test_test_consensus.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 621,
   "metadata": {},
   "outputs": [],
   "source": [
    "# My library\n",
    "from molgraph.dataset import *\n",
    "from molgraph.testingconsensus import *\n",
    "# pytorch\n",
    "import torch\n",
    "\n",
    "# Ensure that all operations are deterministic on GPU (if used) for reproducibility\n",
    "torch.backends.cudnn.determinstic = True\n",
    "torch.backends.cudnn.benchmark = False"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Argument"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 622,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Namespace(batch_normalize=True, batch_size=256, class_number=1, device=device(type='cpu'), dropout=0.4, edge_dim=32, experiment_number='001', file='t02_aqsoldb_9982', fold=5, gpu=-1, grad_norm=1.0, graphtask='regression', heads=1, hidden_channels=256, in_channels=256, lr=5e-05, lr_schedule=True, model='GIN', mol_embedding=256, num_epochs=300, num_layers=3, num_layers_reduced=3, num_layers_self=3, num_layers_self_reduced=3, out_channels=256, patience=30, reduced=['functional'], schema='AR_0', seed=42, smiles='SMILES', splitting='random', splitting_seed=42, task='Solubility', vocab_len=100, weight_decay=5e-05)"
      ]
     },
     "execution_count": 622,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parser = ArgumentParser()\n",
    "args = parser.getArgument('''\n",
    "--file bbbp\n",
    "--model GIN\n",
    "--schema AR_0\n",
    "--reduced functional\n",
    "--vocab_len 100\n",
    "--mol_embedding 256\n",
    "--batch_normalize\n",
    "--fold 5\n",
    "'''.split())\n",
    "args"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 623,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Function: getDataset()\n",
      "number of valid smiles: 9945\n",
      "Function: generateDatasetSplitting()\n",
      "Fold: 5\n",
      "Fold Number: 0\n",
      "-- Datasets Train: 6365\n",
      "-- Datasets Val: 1591\n",
      "-- Datasets Test:  1989\n",
      "-- Total: 9945\n",
      "Fold Number: 1\n",
      "-- Datasets Train: 6365\n",
      "-- Datasets Val: 1591\n",
      "-- Datasets Test:  1989\n",
      "-- Total: 9945\n",
      "Fold Number: 2\n",
      "-- Datasets Train: 6365\n",
      "-- Datasets Val: 1591\n",
      "-- Datasets Test:  1989\n",
      "-- Total: 9945\n",
      "Fold Number: 3\n",
      "-- Datasets Train: 6365\n",
      "-- Datasets Val: 1591\n",
      "-- Datasets Test:  1989\n",
      "-- Total: 9945\n",
      "Fold Number: 4\n",
      "-- Datasets Train: 6364\n",
      "-- Datasets Val: 1592\n",
      "-- Datasets Test:  1989\n",
      "-- Total: 9945\n",
      "Function: generateGraphDataset()\n",
      "Datasets graph:  9945\n"
     ]
    }
   ],
   "source": [
    "file = args.file\n",
    "smiles = args.smiles \n",
    "task = args.task\n",
    "splitting = args.splitting \n",
    "splitting_fold = args.fold\n",
    "splitting_seed = args.splitting_seed\n",
    "\n",
    "# get validated dataset\n",
    "datasets = getDataset(file, smiles, task, splitting)\n",
    "# compute positive weight for classification\n",
    "if args.graphtask == 'classification':\n",
    "    args.pos_weight = getPosWeight(datasets)\n",
    "    print('pos_weight:', args.pos_weight)\n",
    "# generate dataset splitting\n",
    "datasets_splitted = generateDatasetSplitting(file, splitting, splitting_fold, splitting_seed)\n",
    "# generate all graph dataset\n",
    "datasets_graph = generateGraphDataset(file)\n",
    "# generate all reduced graph dataset\n",
    "dict_reducedgraph = dict()\n",
    "for g in args.reduced:\n",
    "    if g == 'substructure':\n",
    "        for i in range(splitting_fold):\n",
    "            vocab_file = file+'_'+str(i)\n",
    "            if not os.path.exists('vocab/'+vocab_file+'.txt'):\n",
    "                generateVocabTrain(file, splitting_seed, splitting_fold, vocab_len=args.vocab_len)\n",
