atom_feature_T_response_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bfeb39cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using backend: tensorflow\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import rdkit\n",
    "from main import create_input, create_model, predict\n",
    "from argparse import ArgumentParser\n",
    "from molgraph import single_mol_from_supergraph\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0ab098f",
   "metadata": {},
   "source": [
    "# Run predictive model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f2eb1bf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "parser = ArgumentParser()\n",
    "parser.add_argument('-predict', action=\"store_true\", default=True)\n",
    "parser.add_argument('-watsoneq', action='store_true', default=False)\n",
    "parser.add_argument('-K_fold', action='store_true', default=False)\n",
    "parser.add_argument('-maxatoms', type=int, default=64)\n",
    "parser.add_argument('-lr', type=float, default=5.0e-4)\n",
    "parser.add_argument('-epoch', type=int, default=100)\n",
    "parser.add_argument('-batchsize', type=int, default=256)\n",
    "parser.add_argument('-layers', type=int, default=5)\n",
    "parser.add_argument('-heads', type=int, default=5)\n",
    "parser.add_argument('-residcon', action=\"store_true\", default=True)\n",
    "parser.add_argument('-explicitH', action=\"store_true\", default=False)\n",
    "parser.add_argument('-dropout', type=float, default=0.0)\n",
    "parser.add_argument('-modelname', type=str, default='best_211007')\n",
    "parser.add_argument('-num_hidden', type=int, default=32)\n",
    "parser.add_argument('-train_only', action=\"store_true\", default=False)\n",
    "parser.add_argument('-sw_thr', type=float, default=0.0)\n",
    "parser.add_argument('-sw_decay', type=int, default=1)\n",
    "parser.add_argument('-loss', type=str, default='kl_div_normal')\n",
    "args = parser.parse_args('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d9f88cb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/Data_211005.csv')\n",
    "tmp = []\n",
    "#for smi in df.smiles.unique():\n",
    "#    tmp.append([smi,298.15])\n",
    "#pd.DataFrame(tmp).to_csv('molecules_to_predict.csv', header=['smiles','temperature'], index=False)\n",
    "pd.DataFrame(df[['smiles','Train/Valid/Test','temperature','HoV (kJ/mol)']]).to_csv('molecules_to_predict.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ca09dbbd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<gnn.GAT_unc object at 0x102c52100>\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "device ='/cpu:0'\n",
    "data = pd.read_csv('molecules_to_predict.csv')\n",
    "data['total_atoms'] = [ rdkit.Chem.MolFromSmiles(smi).GetNumHeavyAtoms() for smi in data.smiles]\n",
    "INPUT = create_input(data, device, args)\n",
    "data, num_mols, T, Graphs, seg = INPUT\n",
    "atom_feat_dim = Graphs.ndata['feat'].shape[-1]\n",
    "model, model_name = create_model(args, atom_feat_dim, '')\n",
    "################\n",
    "Predicted_HoV, atom_features_each_layer, Attention_each_layer, before_T_update, after_T_update = predict(model, \\\n",
    "                                                                                  Graphs.ndata['feat'], Graphs, \\\n",
    "                                                         seg, args.maxatoms, T, '', num_mols, mu_s_NLR=[])\n",
    "if len(Predicted_HoV.shape) == 2: # mean+std learning\n",
    "    pred_mean, pred_stddev = Predicted_HoV[:, 0], Predicted_HoV[:, 1]\n",
    "    data['Predicted'] = pred_mean.numpy()\n",
    "    data['ML_unc'] = pred_stddev.numpy()\n",
    "else:\n",
    "    data['Predicted'] = Predicted_HoV.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8b4a9a3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.213752654098938"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Just to test if the model was loaded properly\n",
    "valid = data[data['Train/Valid/Test'] == 'Valid']\n",
    "np.abs(valid.Predicted - valid['HoV (kJ/mol)']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "531e63d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>smiles</th>\n",
       "      <th>Train/Valid/Test</th>\n",
       "      <th>temperature</th>\n",
       "      <th>HoV (kJ/mol)</th>\n",
       "      <th>total_atoms</th>\n",
       "      <th>Predicted</th>\n",
       "      <th>ML_unc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>137734</th>\n",
       "      <td>C</td>\n",
       "      <td>Test</td>\n",
