notebooks/tutorial_custom_moleculenet.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Moleculenet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "The `MoleculeNetDataset` class is intended for datasets that consist of a table of smiles and corresponding targets and convert them into a tensors representation for graph networks. The class provides properties and methods for making graph features from smiles.\n",
    "The typical input is a `csv` or `excel` file with smiles and corresponding graph labels.\n",
    "\n",
    "The graph structure matches the molecular graph, i.e. the chemical structure. And features for atoms and bonds are generated with `RDkit` chemical informatics software.\n",
    "\n",
    "The atomic coordinates\n",
    "are generated by a conformer guess. Since this require some computation time, it is only done once and the\n",
    "molecular coordinate or mol-blocks stored in a single SDF file with the base-name of the csv file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "For demonstration, we make an artifical table of smiles and some values and store them to file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.makedirs(\"ExampleMol\", exist_ok=True)\n",
    "csv_data = \"\".join([\n",
    "    \"smiles,Values1,Values2\\n\",  # Need header!\n",
    "    \"CCC, 1, 0.1\\n\",\n",
    "    \"CCCO, 2, 0.3\\nCCCN, 3, 0.2\\n\",\n",
    "    \"CCCC=O, 4, 0.4\\n\"\n",
    "    \"NOCF, 4, 1.4\\n\"\n",
    "])\n",
    "with open(\"ExampleMol/data.csv\", \"w\") as f:\n",
    "    f.write(csv_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "The file structure is:\n",
    "\n",
    "\n",
    "```bash\n",
    "├── ExampleMol\n",
    "    ├── data.csv\n",
    "    └── data.sdf  # After prepare_data\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR:kgcnn.mol.convert:Can not import `RDKit` package for conversion.\n"
     ]
    }
   ],
   "source": [
    "from kgcnn.data.moleculenet import MoleculeNetDataset, OneHotEncoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## 1. Initialization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "To load the dataset from memeory the ``MoleculeNetDataset`` class requires the information of the directory the data is in and the name of the csv-file. Also recommended is to provide a name of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "dts = MoleculeNetDataset(file_name=\"data.csv\", \n",
    "                         data_directory=\"ExampleMol/\", \n",
    "                         dataset_name=\"ExampleMol\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## 2. Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Precompute the molecular structure and possibly also coordinates and cache the information to file as SDF mol table format in the same folder as provided in the class initialization. The structure generation can be run in parallel but the SDF file generated may be large and must still fit in memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:kgcnn.data.ExampleMol:Generating molecules and store ExampleMol/data.sdf to disk...\n",
      "INFO:kgcnn.data.ExampleMol: ... converted molecules 5 from 5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<MoleculeNetDataset []>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dts.prepare_data(\n",
    "    overwrite=True, \n",
    "    smiles_column_name=\"smiles\", \n",
    "    add_hydrogen=True,\n",
    "    make_conformers=True,\n",
    "    optimize_conformer=True,\n",