    "            dict_reducedgraph[g] = generateReducedGraphDict(file, g, vocab_file=vocab_file)\n",
    "    else:\n",
    "        dict_reducedgraph[g] = generateReducedGraphDict(file, g)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 625,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "args_test = dict()\n",
    "\n",
    "# Load model\n",
    "ts = \"2023-Apr-10-17:15:04\"\n",
    "args_test = dict()\n",
    "reduced_list = '_'.join(args.reduced)\n",
    "# directory = './dataset/'+os.path.join(*[args.file, args.model+'_'+args.schema+'_'+reduced_list])\n",
    "# ts = next(os.walk(directory))[1][0]\n",
    "reduced_list = '_'.join(args.reduced)\n",
    "args_test['log_folder_name'] = os.path.join(*[args.file, args.model+'_'+args.schema+'_'+reduced_list, f\"{ts}\"])\n",
    "args_test['exp_name'] = args.experiment_number\n",
    "# args_test['fold_number'] = fold_number\n",
    "args_test['seed'] = args.seed\n",
    "\n",
    "training_bin = torch.load('./dataset/'+args_test['log_folder_name']+'/checkpoints/training_args.bin')\n",
    "\n",
    "args.batch_size = training_bin.batch_size\n",
    "args.num_layers = training_bin.num_layers\n",
    "args.num_layers_reduced = training_bin.num_layers_reduced\n",
    "args.in_channels = training_bin.in_channels\n",
    "args.hidden_channels = training_bin.hidden_channels\n",
    "args.out_channels = training_bin.out_channels\n",
    "args.edge_dim = training_bin.edge_dim\n",
    "args.num_layers_self = training_bin.num_layers_self\n",
    "args.num_layers_self_reduced = training_bin.num_layers_self_reduced\n",
    "args.dropout = training_bin.dropout\n",
    "args.lr = training_bin.lr\n",
    "args.weight_decay = training_bin.weight_decay\n",
    "# print(args)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 634,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # test with single sample\n",
    "testing_type = 'sample'\n",
    "testing_smiles = 'CCCCCCC(=O)OCC(O)CO'\n",
    "smiles_processes = mol_to_smiles(smiles_to_mol(testing_smiles, with_atom_index=False))\n",
    "molecule_test = constructGraph(smiles_processes, 0)\n",
    "datasets_test = {testing_smiles: molecule_test}\n",
    "\n",
    "# test dataset original\n",
    "# testing_type = 'test_dataset'\n",
    "# test_loader, datasets_test =  generateDataLoaderTesting(args.file, args.batch_size)\n",
    "\n",
    "# test dataset external\n",
    "# testing_type = 'external'\n",
    "# test_external = 'test_external.csv'\n",
    "# datasets_test_df = pd.read_csv('dataset/'+args.file+'/'+test_external)\n",
    "# datasets_test_list = []\n",
    "# for idx, row in datasets_test_df.iterrows():\n",
    "#     smiles_processes = mol_to_smiles(smiles_to_mol(row['X'], with_atom_index=False))\n",
    "#     d = constructGraph(smiles_processes, row['y'])\n",
    "#     datasets_test_list.append(d)\n",
    "# test_loader, datasets_test =  generateDataLoaderListing(datasets_test_list, 1)\n",
    "# datasets_test = {d.smiles: d for d in datasets_test}\n",
    "\n",
    "# # all dataset\n",
    "# testing_type = 'dataset'\n",
    "# datasets_test = datasets_graph\n",
    "\n",
    "all_dataset = datasets_test"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 635,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of dataset: 1\n"
     ]
    }
   ],
   "source": [
    "print('Number of dataset:', len(all_dataset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 636,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "getMaskGraph: 100%|██████████| 1/1 [00:00<00:00, 13.25it/s]\n",
      "getMaskGraph: 100%|██████████| 1/1 [00:00<00:00, 13.91it/s]\n",
      "getMaskGraph: 100%|██████████| 1/1 [00:00<00:00, 13.63it/s]\n",
      "getMaskGraph: 100%|██████████| 1/1 [00:00<00:00, 13.92it/s]\n",
      "getMaskGraph: 100%|██████████| 1/1 [00:00<00:00, 13.89it/s]\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fold</th>\n",
       "      <th>SMILES_original</th>\n",
       "      <th>SMILES</th>\n",
       "      <th>Split</th>\n",
       "      <th>Prediction</th>\n",
