silhouette score is 0.3226305250377543\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 19, the average silhouette score is 0.32143951188907943\n",
      "\n",
      "The optimal number of clusters is 5 with a silhouette score of 0.3642408839023856\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "PCA Result DataFrame with Cluster Labels:\n",
      "           PC1        PC2        PC3  Cluster Ligand\n",
      "0     3.689471  -5.086006  -7.133747        4    NaN\n",
      "1    -8.700032  -4.439363   6.693798        4    002\n",
      "2   -16.045081  -0.364023  -6.631076        2    003\n",
      "3   -15.391295  26.005289 -57.717494        2    004\n",
      "4     5.067326  27.849428 -39.823364        2    005\n",
      "..         ...        ...        ...      ...    ...\n",
      "371 -18.630044  19.682288  -7.110051        2    406\n",
      "372 -20.878137  17.579001  -8.174826        2    407\n",
      "373 -24.463510  15.813892 -12.295583        2    408\n",
      "374 -25.573103  13.756002 -26.277375        2    409\n",
      "375   3.590266  22.825549  24.251511        4    411\n",
      "\n",
      "[376 rows x 5 columns]\n",
      "{'c1': ['038', '072', '073', '074', '075', '076', '077', '078', '092', '099', '111', '121', '122', '145', '146', '149', '150', '151', '160', '161', '168', '169', '170', '171', '172', '173', '174', '177', '178', '179', '180', '181', '182', '183', '186', '199', '200', '201', '202', '205', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '237', '238', '239', '240', '241', '250', '251', '252', '254', '257', '259', '264', '265', '296', '299', '301', '303', '309', '310', '311', '312', '348', '350', '353', '356', '357', '358', '359', '360', '387'], 'c2': ['022', '031', '040', '042', '053', '056', '057', '058', '084', '085', '086', '087', '089', '090', '096', '098', '114', '117', '118', '123', '129', '132', '134', '136', '162', '163', '164', '165', '166', '167', '190', '244', '255', '269', '270', '275', '276', '280', '287', '304', '305', '306', '307', '308', '313', '314', '327', '330', '331', '332', '333', '334', '335', '336', '337', '338', '341', '342', '343', '344', '345', '346', '347', '349', '351', '352', '355', '361', '362', '363', '364', '365', '366', '377', '378', '379', '380', '381', '382', '383', '394', '395', '396', '397', '399', '400', '401', '402', '403', '404', '405'], 'c3': ['003', '004', '005', '006', '007', '009', '027', '029', '033', '039', '048', '080', '081', '082', '093', '095', '097', '100', '107', '120', '124', '127', '131', '135', '137', '138', '139', '140', '142', '143', '144', '189', '192', '195', '233', '242', '243', '245', '246', '249', '260', '261', '262', '263', '267', '268', '271', '273', '277', '278', '286', '289', '290', '291', '292', '293', '324', '328', '368', '370', '390', '398', '406', '407', '408', '409'], 'c4': ['036', '060', '071', '079', '083', '101', '102', '103', '104', '105', '106', '141', '147', '148', '152', '153', '154', '155', '156', '157', '158', '159', '184', '187', '188', '191', '193', '194', '196', '197', '198', '203', '204', '206', '253', '256', '258', '294', '297', '298', '322', '329', '339', '376', '391'], 'c5': [nan, '002', '008', '010', '011', '013', '018', '019', '020', '021', '030', '032', '035', '037', '041', '043', '044', '046', '047', '055', '059', '088', '091', '094', '108', '109', '110', '112', '113', '115', '116', '119', '125', '126', '128', '130', '133', '175', '176', '232', '234', '236', '247', '248', '266', '272', '274', '279', '281', '282', '283', '284', '285', '288', '295', '300', '302', '315', '316', '317', '318', '319', '320', '321', '323', '325', '326', '340', '354', '367', '369', '384', '385', '386', '388', '389', '392', '393', '411']}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Generate dataframe from pca data: \n",
    "pca_df = pd.DataFrame(data=components, columns=['PC1', 'PC2','PC3'])\n",
    "\n",
    "# Apply KMeans Clustering\n",
    "# Define a range of clusters to evaluate\n",
    "range_n_clusters = list(range(2, 20))\n",
    "\n",
    "# Initialize variables to store the best number of clusters and corresponding metrics\n",
    "best_n_clusters = 0\n",
    "best_silhouette_score = -1\n",
    "inertia = []\n",
    "\n",
    "# Iterate over different cluster numbers to find the optimal number\n",
    "for n_clusters in range_n_clusters:\n",
    "    kmeans = KMeans(init=\"k-means++\", n_clusters=n_clusters, n_init=4, random_state=42)\n",
    "    cluster_labels = kmeans.fit_predict(pca_df)\n",
    "    # Calculate silhouette score\n",
    "    silhouette_avg = silhouette_score(pca_df, cluster_labels)\n",
    "    inertia.append(kmeans.inertia_)\n",
    "    \n",
    "    print(f\"For n_clusters = {n_clusters}, the average silhouette score is {silhouette_avg}\")\n",
    "    \n",
    "    # Update the best silhouette score and corresponding number of clusters\n",
    "    if silhouette_avg > best_silhouette_score:\n",
    "        best_n_clusters = n_clusters\n",
    "        best_silhouette_score = silhouette_avg\n",
    "\n",
    "# Print the optimal number of clusters and the best silhouette score\n",
    "print(f\"\\nThe optimal number of clusters is {best_n_clusters} with a silhouette score of {best_silhouette_score}\")\n",
    "\n",
    "# Plot the inertia to visualize the elbow method\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(range_n_clusters, inertia, marker='o')\n",
    "plt.title('Elbow Method For Optimal Number of Clusters')\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('Inertia')\n",
    "plt.show()\n",
    "\n",
    "# Apply KMeans with the optimal number of clusters\n",
    "kmeans = KMeans(n_clusters=best_n_clusters, random_state=42)\n",
    "cluster_labels = kmeans.fit_predict(pca_df)\n",