    "    num_workers=None  # Default is #cpus\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## 3. Read Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "After ``prepare_data()`` is called, the cached mol-file can be read directly from the data-directory.\n",
    "The reading step can also define the labels or targets to assigning property `graph_labels` from the column of the csv table. By default a simple graph is generated without attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:kgcnn.data.ExampleMol:Read molecules from mol-file.\n",
      "INFO:kgcnn.data.ExampleMol: ... process molecules 0 from 5\n",
      "INFO:kgcnn.mol.encoder:OneHotEncoder Symbol found ['C', 'O', 'N', 'F']\n",
      "INFO:kgcnn.mol.encoder:OneHotEncoder Hybridization found [rdkit.Chem.rdchem.HybridizationType.SP3, rdkit.Chem.rdchem.HybridizationType.SP2]\n",
      "INFO:kgcnn.mol.encoder:OneHotEncoder TotalDegree found [4, 2, 3, 1]\n",
      "INFO:kgcnn.mol.encoder:OneHotEncoder TotalNumHs found [3, 2, 1, 0]\n",
      "INFO:kgcnn.mol.encoder:OneHotEncoder CIPCode found [None]\n",
      "INFO:kgcnn.mol.encoder:OneHotEncoder ChiralityPossible found [None]\n",
      "INFO:kgcnn.mol.encoder:OneHotEncoder BondType found [rdkit.Chem.rdchem.BondType.SINGLE, rdkit.Chem.rdchem.BondType.DOUBLE]\n",
      "INFO:kgcnn.mol.encoder:OneHotEncoder Stereo found [rdkit.Chem.rdchem.BondStereo.STEREONONE]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of graphs: 5\n"
     ]
    }
   ],
   "source": [
    "dts.read_in_memory(\n",
    "    label_column_name=[\"Values1\", \"Values2\"], \n",
    "    add_hydrogen=False,  # We remove H's \n",
    "    has_conformers=True  # We keep strucutre\n",
    ")\n",
    "print(\"Number of graphs:\", len(dts))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## 4. Setting Attributes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "For molecular graphs nodes, edges or atoms and bonds should have attributes that are for `MoleculeNetDataset` generated from `RDkit` that includes chirality, stereo information (etc). Note that if a smile can not be processed by `RDkit` the graph will not have attributes.\n",
    "\n",
    "This can be achieved by setting a list of identifiers of predefined attributes or supply custom functions.\n",
    "Additionally an encoder can be provided to cast or transform the `RDkit` data formats into a list or value that eventually be cast into numpy `dtype=\"float\"` array. Also one-hot-encoding or mapping on to distributions can be handled by encoders. Attributes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atoms: ['NumBonds', 'AtomicNum', 'AtomMapNum', 'Idx', 'Degree', 'TotalDegree', 'Symbol', 'NumExplicitHs', 'NumImplicitHs', 'TotalNumHs', 'IsAromatic', 'Isotope', 'TotalValence', 'Mass', 'IsInRing', 'Hybridization', 'NoImplicit', 'ChiralTag', 'FormalCharge', 'ExplicitValence', 'ImplicitValence', 'NumRadicalElectrons', 'HasOwningMol', 'PDBResidueInfo', 'MonomerInfo', 'Smarts', 'CIPCode', 'CIPRank', 'ChiralityPossible', 'MolFileRLabel', 'GasteigerCharge', 'GasteigerHCharge', 'AtomFeatures', 'DescribeQuery']\n",
      "Bonds: ['BondType', 'IsAromatic', 'IsConjugated', 'IsInRing', 'Stereo', 'Idx', 'BeginAtom', 'BeginAtomIdx', 'BondDir', 'BondTypeAsDouble', 'EndAtom', 'EndAtomIdx', 'Smarts', 'DescribeQuery']\n",
      "Molecule: ['C', 'N', 'O', 'H', 'S', 'F', 'Cl', 'NumAtoms', 'AtomsIsInRing', 'AtomsIsAromatic', 'NumBonds', 'BondsIsConjugated', 'BondsIsAromatic', 'NumRotatableBonds', 'ExactMolWt', 'FpDensityMorgan3', 'FractionCSP3', 'MolLogP', 'MolMR', 'fr_Al_COO', 'fr_Ar_COO', 'fr_Al_OH', 'fr_Ar_OH', 'fr_C_O_noCOO', 'fr_NH2', 'fr_SH', 'fr_sulfide', 'fr_alkyl_halide']\n"