       "      <th>TrueValue</th>\n",
       "      <th>Schema</th>\n",
       "      <th>Node_ID</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>CCCCCCC(=O)OCC(O)CO</td>\n",
       "      <td>C[CH2:1][CH2:2][CH2:3][CH2:4][CH2:5][C:6](=[O:...</td>\n",
       "      <td>-</td>\n",
       "      <td>-1.303499</td>\n",
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       "      <td>-</td>\n",
       "      <td>-1.303499</td>\n",
       "      <td>0.0</td>\n",
       "      <td>atom</td>\n",
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       "      <td>[a] C</td>\n",
       "      <td>0.202833</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>CCCCCCC(=O)OCC(O)CO</td>\n",
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       "      <td>-</td>\n",
       "      <td>-1.303499</td>\n",
       "      <td>0.0</td>\n",
       "      <td>atom</td>\n",
       "      <td>2</td>\n",
       "      <td>[a] C</td>\n",
       "      <td>0.600035</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>CCCCCCC(=O)OCC(O)CO</td>\n",
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       "      <td>0.615756</td>\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>CCCCCCC(=O)OCC(O)CO</td>\n",
       "      <td>C[CH2:1][CH2:2][CH2:3][CH2:4][CH2:5][C:6](=[O:...</td>\n",
       "      <td>-</td>\n",
       "      <td>-1.303499</td>\n",
       "      <td>0.0</td>\n",
       "      <td>atom</td>\n",
       "      <td>4</td>\n",
       "      <td>[a] C</td>\n",
       "      <td>0.650250</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>4</td>\n",
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       "      <td>C[CH2:1][CH2:2][CH2:3][CH2:4][CH2:5][C:6](=[O:...</td>\n",
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       "      <td>[a] C</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>4</td>\n",
       "      <td>CCCCCCC(=O)OCC(O)CO</td>\n",
       "      <td>C[CH2:1][CH2:2][CH2:3][CH2:4][CH2:5][C:6](=[O:...</td>\n",
       "      <td>-</td>\n",
       "      <td>-1.232308</td>\n",
       "      <td>0.0</td>\n",
       "      <td>x</td>\n",
       "      <td>10</td>\n",
       "      <td>[a] C</td>\n",
       "      <td>0.953825</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>4</td>\n",
       "      <td>CCCCCCC(=O)OCC(O)CO</td>\n",
       "      <td>C[CH2:1][CH2:2][CH2:3][CH2:4][CH2:5][C:6](=[O:...</td>\n",
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       "      <td>11</td>\n",
       "      <td>[a] O</td>\n",
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       "      <th>39</th>\n",
       "      <td>4</td>\n",
       "      <td>CCCCCCC(=O)OCC(O)CO</td>\n",
       "      <td>C[CH2:1][CH2:2][CH2:3][CH2:4][CH2:5][C:6](=[O:...</td>\n",
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       "      <td>-1.232308</td>\n",
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       "      <td>[a] C</td>\n",
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      ],
      "text/plain": [
       "    Fold      SMILES_original  \\\n",
       "0      0  CCCCCCC(=O)OCC(O)CO   \n",
       "1      0  CCCCCCC(=O)OCC(O)CO   \n",
       "2      0  CCCCCCC(=O)OCC(O)CO   \n",
       "3      0  CCCCCCC(=O)OCC(O)CO   \n",
       "4      0  CCCCCCC(=O)OCC(O)CO   \n",
       "..   ...                  ...   \n",
       "36     4  CCCCCCC(=O)OCC(O)CO   \n",
       "37     4  CCCCCCC(=O)OCC(O)CO   \n",
       "38     4  CCCCCCC(=O)OCC(O)CO   \n",
       "39     4  CCCCCCC(=O)OCC(O)CO   \n",
       "40     4  CCCCCCC(=O)OCC(O)CO   \n",
       "\n",
       "                                               SMILES Split  Prediction  \\\n",
       "0   C[CH2:1][CH2:2][CH2:3][CH2:4][CH2:5][C:6](=[O:...     -   -1.303499   \n",
       "1   C[CH2:1][CH2:2][CH2:3][CH2:4][CH2:5][C:6](=[O:...     -   -1.303499   \n",
       "2   C[CH2:1][CH2:2][CH2:3][CH2:4][CH2:5][C:6](=[O:...     -   -1.303499   \n",
       "3   C[CH2:1][CH2:2][CH2:3][CH2:4][CH2:5][C:6](=[O:...     -   -1.303499   \n",