       "      <td>150.0000</td>\n",
       "      <td>6.61901</td>\n",
       "      <td>1</td>\n",
       "      <td>87.557961</td>\n",
       "      <td>22.069706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137736</th>\n",
       "      <td>C</td>\n",
       "      <td>Test</td>\n",
       "      <td>90.6941</td>\n",
       "      <td>8.73150</td>\n",
       "      <td>1</td>\n",
       "      <td>90.610909</td>\n",
       "      <td>23.045452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137735</th>\n",
       "      <td>C</td>\n",
       "      <td>Test</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>8.51043</td>\n",
       "      <td>1</td>\n",
       "      <td>89.892754</td>\n",
       "      <td>22.790493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137737</th>\n",
       "      <td>C#C</td>\n",
       "      <td>Test</td>\n",
       "      <td>308.3950</td>\n",
       "      <td>0.01000</td>\n",
       "      <td>2</td>\n",
       "      <td>10.274034</td>\n",
       "      <td>6.190171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137738</th>\n",
       "      <td>C#C</td>\n",
       "      <td>Test</td>\n",
       "      <td>191.6600</td>\n",
       "      <td>16.11890</td>\n",
       "      <td>2</td>\n",
       "      <td>15.257702</td>\n",
       "      <td>4.088116</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124098</th>\n",
       "      <td>c1coc(Cc2ccco2)c1</td>\n",
       "      <td>Train</td>\n",
       "      <td>440.0000</td>\n",
       "      <td>42.35520</td>\n",
       "      <td>11</td>\n",
       "      <td>42.299168</td>\n",
       "      <td>2.965742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124099</th>\n",
       "      <td>c1coc(Cc2ccco2)c1</td>\n",
       "      <td>Train</td>\n",
       "      <td>300.0000</td>\n",
       "      <td>52.88470</td>\n",
       "      <td>11</td>\n",
       "      <td>51.890205</td>\n",
       "      <td>5.720402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124078</th>\n",
       "      <td>c1coc(Cc2ccco2)c1</td>\n",
       "      <td>Train</td>\n",
       "      <td>480.0000</td>\n",
       "      <td>38.85640</td>\n",
       "      <td>11</td>\n",
       "      <td>38.877991</td>\n",
       "      <td>2.603239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124091</th>\n",
       "      <td>c1coc(Cc2ccco2)c1</td>\n",
       "      <td>Train</td>\n",
       "      <td>400.0000</td>\n",
       "      <td>45.58840</td>\n",
       "      <td>11</td>\n",
       "      <td>45.296219</td>\n",
       "      <td>3.306897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124080</th>\n",
       "      <td>c1coc(Cc2ccco2)c1</td>\n",
       "      <td>Train</td>\n",
       "      <td>500.0000</td>\n",
       "      <td>36.97130</td>\n",
       "      <td>11</td>\n",
       "      <td>37.073223</td>\n",
       "      <td>2.567383</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>153105 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   smiles Train/Valid/Test  temperature  HoV (kJ/mol)  \\\n",
       "137734                  C             Test     150.0000       6.61901   \n",
       "137736                  C             Test      90.6941       8.73150   \n",
       "137735                  C             Test     100.0000       8.51043   \n",
       "137737                C#C             Test     308.3950       0.01000   \n",
       "137738                C#C             Test     191.6600      16.11890   \n",
       "...                   ...              ...          ...           ...   \n",
       "124098  c1coc(Cc2ccco2)c1            Train     440.0000      42.35520   \n",
       "124099  c1coc(Cc2ccco2)c1            Train     300.0000      52.88470   \n",
       "124078  c1coc(Cc2ccco2)c1            Train     480.0000      38.85640   \n",
       "124091  c1coc(Cc2ccco2)c1            Train     400.0000      45.58840   \n",
       "124080  c1coc(Cc2ccco2)c1            Train     500.0000      36.97130   \n",
       "\n",
       "        total_atoms  Predicted     ML_unc  \n",
       "137734            1  87.557961  22.069706  \n",
       "137736            1  90.610909  23.045452  \n",
       "137735            1  89.892754  22.790493  \n",
       "137737            2  10.274034   6.190171  \n",
       "137738            2  15.257702   4.088116  \n",
       "...             ...        ...        ...  \n",
       "124098           11  42.299168   2.965742  \n",
       "124099           11  51.890205   5.720402  \n",
       "124078           11  38.877991   2.603239  \n",