    "\n",
    "# Add cluster labels to the PCA result DataFrame\n",
    "pca_df['Cluster'] = cluster_labels\n",
    "pca_df['Ligand'] = X['ligand_id']\n",
    "\n",
    "\n",
    "# Print the PCA result DataFrame with cluster labels\n",
    "print(\"\\nPCA Result DataFrame with Cluster Labels:\")\n",
    "print(pca_df)\n",
    "\n",
    "\n",
    "# Create a dictionary to store clusters and their corresponding ligands\n",
    "cluster_dict_all_descriptor = {}\n",
    "for cluster_num in range(best_n_clusters):\n",
    "    cluster_key = f'c{cluster_num + 1}'  # Create cluster key like 'c1', 'c2', etc.\n",
    "    ligands_in_cluster = pca_df[pca_df['Cluster'] == cluster_num]['Ligand'].tolist()\n",
    "    cluster_dict_all_descriptor[cluster_key] = ligands_in_cluster\n",
    "\n",
    "# Print the dictionary\n",
    "print(cluster_dict_all_descriptor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "00566dda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['A-M-B Angle', 'M-A Distance', 'M-B Distance', 'Metal_NBO', 'A_NBO',\n",
      "       'B_NBO', 'HOMO_LUMO', 'A-B_NBO', 'NBO-dist-2-avg', 'NBO-dist-3-avg',\n",
      "       'NBO-dist-4-avg', 'NBO-dist-2-a-avg', 'NBO-dist-3-a-avg',\n",
      "       'NBO-dist-4-a-avg', 'NBO-dist-2-b-avg', 'NBO-dist-3-b-avg',\n",
      "       'NBO-dist-4-b-avg', 'NBO-dist-2-a-b-diff', 'NBO-dist-3-a-b-diff',\n",
      "       'NBO-dist-4-a-b-diff', 'NBO-dist-2-min', 'NBO-dist-3-min',\n",
      "       'NBO-dist-4-min', 'NBO-dist-2-max', 'NBO-dist-3-max', 'NBO-dist-4-max',\n",
      "       'NBO-dist-2-a-min', 'NBO-dist-3-a-min', 'NBO-dist-4-a-min',\n",
      "       'NBO-dist-2-b-min', 'NBO-dist-3-b-min', 'NBO-dist-4-b-min',\n",
      "       'NBO-dist-2-a-max', 'NBO-dist-3-a-max', 'NBO-dist-4-a-max',\n",
      "       'NBO-dist-2-b-max', 'NBO-dist-3-b-max', 'NBO-dist-4-b-max',\n",
      "       'NBO-metal-donor-diff', 'NBO-metal-a-diff', 'NBO-metal-b-diff',\n",
      "       'NBO-dist-2-1-avg-diff', 'NBO-dist-3-2-avg-diff',\n",
      "       'NBO-dist-4-3-avg-diff', 'NBO-dist-2-1-a-avg-diff',\n",
      "       'NBO-dist-3-2-a-avg-diff', 'NBO-dist-4-3-a-avg-diff',\n",
      "       'NBO-dist-2-1-b-avg-diff', 'NBO-dist-3-2-b-avg-diff',\n",
      "       'NBO-dist-4-3-b-avg-diff', 'Metal_mullkien', 'A_mulliken', 'B_mulliken',\n",
      "       'A-B_mulliken', 'Mulliken-dist-2-avg', 'Mulliken-dist-3-avg',\n",
      "       'Mulliken-dist-4-avg', 'Total internal angle deviation %',\n",
      "       'Total % VBur', '% VBur SE', '% VBur SW', '% VBur NE', '% VBur NW',\n",
      "       '% VBur max', '% VBur min', '% VBur max min diff', '% VBur NE NW diff',\n",
      "       '% VBur SE SW diff', '% VBur NE SE diff', '% VBur NW SW diff',\n",
      "       '% VBur NE SW diff', '% VBur NW SE diff', '% VBur asymm 1',\n",
      "       '% VBur asymm 2', '% VBur asymm 3', '% VBur octant 1',\n",
      "       '% VBur octant 2', '% VBur octant 3', '% VBur octant 4',\n",
      "       '% VBur octant 5', '% VBur octant 6', '% VBur octant 7',\n",
      "       '% VBur octant 8'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#Obtain only nbo based descriptor X_nbo\n",
    "# Obtain only percent buried volume based descriptor X_Vbur \n",
    "\n",
    "X_2_columns = X_2.columns\n",
    "print(X_2_columns)\n",
    "\n",
    "nbo_list = ['Metal_NBO', 'A_NBO', 'B_NBO', 'HOMO_LUMO', 'A-B_NBO',\n",
    "       'NBO-dist-2-avg', 'NBO-dist-3-avg', 'NBO-dist-4-avg',\n",
    "       'NBO-dist-2-a-avg', 'NBO-dist-3-a-avg', 'NBO-dist-4-a-avg',\n",
    "       'NBO-dist-2-b-avg', 'NBO-dist-3-b-avg', 'NBO-dist-4-b-avg',\n",
    "       'NBO-dist-2-a-b-diff', 'NBO-dist-3-a-b-diff', 'NBO-dist-4-a-b-diff',\n",
    "       'NBO-dist-2-min', 'NBO-dist-3-min', 'NBO-dist-4-min', 'NBO-dist-2-max',\n",
    "       'NBO-dist-3-max', 'NBO-dist-4-max', 'NBO-dist-2-a-min',\n",
    "       'NBO-dist-3-a-min', 'NBO-dist-4-a-min', 'NBO-dist-2-b-min',\n",
    "       'NBO-dist-3-b-min', 'NBO-dist-4-b-min', 'NBO-dist-2-a-max',\n",
    "       'NBO-dist-3-a-max', 'NBO-dist-4-a-max', 'NBO-dist-2-b-max',\n",
    "       'NBO-dist-3-b-max', 'NBO-dist-4-b-max', 'NBO-metal-donor-diff',\n",
    "       'NBO-metal-a-diff', 'NBO-metal-b-diff', 'NBO-dist-2-1-avg-diff',\n",
    "       'NBO-dist-3-2-avg-diff', 'NBO-dist-4-3-avg-diff',\n",
    "       'NBO-dist-2-1-a-avg-diff', 'NBO-dist-3-2-a-avg-diff',\n",
    "       'NBO-dist-4-3-a-avg-diff', 'NBO-dist-2-1-b-avg-diff',\n",
    "       'NBO-dist-3-2-b-avg-diff', 'NBO-dist-4-3-b-avg-diff', 'Metal_mullkien',\n",
    "       'A_mulliken', 'B_mulliken', 'A-B_mulliken', 'Mulliken-dist-2-avg',\n",
    "       'Mulliken-dist-3-avg', 'Mulliken-dist-4-avg']\n",
    "\n",
    "\n",
    "vbur_list = ['Total % VBur', '% VBur SE',\n",
    "       '% VBur SW', '% VBur NE', '% VBur NW', '% VBur max', '% VBur min',\n",
    "       '% VBur max min diff', '% VBur NE NW diff', '% VBur SE SW diff',\n",
    "       '% VBur NE SE diff', '% VBur NW SW diff', '% VBur NE SW diff',\n",
    "       '% VBur NW SE diff', '% VBur asymm 1', '% VBur asymm 2',\n",
    "       '% VBur asymm 3', '% VBur octant 1', '% VBur octant 2',\n",
    "       '% VBur octant 3', '% VBur octant 4', '% VBur octant 5',\n",
    "       '% VBur octant 6', '% VBur octant 7', '% VBur octant 8']\n",
    "\n",
    "X_nbo = X_2[nbo_list]\n",
    "X_vbur = X_2[vbur_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "e9fc5960",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total % VBur</th>\n",
       "      <th>% VBur SE</th>\n",
       "      <th>% VBur SW</th>\n",
       "      <th>% VBur NE</th>\n",
       "      <th>% VBur NW</th>\n",
       "      <th>% VBur max</th>\n",
       "      <th>% VBur min</th>\n",
