0_prepare_features.ipynb

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      "name": "0_prepare_features.ipynb",
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      "name": "python3",
      "display_name": "Python 3"
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      "name": "python"
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    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-k976iTikGqm",
        "outputId": "ba4658ad-b0bd-4bdb-8aea-3cc02fd61e9c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'gpHSP'...\n",
            "remote: Enumerating objects: 84, done.\u001b[K\n",
            "remote: Counting objects: 100% (46/46), done.\u001b[K\n",
            "remote: Compressing objects: 100% (31/31), done.\u001b[K\n",
            "remote: Total 84 (delta 24), reused 37 (delta 15), pack-reused 38\u001b[K\n",
            "Unpacking objects: 100% (84/84), done.\n"
          ]
        }
      ],
      "source": [
        "! rm -rf gpHSP\n",
        "!git clone https://github.com/aspuru-guzik-group/gpHSP"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Assuming in a colab enviroment"
      ],
      "metadata": {
        "id": "p54WoW6RkOdK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import sys\n",
        "if 'google.colab' in sys.modules:\n",
        "    print('In colab!')\n",
        "    !pip install rdkit-pypi mordred ml_collections ngboost gpflow\n",
        "    sys.path.insert(0,'gpHSP')"
      ],
      "metadata": {
        "id": "qQyCA64AkN6w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import os\n",
        "import numpy as np\n",
        "import gphsp"
      ],
      "metadata": {
        "id": "9ebImi9uCOiK"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Utilities"
      ],
      "metadata": {
        "id": "HVDbXSZAliP3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "at_data_dir = lambda x: os.path.join('gpHSP/data', x)"
      ],
      "metadata": {
        "id": "wseewPpTCGHD"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load data and get all smiles"
      ],
      "metadata": {
        "id": "_wvvTIsMldgt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "files = [at_data_dir('Solvents_exp.csv'),\n",
        "         at_data_dir('Polymers_exp.csv'),\n",
        "         at_data_dir('sample_molecules.csv'),\n",
        "         at_data_dir('HSPiP.csv')\n",
        "         ]\n",
        "all_smiles = []\n",
        "for fname in files:\n",
        "    df = pd.read_csv(fname)\n",
        "    df['smiles'] = df['smiles'].apply(gphsp.get_isomeric_smiles)\n",
        "    gphsp.peek_df(df)\n",
        "    all_smiles.extend(df['smiles'].tolist())\n",
        "\n",
        "all_smiles = np.unique(all_smiles)\n",
        "len(all_smiles)"
      ],
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          "height": 961
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        "id": "tHhmQqxVZTbK",
        "outputId": "0f695bf8-f0cb-4e38-acbf-6a41edc7fb25"
      },
      "execution_count": 28,
      "outputs": [
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          "text": [
            "Index(['key', 'Type', 'δd', 'δp', 'δh', 'smiles', 'ID_type', 'ID', 'Ref',\n",
            "       'organic', 'n_electrons', 'n_atoms', 'charge', 'MolWt', 'label',\n",
            "       'finished', 'job_name', 'homo', 'lumo', 'gap', 'd-moments', 'dipole',\n",
            "       'polar', 'run_time', 'n_cores', 'compute_time', 'Area', 'Hba', 'Hbd',\n",
            "       'Volume', 'sigma_mom_0', 'sigma_mom_1', 'sigma_mom_2', 'sigma_mom_3',\n",
            "       'sigma_mom_4', 'sigma_mom_5', 'sigma_norm', 'sigma_profile', 'drug'],\n",
            "      dtype='object')\n",
            "(193, 39)\n"
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              "      <td>41.428082</td>\n",
              "      <td>[ 0.          0.          0.          0.      ...</td>\n",
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              "                     key  ...   drug\n",
              "0  1,1,1-Trichloroethane  ...  False\n",
              "\n",
              "[1 rows x 39 columns]"
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            "Index(['label', 'δd', 'δp', 'δh', 'smiles', 'test', 'poly_label', 'n_copies',\n",
            "       'organic', 'n_electrons', 'n_atoms', 'charge', 'MolWt', 'n_frags',\n",
            "       'largest', 'finished', 'job_name', 'poly_smiles', 'homo', 'lumo', 'gap',\n",
            "       'd-moments', 'dipole', 'polar', 'run_time', 'n_cores', 'compute_time',\n",
            "       'Area', 'Hba', 'Hbd', 'Volume', 'sigma_mom_0', 'sigma_mom_1',\n",
            "       'sigma_mom_2', 'sigma_mom_3', 'sigma_mom_4', 'sigma_mom_5',\n",
            "       'sigma_norm', 'sigma_profile'],\n",
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              "                  label  ...                                      sigma_profile\n",
              "0  Polyacrylonitrile-n5  ...  [ 0.          0.          0.          0.      ...\n",
              "\n",
              "[1 rows x 39 columns]"
            ]
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            "Index(['names', 'smiles'], dtype='object')\n",
            "(11, 2)\n"
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              "    names                                             smiles\n",
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            "Index(['Chemical', 'CAS', 'smiles', 'HSPiP_indx', 'good_smiles', 'single_frag',\n",
            "       'duplicated', 'sanity', 'label', 'organic', 'n_electrons', 'n_atoms',\n",
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            "       'polar', 'run_time', 'n_cores', 'compute_time', 'Area', 'Hba', 'Hbd',\n",
            "       'Volume', 'sigma_mom_0', 'sigma_mom_1', 'sigma_mom_2', 'sigma_mom_3',\n",
            "       'sigma_mom_4', 'sigma_mom_5', 'sigma_norm', 'sigma_profile'],\n",
            "      dtype='object')\n",
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            "text/plain": [
              "       Chemical  ...                                      sigma_profile\n",
              "0  Acetaldehyde  ...  [  0.00000000e+00   0.00000000e+00   0.0000000...\n",
              "\n",
              "[1 rows x 35 columns]"
            ]
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        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8833"
            ]
          },
          "metadata": {},
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    {
      "cell_type": "markdown",
      "source": [
        "Calculate a mordred values and a mask"
      ],
      "metadata": {
        "id": "w1s-j6DgtpgV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "all_smiles = np.unique([gphsp.get_isomeric_smiles(s) for s in all_smiles])\n",
        "all_values = gphsp.calculate_mordred(all_smiles)\n",
        "mask = gphsp.calculate_mask(all_values)\n",
        "np.savez_compressed(at_data_dir('mordred_features.npz'),\n",
        "                    smiles = all_smiles,\n",
        "                    values = all_values,\n",
        "                    mask = mask)\n",
        "features = gphsp.SmilesMap(at_data_dir('mordred_features.npz'))\n",
        "features(all_values)"
      ],
      "metadata": {
        "id": "jq0x70z9l-_G",
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          "base_uri": "https://localhost:8080/"
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      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "  2%|▏         | 194/8833 [00:38<26:53,  5.35it/s]  "
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/fromnumeric.py:87: RuntimeWarning: overflow encountered in reduce\n",
            "  return ufunc.reduce(obj, axis, dtype, out, **passkwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 11%|█         | 954/8833 [03:08<46:43,  2.81it/s]"
          ]
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/fromnumeric.py:87: RuntimeWarning: overflow encountered in reduce\n",
            "  return ufunc.reduce(obj, axis, dtype, out, **passkwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 8833/8833 [32:28<00:00,  4.53it/s]\n"
          ]
        }
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}