       "4   C[CH2:1][CH2:2][CH2:3][CH2:4][CH2:5][C:6](=[O:...     -   -1.303499   \n",
       "..                                                ...   ...         ...   \n",
       "36  C[CH2:1][CH2:2][CH2:3][CH2:4][CH2:5][C:6](=[O:...     -   -1.232308   \n",
       "37  C[CH2:1][CH2:2][CH2:3][CH2:4][CH2:5][C:6](=[O:...     -   -1.232308   \n",
       "38  C[CH2:1][CH2:2][CH2:3][CH2:4][CH2:5][C:6](=[O:...     -   -1.232308   \n",
       "39  C[CH2:1][CH2:2][CH2:3][CH2:4][CH2:5][C:6](=[O:...     -   -1.232308   \n",
       "40  C[CH2:1][CH2:2][CH2:3][CH2:4][CH2:5][C:6](=[O:...     -   -1.232308   \n",
       "\n",
       "    TrueValue Schema  Node_ID Node_Feature    Weight  Weight_Other  \n",
       "0         0.0   atom        0        [a] C  0.162855           NaN  \n",
       "1         0.0   atom        1        [a] C  0.202833           NaN  \n",
       "2         0.0   atom        2        [a] C  0.600035           NaN  \n",
       "3         0.0   atom        3        [a] C  0.615756           NaN  \n",
       "4         0.0   atom        4        [a] C  0.650250           NaN  \n",
       "..        ...    ...      ...          ...       ...           ...  \n",
       "36        0.0      x        9        [a] C  0.632253           NaN  \n",
       "37        0.0      x       10        [a] C  0.953825           NaN  \n",
       "38        0.0      x       11        [a] O  1.000000           NaN  \n",
       "39        0.0      x       12        [a] C  1.000000           NaN  \n",
       "40        0.0      x       13        [a] O  0.706326           NaN  \n",
       "\n",
       "[205 rows x 11 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction_fold_df 205\n",
      "prediction_consensus_df 41\n"
     ]
    }
   ],
   "source": [
    "if testing_type == 'sample' or testing_type == 'external' or testing_type == 'test_dataset':\n",
    "    prediction_fold_df = getPredictionFold(args, args_test, all_dataset, datasets_splitted=None)\n",
    "    display(prediction_fold_df)\n",
    "    prediction_consensus_df = getPredictionConsensus(prediction_fold_df)\n",
    "    \n",
    "elif testing_type == 'dataset':\n",
    "    reduced_list = '_'.join(args.reduced)\n",
    "    directory = args.model+'_'+args.schema+'_'+reduced_list\n",
    "    path_fold = 'dataset/'+file+'/prediction_fold_'+args.file+'_'+directory+'.csv'\n",
    "    path_consensus = 'dataset/'+file+'/prediction_consensus_'+args.file+'_'+directory+'.csv'\n",
    "    prediction_fold_df = pd.DataFrame()\n",
    "    prediction_consensus_df = pd.DataFrame()\n",
    "\n",
    "    if os.path.exists(path_fold):\n",
    "        prediction_fold_df = pd.read_csv(path_fold)\n",
    "    else:\n",
    "        prediction_fold_df = getPredictionFold(args, args_test, all_dataset, datasets_splitted=datasets_splitted)\n",
    "        prediction_fold_df.to_csv(path_fold, index=False)\n",
    "\n",
    "    if os.path.exists(path_consensus):\n",
    "        prediction_consensus_df = pd.read_csv(path_consensus)\n",
    "    else:\n",
    "        prediction_consensus_df = getPredictionConsensus(prediction_fold_df)\n",
    "        prediction_consensus_df.to_csv(path_consensus, index=False)\n",
    "\n",
    "\n",
    "print('prediction_fold_df', len(prediction_fold_df))\n",
    "print('prediction_consensus_df', len(prediction_consensus_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 638,
   "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>Fold</th>\n",
       "      <th>SMILES_original</th>\n",
       "      <th>SMILES</th>\n",
       "      <th>Split</th>\n",
       "      <th>Prediction</th>\n",
       "      <th>TrueValue</th>\n",
       "      <th>Schema</th>\n",
       "      <th>Node_ID</th>\n",
       "      <th>Node_Feature</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Weight_Other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C</td>\n",
       "      <td>CCCCCCC(=O)OCC(O)CO</td>\n",
       "      <td>C[CH2:1][CH2:2][CH2:3][CH2:4][CH2:5][C:6](=[O:...</td>\n",