       "      <th>% VBur max min diff</th>\n",
       "      <th>% VBur NE NW diff</th>\n",
       "      <th>% VBur SE SW diff</th>\n",
       "      <th>...</th>\n",
       "      <th>% VBur asymm 2</th>\n",
       "      <th>% VBur asymm 3</th>\n",
       "      <th>% VBur octant 1</th>\n",
       "      <th>% VBur octant 2</th>\n",
       "      <th>% VBur octant 3</th>\n",
       "      <th>% VBur octant 4</th>\n",
       "      <th>% VBur octant 5</th>\n",
       "      <th>% VBur octant 6</th>\n",
       "      <th>% VBur octant 7</th>\n",
       "      <th>% VBur octant 8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>1</th>\n",
       "      <td>44.968901</td>\n",
       "      <td>41.050130</td>\n",
       "      <td>49.361722</td>\n",
       "      <td>43.838520</td>\n",
       "      <td>45.625233</td>\n",
       "      <td>49.361722</td>\n",
       "      <td>41.050130</td>\n",
       "      <td>8.311592</td>\n",
       "      <td>44.731877</td>\n",
       "      <td>45.205926</td>\n",
       "      <td>...</td>\n",
       "      <td>5.049152</td>\n",
       "      <td>3.262439</td>\n",
       "      <td>81.564480</td>\n",
       "      <td>92.051808</td>\n",
       "      <td>83.060007</td>\n",
       "      <td>82.575475</td>\n",
       "      <td>5.101565</td>\n",
       "      <td>8.190458</td>\n",
       "      <td>6.671636</td>\n",
       "      <td>0.535781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47.615193</td>\n",
       "      <td>42.345322</td>\n",
       "      <td>42.617872</td>\n",
       "      <td>52.786060</td>\n",
       "      <td>52.711517</td>\n",
       "      <td>52.786060</td>\n",
       "      <td>42.345322</td>\n",
       "      <td>10.440738</td>\n",
       "      <td>52.748789</td>\n",
       "      <td>42.481597</td>\n",
       "      <td>...</td>\n",
       "      <td>0.099003</td>\n",
       "      <td>0.173546</td>\n",
       "      <td>74.021618</td>\n",
       "      <td>74.375699</td>\n",
       "      <td>95.387626</td>\n",
       "      <td>96.706113</td>\n",
       "      <td>8.866008</td>\n",
       "      <td>10.035408</td>\n",
       "      <td>10.860045</td>\n",
       "      <td>10.669027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48.969204</td>\n",
       "      <td>55.339173</td>\n",
       "      <td>57.363492</td>\n",
       "      <td>41.776929</td>\n",
       "      <td>41.397223</td>\n",
       "      <td>57.363492</td>\n",
       "      <td>41.397223</td>\n",
       "      <td>15.966269</td>\n",
       "      <td>41.587076</td>\n",
       "      <td>56.351332</td>\n",
       "      <td>...</td>\n",
       "      <td>0.822307</td>\n",
       "      <td>1.202013</td>\n",
       "      <td>97.623928</td>\n",
       "      <td>96.230898</td>\n",
       "      <td>70.308423</td>\n",
       "      <td>71.878494</td>\n",
       "      <td>11.675363</td>\n",
       "      <td>12.486023</td>\n",
       "      <td>18.496086</td>\n",
       "      <td>13.054417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46.750955</td>\n",
       "      <td>37.835445</td>\n",
       "      <td>53.053951</td>\n",
       "      <td>70.816250</td>\n",
       "      <td>25.298174</td>\n",
       "      <td>70.816250</td>\n",
       "      <td>25.298174</td>\n",
       "      <td>45.518077</td>\n",
       "      <td>48.057212</td>\n",
       "      <td>45.444698</td>\n",
       "      <td>...</td>\n",
       "      <td>15.149786</td>\n",
       "      <td>30.368291</td>\n",
       "      <td>64.340291</td>\n",
       "      <td>88.292024</td>\n",
       "      <td>40.775252</td>\n",
       "      <td>96.850540</td>\n",
       "      <td>44.781960</td>\n",
       "      <td>9.821096</td>\n",
       "      <td>17.815878</td>\n",
       "      <td>11.330600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>42.413460</td>\n",
       "      <td>38.944279</td>\n",
       "      <td>48.327432</td>\n",
       "      <td>56.585445</td>\n",
       "      <td>25.796683</td>\n",
       "      <td>56.585445</td>\n",
       "      <td>25.796683</td>\n",
       "      <td>30.788763</td>\n",
       "      <td>41.191064</td>\n",
       "      <td>43.635855</td>\n",
       "      <td>...</td>\n",
       "      <td>10.702805</td>\n",
       "      <td>20.085958</td>\n",
       "      <td>68.766306</td>\n",
       "      <td>82.636042</td>\n",
       "      <td>51.118151</td>\n",
       "      <td>87.183190</td>\n",
       "      <td>25.987700</td>\n",
       "      <td>0.475214</td>\n",
       "      <td>14.018822</td>\n",
       "      <td>9.122251</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>372</th>\n",
       "      <td>48.706555</td>\n",
       "      <td>64.617499</td>\n",
       "      <td>56.026370</td>\n",
       "      <td>41.790906</td>\n",
       "      <td>32.391446</td>\n",
       "      <td>64.617499</td>\n",
       "      <td>32.391446</td>\n",
       "      <td>32.226053</td>\n",
       "      <td>37.091176</td>\n",
       "      <td>60.321934</td>\n",
       "      <td>...</td>\n",
       "      <td>8.995294</td>\n",
       "      <td>0.404165</td>\n",
       "      <td>98.401975</td>\n",
       "      <td>85.571189</td>\n",
       "      <td>63.939620</td>\n",
       "      <td>73.788669</td>\n",
       "      <td>9.793142</td>\n",
       "      <td>0.843272</td>\n",
       "      <td>26.481551</td>\n",
       "      <td>30.833023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>49.372787</td>\n",
       "      <td>65.120667</td>\n",
       "      <td>58.060007</td>\n",
       "      <td>41.683750</td>\n",
       "      <td>32.626724</td>\n",
       "      <td>65.120667</td>\n",
       "      <td>32.626724</td>\n",
       "      <td>32.493943</td>\n",
       "      <td>37.155237</td>\n",