     ]
    }
   ],
   "source": [
    "# Class to make attributes used by MoleculeNetDataset\n",
    "import rdkit.Chem as Chem\n",
    "from kgcnn.mol.graph_rdkit import MolecularGraphRDKit\n",
    "mol = MolecularGraphRDKit()\n",
    "# Identifiers:\n",
    "print(\"Atoms:\", list(mol.atom_fun_dict.keys()))\n",
    "print(\"Bonds:\", list(mol.bond_fun_dict.keys()))\n",
    "print(\"Molecule:\", list(mol.mol_fun_dict.keys()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Custom functions must take a `RDkit` Mol, Atom or Bond instance as input for node edge or graph respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Or make custom function\n",
    "def mol_feature(m):\n",
    "    return m.GetNumAtoms()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Or using a callback directly for a new attribute, which takes the csv table and molecule list as argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def graph_size_callback(mg, ds):\n",
    "    return mg.mol.GetNumAtoms()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Or using custom transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def custum_trafo(mg):\n",
    "    return mg.compute_partial_charges()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:kgcnn.data.ExampleMol:Read molecules from mol-file.\n",
      "INFO:kgcnn.data.ExampleMol: ... process molecules 0 from 5\n",
      "INFO:kgcnn.mol.encoder:OneHotEncoder Symbol found ['C', 'O', 'N', 'F']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<MoleculeNetDataset [{'node_symbol': array(['C', 'C', 'C'], dtype='<U1'), 'node_number': array([6, 6, 6]), 'edge_indices': array([[0, 1],\n",
       "       [1, 0],\n",
       "       [1, 2],\n",
       "       [2, 1]], dtype=int64), 'edge_number': array([1, 1, 1, 1]), 'graph_size': array(3), 'node_coordinates': array([[ 1.0668, -0.074 , -0.0147],\n",
       "       [ 2.5858, -0.0537,  0.0053],\n",
       "       [ 3.1191,  0.9601,  1.0035]]), 'graph_labels': array([1, 0.1], dtype=object), 'node_attributes': array([[ 1.        ,  0.        ,  0.        ,  3.        , -0.06564544],\n",
       "       [ 1.        ,  0.        ,  0.        ,  2.        , -0.05903836],\n",
       "       [ 1.        ,  0.        ,  0.        ,  3.        , -0.06564544]],\n",
       "      dtype=float32), 'edge_attributes': array([[1., 0.],\n",
       "       [1., 0.],\n",
       "       [1., 0.],\n",
       "       [1., 0.]], dtype=float32), 'graph_attributes': array([44.0626,  3.    ], dtype=float32), 'size': array(3)} ...]>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dts.set_attributes(\n",
    "    # Nodes\n",
    "    nodes=[\"Symbol\", \"TotalNumHs\", \"GasteigerCharge\"], \n",
    "    encoder_nodes={\n",
    "        \"Symbol\": OneHotEncoder([\"C\", \"N\", \"O\"], dtype=\"str\", add_unknown=False)\n",
    "    },\n",
    "    # Edges\n",
    "    edges=[\"BondType\", \"Stereo\"], \n",
    "    encoder_edges = {\n",
    "        \"BondType\": int\n",
    "    },\n",
    "    # Graph-level\n",
    "    graph=[\"ExactMolWt\", mol_feature],\n",
    "    additional_callbacks= {\"size\": graph_size_callback},\n",
    "    custom_transform=custum_trafo\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## 4. Checking graphs in dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([array([6, 6, 6]),\n",
       "  array([6, 6, 6, 8]),\n",
