ml_models/classification_model.ipynb

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   "source": [
    "## Building a Simple Decision Tree\n",
    "\n",
    "In this tutorial we'll build a machine learning model that classifies molecules as soluble or insoluble.  After building the model, we'll visualize the results.  "
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "### Software Installation\n",
    "One of the Python libraries [dtreeviz](https://github.com/parrt/dtreeviz) that we are going to install requires the installation of a program called [graphviz](https://graphviz.org/).  We can use [apt-get](https://help.ubuntu.com/community/AptGet/Howto) to install graphviz. "
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     "text": [
      "Reading package lists... Done\n",
      "Building dependency tree       \n",
      "Reading state information... Done\n",
      "graphviz is already the newest version (2.40.1-2).\n",
      "The following packages were automatically installed and are no longer required:\n",
      "  cuda-command-line-tools-10-0 cuda-command-line-tools-10-1\n",
      "  cuda-command-line-tools-11-0 cuda-compiler-10-0 cuda-compiler-10-1\n",
      "  cuda-compiler-11-0 cuda-cuobjdump-10-0 cuda-cuobjdump-10-1\n",
      "  cuda-cuobjdump-11-0 cuda-cupti-10-0 cuda-cupti-10-1 cuda-cupti-11-0\n",
      "  cuda-cupti-dev-11-0 cuda-documentation-10-0 cuda-documentation-10-1\n",
      "  cuda-documentation-11-0 cuda-documentation-11-1 cuda-gdb-10-0 cuda-gdb-10-1\n",
      "  cuda-gdb-11-0 cuda-gpu-library-advisor-10-0 cuda-gpu-library-advisor-10-1\n",
      "  cuda-libraries-10-0 cuda-libraries-10-1 cuda-libraries-11-0\n",
      "  cuda-memcheck-10-0 cuda-memcheck-10-1 cuda-memcheck-11-0 cuda-nsight-10-0\n",
      "  cuda-nsight-10-1 cuda-nsight-11-0 cuda-nsight-11-1 cuda-nsight-compute-10-0\n",
      "  cuda-nsight-compute-10-1 cuda-nsight-compute-11-0 cuda-nsight-compute-11-1\n",
      "  cuda-nsight-systems-10-1 cuda-nsight-systems-11-0 cuda-nsight-systems-11-1\n",
      "  cuda-nvcc-10-0 cuda-nvcc-10-1 cuda-nvcc-11-0 cuda-nvdisasm-10-0\n",
      "  cuda-nvdisasm-10-1 cuda-nvdisasm-11-0 cuda-nvml-dev-10-0 cuda-nvml-dev-10-1\n",
      "  cuda-nvml-dev-11-0 cuda-nvprof-10-0 cuda-nvprof-10-1 cuda-nvprof-11-0\n",
      "  cuda-nvprune-10-0 cuda-nvprune-10-1 cuda-nvprune-11-0 cuda-nvtx-10-0\n",
      "  cuda-nvtx-10-1 cuda-nvtx-11-0 cuda-nvvp-10-0 cuda-nvvp-10-1 cuda-nvvp-11-0\n",
      "  cuda-nvvp-11-1 cuda-samples-10-0 cuda-samples-10-1 cuda-samples-11-0\n",
      "  cuda-samples-11-1 cuda-sanitizer-11-0 cuda-sanitizer-api-10-1\n",
      "  cuda-toolkit-10-0 cuda-toolkit-10-1 cuda-toolkit-11-0 cuda-toolkit-11-1\n",
      "  cuda-tools-10-0 cuda-tools-10-1 cuda-tools-11-0 cuda-tools-11-1\n",
      "  cuda-visual-tools-10-0 cuda-visual-tools-10-1 cuda-visual-tools-11-0\n",
      "  cuda-visual-tools-11-1 default-jre dkms freeglut3 freeglut3-dev\n",
      "  keyboard-configuration libargon2-0 libcap2 libcryptsetup12\n",
      "  libdevmapper1.02.1 libfontenc1 libidn11 libip4tc0 libjansson4\n",
      "  libnvidia-cfg1-510 libnvidia-common-460 libnvidia-common-510\n",
      "  libnvidia-extra-510 libnvidia-fbc1-510 libnvidia-gl-510 libpam-systemd\n",
      "  libpolkit-agent-1-0 libpolkit-backend-1-0 libpolkit-gobject-1-0 libxfont2\n",
      "  libxi-dev libxkbfile1 libxmu-dev libxmu-headers libxnvctrl0 libxtst6\n",
      "  nsight-compute-2020.2.1 nsight-compute-2022.1.0 nsight-systems-2020.3.2\n",
      "  nsight-systems-2020.3.4 nsight-systems-2021.5.2 nvidia-dkms-510\n",