       "      <td>61.590337</td>\n",
       "      <td>...</td>\n",
       "      <td>8.058843</td>\n",
       "      <td>0.998183</td>\n",
       "      <td>98.541744</td>\n",
       "      <td>85.613120</td>\n",
       "      <td>64.480060</td>\n",
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       "      <td>9.900298</td>\n",
       "      <td>0.773388</td>\n",
       "      <td>30.506895</td>\n",
       "      <td>31.699590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>50.093179</td>\n",
       "      <td>61.719624</td>\n",
       "      <td>58.225401</td>\n",
       "      <td>46.640887</td>\n",
       "      <td>33.786806</td>\n",
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       "      <td>27.932818</td>\n",
       "      <td>40.213846</td>\n",
       "      <td>59.972512</td>\n",
       "      <td>...</td>\n",
       "      <td>8.174152</td>\n",
       "      <td>4.679929</td>\n",
       "      <td>96.706113</td>\n",
       "      <td>86.311964</td>\n",
       "      <td>66.912039</td>\n",
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       "      <td>0.661573</td>\n",
       "      <td>30.138837</td>\n",
       "      <td>26.733135</td>\n",
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       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>49.894009</td>\n",
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       "      <td>54.141819</td>\n",
       "      <td>59.816437</td>\n",
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       "      <td>59.816437</td>\n",
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       "      <td>23.790999</td>\n",
       "      <td>47.920937</td>\n",
       "      <td>51.867080</td>\n",
       "      <td>...</td>\n",
       "      <td>9.620760</td>\n",
       "      <td>14.170239</td>\n",
       "      <td>82.389117</td>\n",
       "      <td>89.116660</td>\n",
       "      <td>64.591875</td>\n",
       "      <td>97.414275</td>\n",
       "      <td>22.218599</td>\n",
       "      <td>7.459001</td>\n",
       "      <td>19.166977</td>\n",
       "      <td>16.795565</td>\n",
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       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>44.278792</td>\n",
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       "      <td>25.687197</td>\n",
       "      <td>50.900345</td>\n",
       "      <td>37.657240</td>\n",
       "      <td>...</td>\n",
       "      <td>12.444092</td>\n",
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       "      <td>85.328923</td>\n",
       "      <td>53.340477</td>\n",
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       "      <td>11.405143</td>\n",
       "      <td>7.766493</td>\n",
       "      <td>0.633619</td>\n",
       "      <td>11.325941</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>376 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Total % VBur  % VBur SE  % VBur SW  % VBur NE  % VBur NW  % VBur max  \\\n",
       "1       44.968901  41.050130  49.361722  43.838520  45.625233   49.361722   \n",
       "2       47.615193  42.345322  42.617872  52.786060  52.711517   52.786060   \n",
       "3       48.969204  55.339173  57.363492  41.776929  41.397223   57.363492   \n",
       "4       46.750955  37.835445  53.053951  70.816250  25.298174   70.816250   \n",
       "5       42.413460  38.944279  48.327432  56.585445  25.796683   56.585445   \n",
       "..            ...        ...        ...        ...        ...         ...   \n",
       "372     48.706555  64.617499  56.026370  41.790906  32.391446   64.617499   \n",
       "373     49.372787  65.120667  58.060007  41.683750  32.626724   65.120667   \n",
       "374     50.093179  61.719624  58.225401  46.640887  33.786806   61.719624   \n",
       "375     49.894009  49.592341  54.141819  59.816437  36.025438   59.816437   \n",
       "376     44.278792  48.327432  26.987048  52.674245  49.126444   52.674245   \n",
       "\n",
       "     % VBur min  % VBur max min diff  % VBur NE NW diff  % VBur SE SW diff  \\\n",
       "1     41.050130             8.311592          44.731877          45.205926   \n",
       "2     42.345322            10.440738          52.748789          42.481597   \n",
       "3     41.397223            15.966269          41.587076          56.351332   \n",
       "4     25.298174            45.518077          48.057212          45.444698   \n",
       "5     25.796683            30.788763          41.191064          43.635855   \n",
       "..          ...                  ...                ...                ...   \n",
       "372   32.391446            32.226053          37.091176          60.321934   \n",
       "373   32.626724            32.493943          37.155237          61.590337   \n",
       "374   33.786806            27.932818          40.213846          59.972512   \n",
       "375   36.025438            23.790999          47.920937          51.867080   \n",
       "376   26.987048            25.687197          50.900345          37.657240   \n",
       "\n",
       "     ...  % VBur asymm 2  % VBur asymm 3  % VBur octant 1  % VBur octant 2  \\\n",
       "1    ...        5.049152        3.262439        81.564480        92.051808   \n",
       "2    ...        0.099003        0.173546        74.021618        74.375699   \n",
       "3    ...        0.822307        1.202013        97.623928        96.230898   \n",
       "4    ...       15.149786       30.368291        64.340291        88.292024   \n",
       "5    ...       10.702805       20.085958        68.766306        82.636042   \n",
       "..   ...             ...             ...              ...              ...   \n",