       "  array([6, 6, 6, 7]),\n",
       "  array([6, 6, 6, 6, 8]),\n",
       "  array([7, 8, 6, 9])],\n",
       " [array(['C', 'C', 'C'], dtype='<U1'),\n",
       "  array(['C', 'C', 'C', 'O'], dtype='<U1'),\n",
       "  array(['C', 'C', 'C', 'N'], dtype='<U1'),\n",
       "  array(['C', 'C', 'C', 'C', 'O'], dtype='<U1'),\n",
       "  array(['N', 'O', 'C', 'F'], dtype='<U1')])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dts.get(\"node_number\"), dts.get(\"node_symbol\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'node_symbol': array(['C', 'C', 'C', 'C', 'O'], dtype='<U1'), 'node_number': array([6, 6, 6, 6, 8]), 'edge_indices': array([[0, 1],\n",
      "       [1, 0],\n",
      "       [1, 2],\n",
      "       [2, 1],\n",
      "       [2, 3],\n",
      "       [3, 2],\n",
      "       [3, 4],\n",
      "       [4, 3]], dtype=int64), 'edge_number': array([1, 1, 1, 1, 1, 1, 2, 2]), 'graph_size': array(5), 'node_coordinates': array([[ 1.0575,  0.0824, -0.0342],\n",
      "       [ 2.5771,  0.0621, -0.0188],\n",
      "       [ 3.1042, -1.0786,  0.8443],\n",
      "       [ 4.6098, -1.1031,  0.8629],\n",
      "       [ 5.2518, -1.938 ,  1.4947]]), 'graph_labels': array([4, 0.4], dtype=object), 'node_attributes': array([[ 1.0000000e+00,  0.0000000e+00,  0.0000000e+00,  3.0000000e+00,\n",
      "        -6.4970836e-02],\n",
      "       [ 1.0000000e+00,  0.0000000e+00,  0.0000000e+00,  2.0000000e+00,\n",
      "        -4.9520925e-02],\n",
      "       [ 1.0000000e+00,  0.0000000e+00,  0.0000000e+00,  2.0000000e+00,\n",
      "         2.7463208e-03],\n",
      "       [ 1.0000000e+00,  0.0000000e+00,  0.0000000e+00,  1.0000000e+00,\n",
      "         1.1947978e-01],\n",
      "       [ 0.0000000e+00,  0.0000000e+00,  1.0000000e+00,  0.0000000e+00,\n",
      "        -3.0340311e-01]], dtype=float32), 'edge_attributes': array([[1., 0.],\n",
      "       [1., 0.],\n",
      "       [1., 0.],\n",
      "       [1., 0.],\n",
      "       [1., 0.],\n",
      "       [1., 0.],\n",
      "       [2., 0.],\n",
      "       [2., 0.]], dtype=float32), 'graph_attributes': array([72.05752,  5.     ], dtype=float32), 'size': array(5)}\n"
     ]
    }
   ],
   "source": [
    "print(dts[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "G = nx.Graph()\n",
    "G.add_nodes_from([(i, {\"atom\": x}) for i, x in enumerate(dts.get(\"node_symbol\")[3])])\n",
    "G.add_edges_from(dts.get(\"edge_indices\")[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = nx.get_node_attributes(G, 'atom') \n",
    "nx.draw(G,labels=labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcIAAACWCAIAAADCEh9HAAAABmJLR0QA/wD/AP+gvaeTAAASWElEQVR4nO3dWVBUVxoH8NNsCqKICyIi7ijiAqigKIqJM26k4jzwMiVx5mEYJ6VgTbTQTKzWslJFHGsGalJJYWoSMZkHl3khJHHSLmiIgogaMbIocQFxwQVZAwLfPJy2uwUU+p7uPvfe/v+Kmmon9L1fKvKn7z3f/Y6BiBgAACjlIbsAAABtQ4wCAAhBjAIACEGMAgAIQYwCAAhBjAIACEGMAgAIQYwCAAhBjAIACEGMAgAIQYwCAAhBjAIACEGMAgAIQYwCAAhBjAIACEGMAgAIQYwCAAhBjAIACEGMAgAIQYwCAAhBjAIACEGMAgAIQYwCAAhBjAIACEGMAgAIQYwCAAhBjAIACEGMAuhEW1ub7BLcFGIUQCfWrVv31ltvVVdXyy7E7SBGAfSgurr63Llz+fn5kZGR27dvb2pqkl2RG0GMAujBlClTKisrU1NTOzs7P/roo+nTp+/fv7+rq0t2XW7BQESyawAAhyktLd2yZUthYSFjLCYmJjs7e8mSJbKL0jnEKIDeENHRo0e3bdt2+/ZtxlhSUtLHH388YcIE2XXpFmIUQJ9aW1v37t27d+/etrY2Pz+/zZs3f/DBB/7+/rLr0iHEKICe1dbWvv/++1999RURhYaGfvjhhykpKQaDQXZduoIYBdC/4uLi9PT04uJixlhcXFx2dnZcXJzsovQDK/UA+hcXF3f27Nnc3NwxY8YUFxfHx8e/8847Dx48kF2XTuDTqNN1d3c3NjYSUWBgoOxaQCcaGhoCAgIUXJs3Nzfv27cvMzOzvb3d39//vffe27Fjx6BBg5xRpPvAp1HnKikpSUhIWLduXWhoaHp6emNjo+yKQNs6Ozv3798fHh5