       "      <td>-</td>\n",
       "      <td>-1.457173</td>\n",
       "      <td>0.0</td>\n",
       "      <td>atom</td>\n",
       "      <td>0</td>\n",
       "      <td>[a] C</td>\n",
       "      <td>0.102593</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C</td>\n",
       "      <td>CCCCCCC(=O)OCC(O)CO</td>\n",
       "      <td>C[CH2:1][CH2:2][CH2:3][CH2:4][CH2:5][C:6](=[O:...</td>\n",
       "      <td>-</td>\n",
       "      <td>-1.457173</td>\n",
       "      <td>0.0</td>\n",
       "      <td>atom</td>\n",
       "      <td>1</td>\n",
       "      <td>[a] C</td>\n",
       "      <td>0.107128</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>CCCCCCC(=O)OCC(O)CO</td>\n",
       "      <td>C[CH2:1][CH2:2][CH2:3][CH2:4][CH2:5][C:6](=[O:...</td>\n",
       "      <td>-</td>\n",
       "      <td>-1.457173</td>\n",
       "      <td>0.0</td>\n",
       "      <td>atom</td>\n",
       "      <td>2</td>\n",
       "      <td>[a] C</td>\n",
       "      <td>0.301124</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C</td>\n",
       "      <td>CCCCCCC(=O)OCC(O)CO</td>\n",
       "      <td>C[CH2:1][CH2:2][CH2:3][CH2:4][CH2:5][C:6](=[O:...</td>\n",
       "      <td>-</td>\n",
       "      <td>-1.457173</td>\n",
       "      <td>0.0</td>\n",
       "      <td>atom</td>\n",
       "      <td>3</td>\n",
       "      <td>[a] C</td>\n",
       "      <td>0.305075</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C</td>\n",
       "      <td>CCCCCCC(=O)OCC(O)CO</td>\n",
       "      <td>C[CH2:1][CH2:2][CH2:3][CH2:4][CH2:5][C:6](=[O:...</td>\n",
       "      <td>-</td>\n",
       "      <td>-1.457173</td>\n",
       "      <td>0.0</td>\n",
       "      <td>atom</td>\n",
       "      <td>4</td>\n",
       "      <td>[a] C</td>\n",
       "      <td>0.304612</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Fold      SMILES_original  \\\n",
       "0    C  CCCCCCC(=O)OCC(O)CO   \n",
       "1    C  CCCCCCC(=O)OCC(O)CO   \n",
       "2    C  CCCCCCC(=O)OCC(O)CO   \n",
       "3    C  CCCCCCC(=O)OCC(O)CO   \n",
       "4    C  CCCCCCC(=O)OCC(O)CO   \n",
       "\n",
       "                                              SMILES Split  Prediction  \\\n",
       "0  C[CH2:1][CH2:2][CH2:3][CH2:4][CH2:5][C:6](=[O:...     -   -1.457173   \n",
       "1  C[CH2:1][CH2:2][CH2:3][CH2:4][CH2:5][C:6](=[O:...     -   -1.457173   \n",
       "2  C[CH2:1][CH2:2][CH2:3][CH2:4][CH2:5][C:6](=[O:...     -   -1.457173   \n",
       "3  C[CH2:1][CH2:2][CH2:3][CH2:4][CH2:5][C:6](=[O:...     -   -1.457173   \n",
       "4  C[CH2:1][CH2:2][CH2:3][CH2:4][CH2:5][C:6](=[O:...     -   -1.457173   \n",
       "\n",
       "   TrueValue Schema  Node_ID Node_Feature    Weight  Weight_Other  \n",
       "0        0.0   atom        0        [a] C  0.102593           NaN  \n",
       "1        0.0   atom        1        [a] C  0.107128           NaN  \n",
       "2        0.0   atom        2        [a] C  0.301124           NaN  \n",
       "3        0.0   atom        3        [a] C  0.305075           NaN  \n",
       "4        0.0   atom        4        [a] C  0.304612           NaN  "
      ]
     },
     "execution_count": 638,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_consensus_df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interpretation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 639,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CCCCCCC(=O)OCC(O)CO\n",
      "Prediction Result (class): {-1.4571730741438103}\n",