       "372  ...        8.995294        0.404165        98.401975        85.571189   \n",
       "373  ...        8.058843        0.998183        98.541744        85.613120   \n",
       "374  ...        8.174152        4.679929        96.706113        86.311964   \n",
       "375  ...        9.620760       14.170239        82.389117        89.116660   \n",
       "376  ...       12.444092        8.896291        85.328923        53.340477   \n",
       "\n",
       "     % VBur octant 3  % VBur octant 4  % VBur octant 5  % VBur octant 6  \\\n",
       "1          83.060007        82.575475         5.101565         8.190458   \n",
       "2          95.387626        96.706113         8.866008        10.035408   \n",
       "3          70.308423        71.878494        11.675363        12.486023   \n",
       "4          40.775252        96.850540        44.781960         9.821096   \n",
       "5          51.118151        87.183190        25.987700         0.475214   \n",
       "..               ...              ...              ...              ...   \n",
       "372        63.939620        73.788669         9.793142         0.843272   \n",
       "373        64.480060        73.467201         9.900298         0.773388   \n",
       "374        66.912039        81.387439        11.894335         0.661573   \n",
       "375        64.591875        97.414275        22.218599         7.459001   \n",
       "376        90.486396        93.943347        11.405143         7.766493   \n",
       "\n",
       "     % VBur octant 7  % VBur octant 8  \n",
       "1           6.671636         0.535781  \n",
       "2          10.860045        10.669027  \n",
       "3          18.496086        13.054417  \n",
       "4          17.815878        11.330600  \n",
       "5          14.018822         9.122251  \n",
       "..               ...              ...  \n",
       "372        26.481551        30.833023  \n",
       "373        30.506895        31.699590  \n",
       "374        30.138837        26.733135  \n",
       "375        19.166977        16.795565  \n",
       "376         0.633619        11.325941  \n",
       "\n",
       "[376 rows x 25 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_vbur"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1bfd4cb-a7aa-4c94-accc-e8ef68c790ae",
   "metadata": {},
   "source": [
    "### Generate PCA map with V_bur descriptors - find out cluster combinations through this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "17f17963",
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "A_B_NBO_values = X_nbo['A-B_NBO'].tolist()\n",
    "float_values = [float(value) for value in A_B_NBO_values]\n",
    "\n",
    "A_NBO_values = X_nbo['A_NBO'].tolist()\n",
    "float_values = [float(value) for value in A_NBO_values]\n",
    "\n",
    "vbur_values = X_vbur['Total % VBur'].tolist()\n",
    "vbur_values = [float(value) for value in vbur_values]\n",
    "\n",
    "vbur_max_values = X_vbur['% VBur max'].tolist()\n",
    "vbur_max_values = [float(value) for value in vbur_max_values]\n",
    "\n",
    "pca = PCA(n_components=3)\n",
    "components = pca.fit_transform(X_vbur)\n",
    "\n",
    "total_var = pca.explained_variance_ratio_.sum() * 100\n",
    "\n",
    "fig = px.scatter_3d(\n",
    "    components,\n",
    "    x=0,\n",
    "    y=1,\n",
    "    z=2,\n",
    "    # text=names,\n",
    "    color = percent_buried_volume,\n",
    "    title=f'Total Explained Variance: {total_var:.2f}%',\n",
    "    labels={'0': 'PC 1', '1': 'PC 2', '2': 'PC 3'}\n",
    ")\n",
    "\n",
    "fig.update_layout(width=800, \n",
    "                  height=600,\n",
    "                  coloraxis_colorbar=dict(\n",
    "                  title=dict(text=\"Percent Buried<br>Volume\",\n",
    "                             side=\"top\"),\n",
    "                  x=0.85,  # Position the legend closer to the scatter plot\n",
    "                  y=0.5,\n",
    "                  xanchor='left',\n",
    "                  yanchor='middle'),\n",
    "                  title=dict(\n",
    "                  x=0.5,          # Horizontal position (centered)\n",
    "                  y=0.9,          # Vertical position (closer to the plot)\n",
    "                  xanchor='center',\n",
    "                  yanchor='top'\n",
    "                    ) \n",
    "                 )\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da068a20-01aa-4abc-a83b-43addff37498",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "0ad8d972-9acb-4acf-84ed-d1618ba34f5c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 2, the average silhouette score is 0.33344233789882544\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 3, the average silhouette score is 0.3647145926571277\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 4, the average silhouette score is 0.34944822592558616\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 5, the average silhouette score is 0.3643165263047619\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 6, the average silhouette score is 0.33172382177076415\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 7, the average silhouette score is 0.3309404089592618\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 8, the average silhouette score is 0.2932356554062508\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 9, the average silhouette score is 0.30804303302355945\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 10, the average silhouette score is 0.3044609762039521\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 11, the average silhouette score is 0.31542957620228884\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 12, the average silhouette score is 0.2983134339921561\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 13, the average silhouette score is 0.3270701838092892\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 14, the average silhouette score is 0.31090776954740623\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 15, the average silhouette score is 0.32486569623654726\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 16, the average silhouette score is 0.31685620512463514\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 17, the average silhouette score is 0.3154144969621037\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 18, the average silhouette score is 0.31682162019223864\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 19, the average silhouette score is 0.3184653039781163\n",