+6NAhBW/39/fftWtXWVlZcnJyc3Pz7t27Z8+efeTIEYfX6V4InKO2tnb9+vX880JAQICHhwdjLDg4+PPPP+/q6pJdHWjS999/HxkZyX9yN2zYIHg0k8k0a9YsfrQ333yzrKzMETW6I8So47W3t2dlZQ0dOpQx5uPjk5aW1tjYWFpaaumCjomJOXPmjOwyQUuuX7+enJzM//5MnTr18OHDDjlsR0dHTk7OqFGjGGNeXl6pqan19fUOObJbQYw6WF5e3qRJk/hf96SkpF9++aXHP7V0QSclJd28eVNSmaAZzc3NRqNx8ODBjLEhQ4YYjca2tjbHnuLx48dpaWmenp6MsREjRmRlZXV2djr2FPqGGHWYa9eurVy5kkdkRETEsWPH+vy2lpaWzMxM3gXt5+eXkZHR1NTk4lJBE7q7u3Nzc4ODgxljBoMhJSXl3r17zjvdAP8CQ2+IUQdQ8Mu8pqbG0gUdGhqam5vb3d3tmmpBE86fP79o0SIeagsWLDh79qxrzpuXlzd58mTLBVN1dbVrzqtpiFEhHR0dWVlZw4cPZ4x5e3vbe2upqKjI0gUdGxt77tw555UKWlFbW2v5FTtu3DjX/4rtfXP/2bNnrixAcxCjyplMJsuy6YoVK65evargIF1dXbwpmjHm4eGRkpJy//59h5cKmtDa2pqZmcnzy9fXNyMjg3ccS3H37t3U1FTeYRISEpKTk4MOk1dBjCpRWVmZlJTEA3TatGlff/214AGbmpqMRiPvgubLCL/++qtDSgWtyMvLmzhx4qsWJ2UpKSmJj4/nVc2fP7+wsFB2RWqEGLXP06dPMzIyeN4NHz48MzPTgXlXVVVlaWqZNm2ao5paQOUuXry4dOlS/t89Ojr69OnTfX5bTU2Niwvjuru7Dx8+HBYWxle6kpOTb9++LaUS1UKMDpTLrr6PHz+Opmg38ejRI8vi5MiRI1+zOGkymby9vdPS0hoaGlxcJNfS0mLpu/Lz83NG35V2IUYHpKCgYO7cuTzali1bdunSJaee7vnz52iK1je+OBkQEMAXJ/vNxz179vDblEFBQXx3EJeVauvOnTspKSn8B2H8+PG5ublSylAbxGg/JHYm8T4qLy8vSx/V8+fPXXNqcCqTyTRz5kx7FycHeO3vAqdOnbJ8qkhMTLx8+bKsSlQCMfpKva9iWltbXV9GeXn5qlWr+F/ZGTNmfPfdd66vARylsrJy7dq1/L9meHh4fn6+vUd4/WNyLtPZ2fnJJ5/wCyZPT8+NGzc+duMLJsRoH/g9df7UJr+nfuvWLbkloSla6548eZKRkeHj42NZnGxvb1d2KPX0RfEVVx8fn7H+/s+nTaPMTFL6L6VpiNGeLly4sHjxYh5Y8+bN++GHH2RXZMaboocNG4amaG3hi5NBQUGWxckHDx6IH9a2Sz8kJETig3DXrl27sHEjMUaMUUQEud9TpIhRq7q6Oku/8dixY9XZb6yJIsHi5MmTc+bMcd5tRFnPjPbBZKLISHOYrlhBip5G0SjEKJEGP+iVlJTYfmRGU7QKuWxR28UTTF6no4OysigggBgjb29KSyNJ7VkuhhjV6m3H3k3R0m/gAmc72s5lLZYumKc3UI8eUVoaeXoSYzRyJGVlkd7H7rl1jOpgERxN0aoi/YGfGzduWB6Ek9zXefEiLV1qvsaPjiZ57Vku4KYxqrOWTH79iLF7cvV4/PzHH3+UVcmJEycsN2SXL1/+008/yaqE8vJo0iRzmCYlkToGBTic28Wojh8Qsm2KXrZsGZqiXUaFw5B4e8Do0aMt7QEPHz6UU0prK2Vm0tChxBj5+lJGBskbW+Uk7hWjPR5Xv3LliuyKHMxJvTXwKn3uuyW7KCvbZtXAwECRZlVRtbWUkkIGAzFGISGUm0s6umBylxi1HZ7kwB3B1MmpY6jAoscDRapdnKyoqFizZg2vc/r06d988420Us6fp0WLzNf4CxaQxPYsh9J/jPIVTDcc5Wk7FDU8PFx8KCpYaHHbIpPJFBERYXmQ/+eff5ZTR3c35eZScDAxRgYDpaSQrPYsx9FzjPYebSetn04eh4zoBwtNb6Jp71gpJ2puJqORBg8mxmjIEDIaScsdJrqN0eLi4oULF/L4cPNtjnr/8Dx9+lR2Udpju6W7gn231GPgQ06d7vp1Sk42X+OHhZFmx+7pMEal7wimTrY/PJr7GCWd/j7Ul5aWJiQk8H+jmJiYM2fOSCvlxAmaM8ccpsuXk8T2LKV0FaPYAr5ftjMro6KiCgoKZFekdlVVVbb7bulscbLHBlA3b96UU8fz55STQ6NHE2Pk4UEpKSSrPUsR/cSoWv5CaIFKZlaqnJs0PNh++OBj96R9+HjyhDIyyMeHGKPAQA2N3dNDjKro8kQ7bGdWqrDhUS433PVaRbfCKipozRrzNf706SSxPWvAtB2j9fX1arlZrk137961nVmphsdvpCsoKIiKirI8DObsfbdURUULsyYTRURYx+7Jas8aGK3GKF995qPtJLduaF+PmZUSHwaXS+K+W+rBP4mraOzesGHqH7unyRhVSyOxjvDRROPHj2duuRc5H5Tl6+vLpO67pR4NDQ1bt27lT5EOGzbsk3/8Q9ptyvp69Y/d01iMquixNj1S0cxKV1Hhvlvqcf36df4I9enERJo6lSR2KZSWUkKC+Ro/JoZUtv6hmRhV0ZAFvXOfvchVu++Wqpj+97+u6GhzhP32tzJvU+bl0cSJ1rF7qunG0UCM8tF2qhj55U5sNxGSPLPSCbCllX14X+eoUcQYeXlRaqq0vs6WFsrMJH9/69g9FfSGqz1GVTSA1v30nlmpg7F7tvtu8cVJle+7pSKPH1NaGnl5EWM0YgRlZZGsYee2Y/fGjZM+dk+9MWq5L8MYCwsL0/Glpcrp6XZKj323bty4IbsiDSovp1WrzFfWM2bQt99Kq6S4mBYuJMYoKkruupMaY9QNFzrUT+uLez323fpW4g+/PuTl0eTJ1tuUsn4hdXXRZ5+R7Jva6opRFW0VC30xmUwzZ87UVquZzvbdUpH29p59ne56e0RFMdqjCfysXiZj64yKZlb2p/e+W1icdLy6OkpNJQ8PYozGjqWcHHK/xTpVxKjt87whISHu+fSItqhoZuUr6H7fLXW5cIEWLzZf48+bJ/0q28Ukx6jtgAw+XQYDMjREnUNh3GrfLRXp7qbDh2nCBPPuIMnJJPdBhmfPqKyMTp2iI0coP5+KipxXj8wYxbg2fVDPiEK33XdLRVpayGgkX19ijPz8yGgkFz9W++AB7dlDS5aYG7N6fI0bR3/8Izl67yw5MWo7PDg6Ovr06dNSygBH4VcVEmdW2o62w+KkfDU11r7O0FAX9XW2tNDOnebO/H6/li6l8+cddWZXx6j676mBYrJmVqpovBvYKiigqChzbMXFUVGRE89VU2PdicT2y9+fJk6kMWPMi2C2X97e9OmnDjm562JUQyu8IMKVoaaiYcPQp64uys2lMWOsu4M4YwB2QwOFh78UkfHxlJv70hOrnZ10