      "graph_g {'atom': {0: 0.09219603982424063, 1: 0.09688800999605568, 2: 0.2975638624793222, 3: 0.3016506285939143, 4: 0.3011715918961939, 5: 0.298387546014162, 6: 0.0, 7: 0.5228028901667442, 8: 0.2282834509181551, 9: 0.4626024856848898, 10: 0.2962427156539102, 11: 0.9999999999999999, 12: 0.3465464898293187, 13: 0.9195562360467278}, 'bond': {}}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[10:11:31] The new font size 1 is below the current minimum (6).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "graph_r {'atom': {0: 0.8923558682695006, 1: 0.9999999999999999, 2: 0.7486566846697409, 3: 0.22523166120085622, 4: 0.1827746863979188, 5: 0.18792841730947718, 6: 0.1813166115935752, 7: 0.16842148027877402, 8: 0.08477411178894922, 9: 0.0, 10: 0.09184311980146492, 11: 0.19046997137833713, 12: 0.5658308155859211}, 'bond': {}}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[10:11:31] The new font size 1 is below the current minimum (6).\n",
      "[10:11:33] The new font size 1 is below the current minimum (6).\n",
      "[10:11:33] The new font size 1 is below the current minimum (6).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "graph_x {'atom': {0: 0.0, 1: 0.1270344932953149, 2: 0.27567392882950054, 3: 0.29464644774661913, 4: 0.29388676013433257, 5: 0.22203046794933945, 6: 0.42794734577471655, 7: 0.5213961144504042, 8: 0.1287136074825122, 9: 0.39309697242305586, 10: 0.9773998756197465, 11: 1.0, 12: 0.9155173556639049, 13: 0.9122041972789501}, 'bond': {}}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'prediction': -1.4571730741438103,\n",
       " 'graph_g': {'atom': {0: 0.09219603982424063,\n",
       "   1: 0.09688800999605568,\n",
       "   2: 0.2975638624793222,\n",
       "   3: 0.3016506285939143,\n",
       "   4: 0.3011715918961939,\n",
       "   5: 0.298387546014162,\n",
       "   6: 0.0,\n",
       "   7: 0.5228028901667442,\n",
       "   8: 0.2282834509181551,\n",
       "   9: 0.4626024856848898,\n",
       "   10: 0.2962427156539102,\n",
       "   11: 0.9999999999999999,\n",
       "   12: 0.3465464898293187,\n",
       "   13: 0.9195562360467278},\n",
       "  'bond': {}},\n",
       " 'graph_r': {'atom': {0: 0.8923558682695006,\n",
       "   1: 0.9999999999999999,\n",
       "   2: 0.7486566846697409,\n",
       "   3: 0.22523166120085622,\n",
       "   4: 0.1827746863979188,\n",
       "   5: 0.18792841730947718,\n",
       "   6: 0.1813166115935752,\n",
       "   7: 0.16842148027877402,\n",
       "   8: 0.08477411178894922,\n",
       "   9: 0.0,\n",
       "   10: 0.09184311980146492,\n",
       "   11: 0.19046997137833713,\n",
       "   12: 0.5658308155859211},\n",
       "  'bond': {}},\n",
       " 'graph_x': {'atom': {0: 0.0,\n",
       "   1: 0.1270344932953149,\n",
       "   2: 0.27567392882950054,\n",
       "   3: 0.29464644774661913,\n",
       "   4: 0.29388676013433257,\n",
       "   5: 0.22203046794933945,\n",
       "   6: 0.42794734577471655,\n",
       "   7: 0.5213961144504042,\n",
       "   8: 0.1287136074825122,\n",
       "   9: 0.39309697242305586,\n",
       "   10: 0.9773998756197465,\n",
       "   11: 1.0,\n",
       "   12: 0.9155173556639049,\n",
       "   13: 0.9122041972789501},\n",
       "  'bond': {}}}"
      ]
     },
     "execution_count": 639,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 3000x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if testing_type == 'sample':\n",
    "    smiles_test = Chem.MolToSmiles(Chem.MolFromSmiles(testing_smiles))\n",
    "else:\n",
    "    smiles_test = Chem.MolToSmiles(Chem.MolFromSmiles('O=C1NC(=O)SC1Cc4ccc(OCC3(Oc2c(c(c(O)c(c2CC3)C)C)C)C)cc4'))\n",
    "    \n",
    "prediction_mol_df = prediction_consensus_df[prediction_consensus_df['SMILES_original'] == smiles_test]\n",
    "prediction_mol_df\n",
    "print(smiles_test)\n",
    "getPrediction(prediction_mol_df, args, print_result=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 640,
   "metadata": {},
   "outputs": [],
   "source": [
    "if args.graphtask == 'classification' and len(prediction_consensus_df) > 1:\n",
    "    prediction_consensus_df_unique = prediction_consensus_df.drop_duplicates(subset=['SMILES_original'])\n",
    "    print(accuracy_score(prediction_consensus_df_unique['TrueValue'], [i>=0.5 for i in prediction_consensus_df_unique['Prediction']]))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "myenv38",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}