      "\n",
      "The optimal number of clusters is 3 with a silhouette score of 0.3647145926571277\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "PCA Result DataFrame with Cluster Labels:\n",
      "           PC1        PC2        PC3  Cluster Ligand\n",
      "0     3.693650  -5.061245   6.767247        2    NaN\n",
      "1    -8.644768  -4.311412  -6.470653        1    002\n",
      "2   -15.888863  -0.176192   7.600570        1    003\n",
      "3   -15.358850  26.183543  58.773503        2    004\n",
      "4     5.110844  27.947913  40.396662        2    005\n",
      "..         ...        ...        ...      ...    ...\n",
      "371 -18.414279  19.674493   8.110057        2    406\n",
      "372 -20.671890  17.578170   9.181137        2    407\n",
      "373 -24.268662  15.824961  13.167304        1    408\n",
      "374 -25.438013  13.691229  26.295564        1    409\n",
      "375   3.801941  22.786031 -23.429120        2    411\n",
      "\n",
      "[376 rows x 5 columns]\n",
      "{'c1': ['038', '048', '072', '073', '074', '075', '076', '077', '078', '092', '099', '111', '121', '122', '145', '146', '147', '148', '149', '150', '151', '160', '161', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '186', '199', '200', '201', '202', '205', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '237', '238', '239', '240', '241', '250', '251', '252', '254', '257', '259', '264', '265', '296', '299', '301', '303', '309', '310', '311', '312', '348', '350', '353', '356', '357', '358', '359', '360', '387'], 'c2': ['002', '003', '011', '013', '018', '019', '020', '021', '022', '030', '031', '032', '033', '035', '040', '041', '042', '043', '046', '047', '053', '055', '056', '057', '058', '059', '084', '085', '086', '087', '088', '089', '090', '094', '096', '097', '098', '113', '114', '117', '118', '119', '123', '129', '132', '134', '136', '137', '138', '162', '163', '164', '165', '166', '167', '190', '244', '247', '248', '255', '268', '269', '270', '271', '275', '276', '280', '281', '287', '290', '291', '292', '293', '304', '305', '306', '307', '308', '313', '314', '327', '330', '331', '332', '333', '334', '335', '336', '337', '338', '341', '342', '343', '344', '345', '346', '347', '349', '351', '352', '355', '361', '362', '363', '364', '365', '366', '368', '369', '377', '378', '379', '380', '381', '382', '383', '385', '386', '388', '390', '394', '395', '396', '397', '399', '400', '401', '402', '403', '404', '405', '408', '409'], 'c3': [nan, '004', '005', '006', '007', '008', '009', '010', '027', '029', '036', '037', '039', '044', '060', '071', '079', '080', '081', '082', '083', '091', '093', '095', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '112', '115', '116', '120', '124', '125', '126', '127', '128', '130', '131', '133', '135', '139', '140', '141', '142', '143', '144', '152', '153', '154', '155', '156', '157', '158', '159', '184', '187', '188', '189', '191', '192', '193', '194', '195', '196', '197', '198', '203', '204', '206', '232', '233', '234', '236', '242', '243', '245', '246', '249', '253', '256', '258', '260', '261', '262', '263', '266', '267', '272', '273', '274', '277', '278', '279', '282', '283', '284', '285', '286', '288', '289', '294', '295', '297', '298', '300', '302', '315', '316', '317', '318', '319', '320', '321', '322', '323', '324', '325', '326', '328', '329', '339', '340', '354', '367', '370', '376', '384', '389', '391', '392', '393', '398', '406', '407', '411']}\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Generate dataframe from pca data: \n",
    "pca_df = pd.DataFrame(data=components, columns=['PC1', 'PC2','PC3'])\n",
    "\n",
    "# Apply KMeans Clustering\n",
    "# Define a range of clusters to evaluate\n",
    "range_n_clusters = list(range(2, 20))\n",
    "\n",
    "# Initialize variables to store the best number of clusters and corresponding metrics\n",
    "best_n_clusters = 0\n",
    "best_silhouette_score = -1\n",
    "inertia = []\n",
    "\n",
    "# Iterate over different cluster numbers to find the optimal number\n",
    "for n_clusters in range_n_clusters:\n",
    "    kmeans = KMeans(init=\"k-means++\", n_clusters=n_clusters, n_init=4, random_state=42)\n",
    "    cluster_labels = kmeans.fit_predict(pca_df)\n",
    "    # Calculate silhouette score\n",
    "    silhouette_avg = silhouette_score(pca_df, cluster_labels)\n",
    "    inertia.append(kmeans.inertia_)\n",
    "    \n",
    "    print(f\"For n_clusters = {n_clusters}, the average silhouette score is {silhouette_avg}\")\n",
    "    \n",
    "    # Update the best silhouette score and corresponding number of clusters\n",
    "    if silhouette_avg > best_silhouette_score:\n",
    "        best_n_clusters = n_clusters\n",
    "        best_silhouette_score = silhouette_avg\n",
    "\n",
    "# Print the optimal number of clusters and the best silhouette score\n",
    "print(f\"\\nThe optimal number of clusters is {best_n_clusters} with a silhouette score of {best_silhouette_score}\")\n",