+jSlp5u3I+VfBgP9+9/i53dRjOpvRzB4DRf0/2LfLS15+pQyMmjQIPPHQ6ORHHvP+ve/tybj4MH05Zevu4dQXU2xsdbv9/OjigrB8zs9RisrK9euXcsDNDw8PD8/39lnBJVw3oKPeha1wA5VVdbtlKdNc9jYvf/+15qJPj50/Hj/b3n2jOLirO+KixMswYkxyncE489i8x3BtPssNihmOxtBvP1InS1WYAeTiSIjrbuDlJWJHnDBAmsgGo0DfdfNmy8tRp08KVKCU2KUL5sGBQXpaTIQiDhx4sTs2bN5/L3xxhsKmuGx75Z+dHRYx+55e1NqKtXXKzxUUZE1CoOC7LtXsHOn9b2/+53CAojIGTF66tSpuXPn8h+YxMTEy5cvO/wUoEW2c2PtejQT+27pEx+7x3cH4WP3FPxe3LbNGoV//at9771zx3x2nuYC/a2OjFH3mZoOitkOCgkMDOx3UAj23dK5a9do5UpzlkVE2N0Yb9lZhDE6dcrus8+fb317YaHdb3/BMTHKdwTjo+34jmAYbQevUV5evnr16tePrdP6aD6wQ4+xe9XVA3pXR4f5cSneuqTgGuXPf7bG6L59dr/9BdEY5TuChYWFMbfcURJE5OXlTZkypfcQZT0NioaB4mP3hg41L7inpVG/4zXu3rWG4PjxSk76r39Zj7B5s5IjEJFgjJaUlMTHx/Mfg/nz57vt/uagWI/7nps2bcrOzsa+W+7r7l3r2L2QkH7G7l29ag3BOXOUnO4//7EeISVFcdXKY7S7uzsmJoaPtjt48CCeHgHF6urqNmzYwDeY4/+7YsWKMvFWGNCoc+esHfILF9LFi31/W2GhNQSXLFFyovx86xGSkhTX68WUMhgMWVlZx44d27FjB3+YBECZsWPHHjhw4N133/3iiy+Cg4Pnzp27bt062UWBPAsXsqIidvQo27qVFRWxhw/7/jYi62uDQcmJbN9lezQ7KY9RxlhCQoKlFxpAUGxsbGxsrOwqQB0MBpaczFavZkePspUr+/6e4cOtr5ualJylsdH6esQIJUdgjDHmofidAADO5e/P/vCHV/5T2xhtaFByfNt32R7NTohRANCm0aOZt7f5dU0Na221+wgVFdbXISGKC0GMAoA2DRrE5swxv+7qYlev2n2ES5esr+PiFBeCGAUAzXox2ZYxxo4ft++9DQ2spMT82tOTLViguArEKABo1qpV1tcHD9r33kOHWFub+XVCAhNoN0KMAoBmrVnDXmyLySor2eHDA31jWxv7+9+tf9y0SaQKxCgAaJaHB/vLX6x/3LLllU2mPfztb6y62vx6wgT29ttCVYi8GQBAsvR0FhVlfn3vHktMZHV1/bxl1y72z3+aXxsM7LPPmJdQBz1iFAC0zMeHffkl8/U1/7G8nM2axbKzWUtLH99cUsISE9nu3db/Z9Mm9pvfCJZgIIFHoAAAVOHMGfb22y+10w8ezBISWHg4CwpiTU3s/n1WWMhu3XrpXX/6E/v0U+bpKXhyxCgA6EJZGVu/nl25MqBv9vNjO3ey7dsdcmZc1AOALsyezS5dYgcOsBd7ufdt2DCWmsqqqhyVoQyfRgFAh+7cYSYTq6hgjx6x+nrm789Gj2bBwSwhgcXHCy4o9YYYBQAQgot6AAAhiFEAACGIUQAAIYhRAAAhiFEAACGIUQAAIYhRAAAhiFEAACGIUQAAIYhRAAAhiFEAACGIUQAAIYhRAAAhiFEAACGIUQAAIYhRAAAhiFEAACGIUQAAIYhRAAAhiFEAACGIUQAAIYhRAAAhiFEAACGIUQAAIYhRAAAhiFEAACGIUQAAIYhRAAAhiFEAACGIUQAAIYhRAAAhiFEAACGIUQAAIYhRAAAhiFEAACGIUQAAIYhRAAAh/wccsnMoXJ+eXQAAAIB6VFh0cmRraXRQS0wgcmRraXQgMjAyMS4wOS40AAB4nHu/b+09BiDgZUAAViBmAeIGRjaGBCDNyAyhmZgQdAaQZmZm5GDQADGYuBkYGRiZGJiYGZhZOJhEGMTdQPrgBj50Uzswa+bMfSDOQ7dl+9PSntnBJJHE7WHiQPUOMHExAAc+GhqXNNjFAAAAwnpUWHRNT0wgcmRraXQgMjAyMS4wOS40AAB4nI1QSQ6DMAy85xXzASKTpSVHIKiqKhKppf1D7/xfddSGwKEIO5a8jEeeCCS7+9t7xmLKCwHQznPO4aWJSIxICbrhcg3op7bLnT4+w/SAheEN9i2yneKYOzV6VEpa19D5hIpkIiZekfRLMlIlZC2Vc6Qbnhv7B6gZuHT3GA0DjxBaRBw6cQh+I+4rt4vBF7nJVdHEBXS5vOYw5T7FYdfsa65U50/nXHwARvhXrWchPzcAAABQelRYdFNNSUxFUyByZGtpdCAyMDIxLjA5LjQAAHicc3Z2drb1V6jR0DXSM7W0MLDQ0TXQM9ax1jXUM7K0NDDRMdAzMdWxNoAKo4qiaNGsAQBlEg+iGu70JAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x1d3ed133ac0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Chem.MolFromSmiles(\"CCCC=O\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Checking the output of ``set_attributes`` method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[ 1.        ,  0.        ,  0.        ,  3.        , -0.06564544],\n",
       "        [ 1.        ,  0.        ,  0.        ,  2.        , -0.05903836],\n",
       "        [ 1.        ,  0.        ,  0.        ,  3.        , -0.06564544]],\n",
       "       dtype=float32),\n",
       " array([[ 1.        ,  0.        ,  0.        ,  3.        , -0.06319688],\n",
       "        [ 1.        ,  0.        ,  0.        ,  2.        , -0.03273489],\n",
       "        [ 1.        ,  0.        ,  0.        ,  2.        ,  0.04278928],\n",
       "        [ 0.        ,  0.        ,  1.        ,  1.        , -0.39638734]],\n",
       "       dtype=float32),\n",
       " array([[ 1.        ,  0.        ,  0.        ,  3.        , -0.06417979],\n",