       "124091           11  45.296219   3.306897  \n",
       "124080           11  37.073223   2.567383  \n",
       "\n",
       "[153105 rows x 7 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ee1cf72a",
   "metadata": {},
   "outputs": [],
   "source": [
    "before_T_update = before_T_update.numpy()\n",
    "after_T_update = after_T_update.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f077dd7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 153105/153105 [00:07<00:00, 20620.61it/s]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "norms = []\n",
    "for i in tqdm(range(len(before_T_update))):\n",
    "    norms.append( np.linalg.norm(before_T_update[i] - after_T_update[i], 2) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "19efa919",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x284fee280>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "sns.set(context='talk', style='ticks',\n",
    "        color_codes=True, rc={'legend.frameon': False})\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "matplotlib.rcParams['figure.dpi'] = 300\n",
    "plt.rcParams[\"font.family\"] = 'Arial'\n",
    "plt.rcParams.update({'font.size': 24})\n",
    "\n",
    "plt.scatter(data['temperature'], norms, s=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "15f71253",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9900646865907132"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.corrcoef(data['temperature'], norms)[0][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6eb35773",
   "metadata": {},
   "outputs": [],
   "source": [
    "import copy\n",
    "from rdkit.Chem import rdDepictor\n",
    "from rdkit.Chem.Draw import rdMolDraw2D\n",
    "from IPython.display import SVG\n",
    "from IPython.display import display, Image\n",
    "import matplotlib\n",
    "import matplotlib.cm as cm\n",
    "from rdkit import Chem\n",
    "\n",
    "def drawmol(smiles, atom_attention_scores):\n",
    "    # need to replace atom_weights, bg.number_of_nodes\n",
    "    \n",
    "    norm = matplotlib.colors.Normalize(vmin=35, vmax=89)\n",
    "    cmap = cm.get_cmap('Greens')\n",
    "    plt_colors = cm.ScalarMappable(norm=norm, cmap=cmap)\n",
    "    atom_colors = {i: plt_colors.to_rgba(atom_attention_scores[i]) for i in range(len(atom_attention_scores))}\n",
    "\n",
    "    mol = Chem.MolFromSmiles(smiles)   \n",
    "    rdDepictor.Compute2DCoords(mol)\n",
    "    drawer = rdMolDraw2D.MolDraw2DSVG(600, 600)\n",
    "    drawer.SetFontSize(30)\n",
    "    \n",
    "    mol = rdMolDraw2D.PrepareMolForDrawing(mol)\n",
    "    drawer.DrawMolecule(mol, highlightAtoms=range(len(atom_attention_scores)),\n",
    "                             highlightBonds=[],\n",
    "                             highlightAtomColors=atom_colors)\n",
    "    drawer.FinishDrawing()\n",
    "    svg = drawer.GetDrawingText()\n",
    "    svg = svg.replace('svg:', '')\n",
    "    return svg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2d2651ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "#smiles_of_interest = 'CCCC(O)CC(=O)OCC'\n",
    "smiles_of_interest = 'CCCCCC(=O)OCC'\n",
    "row_indices = [i for i, x in enumerate(list(data.smiles)) if x == smiles_of_interest]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d697d991",
   "metadata": {},
   "outputs": [],
   "source": [
    "diffs_by_atom_each_T = {}\n",
    "for i in row_indices: # i: each temperature\n",
    "    T = list(data.temperature)[i]\n",
    "    atomfeat_before, atomfeat_after = before_T_update[i], after_T_update[i]\n",
    "    \n",
    "    diffs_by_atom = []\n",
    "    for j in range(Chem.MolFromSmiles(smiles_of_interest).GetNumHeavyAtoms()): # per each atom\n",
    "        diffs_by_atom.append(np.linalg.norm(atomfeat_before[j] - atomfeat_after[j], 2))   \n",
    "    diffs_by_atom_each_T[T] = diffs_by_atom\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7d9e73f0",
   "metadata": {},
   "outputs": [
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      "400.0\n"
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   "source": [
    "for T in sorted(diffs_by_atom_each_T.keys()):\n",
    "    #print(T, min(diffs_by_atom_each_T[T]), max(diffs_by_atom_each_T[T]))\n",
    "    if T == 400.0 or T == 600.0:\n",
    "        print(T)\n",
    "        display(SVG(drawmol(smiles_of_interest, diffs_by_atom_each_T[T])))\n",
    "        #print(T, diffs_by_atom_each_T[T])"
   ]
  },
  {
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