    "\n",
    "# Plot the inertia to visualize the elbow method\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(range_n_clusters, inertia, marker='o')\n",
    "plt.title('Elbow Method For Optimal Number of Clusters')\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('Inertia')\n",
    "plt.show()\n",
    "\n",
    "# Apply KMeans with the optimal number of clusters\n",
    "kmeans = KMeans(n_clusters=best_n_clusters, random_state=42)\n",
    "cluster_labels = kmeans.fit_predict(pca_df)\n",
    "\n",
    "# Add cluster labels to the PCA result DataFrame\n",
    "pca_df['Cluster'] = cluster_labels\n",
    "pca_df['Ligand'] = X['ligand_id']\n",
    "\n",
    "\n",
    "# Print the PCA result DataFrame with cluster labels\n",
    "print(\"\\nPCA Result DataFrame with Cluster Labels:\")\n",
    "print(pca_df)\n",
    "\n",
    "\n",
    "# Create a dictionary to store clusters and their corresponding ligands\n",
    "cluster_dict_vbur_descriptor = {}\n",
    "for cluster_num in range(best_n_clusters):\n",
    "    cluster_key = f'c{cluster_num + 1}'  # Create cluster key like 'c1', 'c2', etc.\n",
    "    ligands_in_cluster = pca_df[pca_df['Cluster'] == cluster_num]['Ligand'].tolist()\n",
    "    cluster_dict_vbur_descriptor[cluster_key] = ligands_in_cluster\n",
    "\n",
    "# Print the dictionary\n",
    "print(cluster_dict_vbur_descriptor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e34c4e30-d065-48cb-baaf-c2b916cc1d69",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2b8dd7fc-6f29-488e-8afe-e59fc40ccdd9",
   "metadata": {},
   "source": [
    "### Generate PCA map with NBO descriptors - find out cluster combinations through this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "7c1bb2e8",
   "metadata": {},
   "outputs": [
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",
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       "                            \n",
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       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
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       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "A_B_NBO_values = X_nbo['A-B_NBO'].tolist()\n",
    "float_values = [float(value) for value in A_B_NBO_values]\n",
    "\n",
    "A_NBO_values = X_nbo['A_NBO'].tolist()\n",
    "float_values = [float(value) for value in A_NBO_values]\n",
    "\n",
    "\n",
    "NBO_dist_2_a_b_diff = X_nbo['NBO-dist-2-a-b-diff'].tolist()\n",
    "NBO_dist_2_a_b_diff = [float(value) for value in NBO_dist_2_a_b_diff]\n",
    "\n",
    "NBO_dist_3_2_b_avg_diff = X_nbo['NBO-dist-3-2-b-avg-diff'].tolist()\n",
    "NBO_dist_3_2_b_avg_diff = [float(value) for value in NBO_dist_2_a_b_diff]\n",
    "\n",
    "vbur_octant_2 = X_2['% VBur octant 2'].tolist()\n",
    "vbur_octant_2 = [float(value) for value in vbur_octant_2]\n",
    "\n",
    "\n",
    "#Create PCA Plot in 3D\n",
    "pca = PCA(n_components=3)\n",
    "components = pca.fit_transform(X_nbo)\n",
    "\n",
    "total_var = pca.explained_variance_ratio_.sum() * 100\n",
    "\n",
    "fig = px.scatter_3d(\n",
    "    components,\n",
    "    x=0,\n",
    "    y=1,\n",
    "    z=2,\n",
    "    # text=names,\n",
    "    color = percent_buried_volume,\n",
    "    title=f'Total Explained Variance: {total_var:.2f}%',\n",
    "    labels={'0': 'PC 1', '1': 'PC 2', '2': 'PC 3'}\n",
    ")\n",
    "\n",
    "fig.update_layout(width=800, \n",
    "                  height=600,\n",
    "                  coloraxis_colorbar=dict(\n",
    "                  title=dict(text=\"Percent Buried<br>Volume\",\n",
    "                             side=\"top\"),\n",
    "                  x=0.85,  # Position the legend closer to the scatter plot\n",
    "                  y=0.5,\n",
    "                  xanchor='left',\n",
    "                  yanchor='middle'),\n",
    "                  title=dict(\n",
    "                  x=0.5,          # Horizontal position (centered)\n",
    "                  y=0.9,          # Vertical position (closer to the plot)\n",
    "                  xanchor='center',\n",
    "                  yanchor='top'\n",
    "                    ) \n",
    "                 )\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "ebeb2b60-bdea-43f4-a324-bd38e46ff6a9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 2, the average silhouette score is 0.721307541563031\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 3, the average silhouette score is 0.6764917127083537\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 4, the average silhouette score is 0.7389933874334447\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 5, the average silhouette score is 0.6661118754550118\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 6, the average silhouette score is 0.6230669898714697\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 7, the average silhouette score is 0.6306069011370585\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 8, the average silhouette score is 0.6516709852582935\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 9, the average silhouette score is 0.5876716242151697\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 10, the average silhouette score is 0.6661770652728465\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 11, the average silhouette score is 0.6128058390859381\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 12, the average silhouette score is 0.62190972443897\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 13, the average silhouette score is 0.6006524231736567\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 14, the average silhouette score is 0.603591350443035\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 15, the average silhouette score is 0.6012220090034905\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 16, the average silhouette score is 0.6115674236475351\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 17, the average silhouette score is 0.5951047038270281\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 18, the average silhouette score is 0.616667014306981\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 19, the average silhouette score is 0.6198182494227539\n",