       "        [ 1.        ,  0.        ,  0.        ,  2.        , -0.04375392],\n",
       "        [ 1.        ,  0.        ,  0.        ,  2.        , -0.00799057],\n",
       "        [ 0.        ,  1.        ,  0.        ,  2.        , -0.3304839 ]],\n",
       "       dtype=float32),\n",
       " array([[ 1.0000000e+00,  0.0000000e+00,  0.0000000e+00,  3.0000000e+00,\n",
       "         -6.4970836e-02],\n",
       "        [ 1.0000000e+00,  0.0000000e+00,  0.0000000e+00,  2.0000000e+00,\n",
       "         -4.9520925e-02],\n",
       "        [ 1.0000000e+00,  0.0000000e+00,  0.0000000e+00,  2.0000000e+00,\n",
       "          2.7463208e-03],\n",
       "        [ 1.0000000e+00,  0.0000000e+00,  0.0000000e+00,  1.0000000e+00,\n",
       "          1.1947978e-01],\n",
       "        [ 0.0000000e+00,  0.0000000e+00,  1.0000000e+00,  0.0000000e+00,\n",
       "         -3.0340311e-01]], dtype=float32),\n",
       " array([[ 0.        ,  1.        ,  0.        ,  2.        , -0.21275702],\n",
       "        [ 0.        ,  0.        ,  1.        ,  0.        , -0.2708128 ],\n",
       "        [ 1.        ,  0.        ,  0.        ,  2.        ,  0.2071647 ],\n",
       "        [ 0.        ,  0.        ,  0.        ,  0.        , -0.21796767]],\n",
       "       dtype=float32)]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dts.get(\"node_attributes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[1., 0.],\n",
       "        [1., 0.],\n",
       "        [1., 0.],\n",
       "        [1., 0.]], dtype=float32),\n",
       " array([[1., 0.],\n",
       "        [1., 0.],\n",
       "        [1., 0.],\n",
       "        [1., 0.],\n",
       "        [1., 0.],\n",
       "        [1., 0.]], dtype=float32),\n",
       " array([[1., 0.],\n",
       "        [1., 0.],\n",
       "        [1., 0.],\n",
       "        [1., 0.],\n",
       "        [1., 0.],\n",
       "        [1., 0.]], dtype=float32),\n",
       " array([[1., 0.],\n",
       "        [1., 0.],\n",
       "        [1., 0.],\n",
       "        [1., 0.],\n",
       "        [1., 0.],\n",
       "        [1., 0.],\n",
       "        [2., 0.],\n",
       "        [2., 0.]], dtype=float32),\n",
       " array([[1., 0.],\n",
       "        [1., 0.],\n",
       "        [1., 0.],\n",
       "        [1., 0.],\n",
       "        [1., 0.],\n",
       "        [1., 0.]], dtype=float32)]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dts.get(\"edge_attributes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([44.0626,  3.    ], dtype=float32),\n",
       " array([60.057514,  4.      ], dtype=float32),\n",
       " array([59.073498,  4.      ], dtype=float32),\n",
       " array([72.05752,  5.     ], dtype=float32),\n",
       " array([65.027695,  4.      ], dtype=float32)]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dts.get(\"graph_attributes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([1, 0.1], dtype=object),\n",
       " array([2, 0.3], dtype=object),\n",
       " array([3, 0.2], dtype=object),\n",
       " array([4, 0.4], dtype=object),\n",
       " array([4, 1.4], dtype=object)]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dts.get(\"graph_labels\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:kgcnn.data.ExampleMol:Pickle dataset...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<MoleculeNetDataset [{'node_symbol': array(['C', 'C', 'C'], dtype='<U1'), 'node_number': array([6, 6, 6]), 'edge_indices': array([[0, 1],\n",