      "\n",
      "The optimal number of clusters is 4 with a silhouette score of 0.7389933874334447\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "PCA Result DataFrame with Cluster Labels:\n",
      "          PC1       PC2       PC3  Cluster Ligand\n",
      "0   -2.868745  2.033627  1.991181        2    NaN\n",
      "1   -2.871057  2.004605  1.973114        2    002\n",
      "2   -2.872544  1.979464  1.913940        2    003\n",
      "3   -2.130146  0.288102 -0.874482        0    004\n",
      "4   -2.113748  0.297505 -0.888563        0    005\n",
      "..        ...       ...       ...      ...    ...\n",
      "371  5.169584 -0.283784  0.219393        1    406\n",
      "372  5.169961 -0.284650  0.220590        1    407\n",
      "373  5.168800 -0.293140  0.223108        1    408\n",
      "374  4.501760 -0.024606 -0.007489        1    409\n",
      "375  4.452521 -0.264406  0.326279        1    411\n",
      "\n",
      "[376 rows x 5 columns]\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\George\\.conda\\envs\\chem3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Generate dataframe from pca data: \n",
    "pca_df = pd.DataFrame(data=components, columns=['PC1', 'PC2','PC3'])\n",
    "\n",
    "# Apply KMeans Clustering\n",
    "# Define a range of clusters to evaluate\n",
    "range_n_clusters = list(range(2, 20))\n",
    "\n",
    "# Initialize variables to store the best number of clusters and corresponding metrics\n",
    "best_n_clusters = 0\n",
    "best_silhouette_score = -1\n",
    "inertia = []\n",
    "\n",
    "# Iterate over different cluster numbers to find the optimal number\n",
    "for n_clusters in range_n_clusters:\n",
    "    kmeans = KMeans(init=\"k-means++\", n_clusters=n_clusters, n_init=4, random_state=42)\n",
    "    cluster_labels = kmeans.fit_predict(pca_df)\n",
    "    # Calculate silhouette score\n",
    "    silhouette_avg = silhouette_score(pca_df, cluster_labels)\n",
    "    inertia.append(kmeans.inertia_)\n",
    "    \n",
    "    print(f\"For n_clusters = {n_clusters}, the average silhouette score is {silhouette_avg}\")\n",
    "    \n",
    "    # Update the best silhouette score and corresponding number of clusters\n",
    "    if silhouette_avg > best_silhouette_score:\n",
    "        best_n_clusters = n_clusters\n",
    "        best_silhouette_score = silhouette_avg\n",
    "\n",
    "# Print the optimal number of clusters and the best silhouette score\n",
    "print(f\"\\nThe optimal number of clusters is {best_n_clusters} with a silhouette score of {best_silhouette_score}\")\n",
    "\n",
    "# Plot the inertia to visualize the elbow method\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(range_n_clusters, inertia, marker='o')\n",
    "plt.title('Elbow Method For Optimal Number of Clusters')\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('Inertia')\n",
    "plt.show()\n",
    "\n",
    "# Apply KMeans with the optimal number of clusters\n",
    "kmeans = KMeans(n_clusters=best_n_clusters, random_state=42)\n",
    "cluster_labels = kmeans.fit_predict(pca_df)\n",
    "\n",
    "# Add cluster labels to the PCA result DataFrame\n",
    "pca_df['Cluster'] = cluster_labels\n",
    "pca_df['Ligand'] = X['ligand_id']\n",
    "\n",
    "\n",
    "# Print the PCA result DataFrame with cluster labels\n",
    "print(\"\\nPCA Result DataFrame with Cluster Labels:\")\n",
    "print(pca_df)\n",
    "\n",
    "\n",
    "# Create a dictionary to store clusters and their corresponding ligands\n",
    "cluster_dict_nbo_descriptor = {}\n",
    "for cluster_num in range(best_n_clusters):\n",
    "    cluster_key = f'c{cluster_num + 1}'  # Create cluster key like 'c1', 'c2', etc.\n",
    "    ligands_in_cluster = pca_df[pca_df['Cluster'] == cluster_num]['Ligand'].tolist()\n",
    "    cluster_dict_nbo_descriptor[cluster_key] = ligands_in_cluster\n",
    "\n",
    "# Print the dictionary\n",
    "print(cluster_dict_nbo_descriptor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "616768a7-e535-4f1b-b8f7-ed1cd1b6bd81",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json \n",
    "\n",
    "data = {\n",
    "    'cluster_dict_all_descriptor' : cluster_dict_all_descriptor,\n",
    "    'cluster_dict_nbo_descriptor' : cluster_dict_nbo_descriptor,\n",
    "    'cluster_dict_vbur_descriptor' : cluster_dict_vbur_descriptor\n",
    "    }\n",
    "\n",
    "# Convert the combined dictionary to a JSON string\n",
    "json_data = json.dumps(data)\n",
    "\n",
    "# Write the JSON string to a .txt file\n",
    "with open('cluster_dictionaries.txt', 'w') as file:\n",
    "    file.write(json_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "ebc30028",
   "metadata": {},
   "outputs": [
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     "execution_count": 88,
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   ],
   "source": [
    "cluster_dict_all_descriptor"
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  },
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   "cell_type": "code",
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   "id": "cdc2638f-508e-4832-9a99-6b023f9b1150",
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