       "       [1, 0],\n",
       "       [1, 2],\n",
       "       [2, 1]], dtype=int64), 'edge_number': array([1, 1, 1, 1]), 'graph_size': array(3), 'node_coordinates': array([[ 1.0668, -0.074 , -0.0147],\n",
       "       [ 2.5858, -0.0537,  0.0053],\n",
       "       [ 3.1191,  0.9601,  1.0035]]), 'graph_labels': array([1, 0.1], dtype=object), 'node_attributes': array([[ 1.        ,  0.        ,  0.        ,  3.        , -0.06564544],\n",
       "       [ 1.        ,  0.        ,  0.        ,  2.        , -0.05903836],\n",
       "       [ 1.        ,  0.        ,  0.        ,  3.        , -0.06564544]],\n",
       "      dtype=float32), 'edge_attributes': array([[1., 0.],\n",
       "       [1., 0.],\n",
       "       [1., 0.],\n",
       "       [1., 0.]], dtype=float32), 'graph_attributes': array([44.0626,  3.    ], dtype=float32), 'size': array(3)} ...]>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dts.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:kgcnn.data.ExampleMol:Load pickled dataset...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<MoleculeNetDataset [{'node_symbol': array(['C', 'C', 'C'], dtype='<U1'), 'node_number': array([6, 6, 6]), 'edge_indices': array([[0, 1],\n",
       "       [1, 0],\n",
       "       [1, 2],\n",
       "       [2, 1]], dtype=int64), 'edge_number': array([1, 1, 1, 1]), 'graph_size': array(3), 'node_coordinates': array([[ 1.0668, -0.074 , -0.0147],\n",
       "       [ 2.5858, -0.0537,  0.0053],\n",
       "       [ 3.1191,  0.9601,  1.0035]]), 'graph_labels': array([1, 0.1], dtype=object), 'node_attributes': array([[ 1.        ,  0.        ,  0.        ,  3.        , -0.06564544],\n",
       "       [ 1.        ,  0.        ,  0.        ,  2.        , -0.05903836],\n",
       "       [ 1.        ,  0.        ,  0.        ,  3.        , -0.06564544]],\n",
       "      dtype=float32), 'edge_attributes': array([[1., 0.],\n",
       "       [1., 0.],\n",
       "       [1., 0.],\n",
       "       [1., 0.]], dtype=float32), 'graph_attributes': array([44.0626,  3.    ], dtype=float32), 'size': array(3)} ...]>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dts.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'node_symbol': array(['C', 'C', 'C'], dtype='<U1'),\n",
       " 'node_number': array([6, 6, 6]),\n",
       " 'edge_indices': array([[0, 1],\n",
       "        [1, 0],\n",
       "        [1, 2],\n",
       "        [2, 1]], dtype=int64),\n",
       " 'edge_number': array([1, 1, 1, 1]),\n",
       " 'graph_size': array(3),\n",
       " 'node_coordinates': array([[ 1.0668, -0.074 , -0.0147],\n",
       "        [ 2.5858, -0.0537,  0.0053],\n",
       "        [ 3.1191,  0.9601,  1.0035]]),\n",
       " 'graph_labels': array([1, 0.1], dtype=object),\n",
       " 'node_attributes': array([[ 1.        ,  0.        ,  0.        ,  3.        , -0.06564544],\n",
       "        [ 1.        ,  0.        ,  0.        ,  2.        , -0.05903836],\n",
       "        [ 1.        ,  0.        ,  0.        ,  3.        , -0.06564544]],\n",
       "       dtype=float32),\n",
       " 'edge_attributes': array([[1., 0.],\n",
       "        [1., 0.],\n",
       "        [1., 0.],\n",
       "        [1., 0.]], dtype=float32),\n",
       " 'graph_attributes': array([44.0626,  3.    ], dtype=float32),\n",
       " 'size': array(3)}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dts[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  }
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