      "  nvidia-kernel-common-510 nvidia-kernel-source-510 nvidia-modprobe\n",
      "  nvidia-settings openjdk-11-jre policykit-1 policykit-1-gnome python3-xkit\n",
      "  screen-resolution-extra systemd systemd-sysv udev x11-xkb-utils\n",
      "  xserver-common xserver-xorg-core-hwe-18.04 xserver-xorg-video-nvidia-510\n",
      "Use 'apt autoremove' to remove them.\n",
      "0 upgraded, 0 newly installed, 0 to remove and 39 not upgraded.\n"
     ]
    }
   ],
   "source": [
    "!apt-get install graphviz"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc5e22ed",
   "metadata": {},
   "source": [
    "Install the necessary Python libraries"
   ]
  },
  {
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   "execution_count": 6,
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     ]
    }
   ],
   "source": [
    "!pip install dtreeviz rdkit-pypi PyTDC"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0ae2059",
   "metadata": {},
   "source": [
    "### Setup\n",
    "Import the necessary Python libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "monthly-valuable",
   "metadata": {
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   "outputs": [],
   "source": [
    "import pandas as pd #data table manipulation\n",
    "from rdkit import Chem # basic cheminformatics\n",
    "from rdkit.Chem.Descriptors import MolWt, MolLogP, NumAromaticRings, NumHDonors, NumHAcceptors\n",
    "import math #needed for log10\n",
    "import seaborn as sns #plotting\n",
    "from sklearn.tree import DecisionTreeClassifier, plot_tree # decision trees\n",
    "from sklearn.model_selection import train_test_split # split a dataset\n",
    "from tqdm import tqdm # progress bar\n",
    "from matplotlib import pyplot as plt # plotting\n",
    "from dtreeviz.trees import * #plotting decision trees\n",
    "from sklearn.metrics import roc_auc_score, matthews_corrcoef, cohen_kappa_score, plot_roc_curve, plot_confusion_matrix # model stats\n",
    "from tdc.single_pred import ADME\n",
    "from rdkit.Chem.MolStandardize import rdMolStandardize\n",
    "from rdkit.rdBase import BlockLogs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "worth-bernard",
   "metadata": {
    "id": "worth-bernard"
   },
   "source": [
    "We want to have progress bars for operations we perfrom on the Pandas dataframe.  In order to do this we neeed to use the function [tqdm.pandas](https://datascientyst.com/progress-bars-pandas-python-tqdm/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "undefined-booking",
   "metadata": {
    "executionInfo": {
     "elapsed": 289,
     "status": "ok",
     "timestamp": 1644717896385,
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     "user_tz": 300
    },
    "id": "undefined-booking"
   },
   "outputs": [],
   "source": [
    "tqdm.pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "charitable-break",
   "metadata": {
    "id": "charitable-break"
   },
   "source": [
    "A simple function to calculate molecular weight, LogP, number of aromatic rings, number of hydrogen bond donors and acceptors from a SMILES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "spanish-decade",
   "metadata": {
    "executionInfo": {
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    "id": "spanish-decade"
   },
   "outputs": [],
   "source": [
    "def calc_descriptors(mol):\n",
    "    if mol:\n",
    "        Chem.DeleteSubstructs(mol, Chem.MolFromSmarts(\"[#1X0]\"))\n",
    "        mw, logp, num_arom_rings, hbd, hba = [x(mol) for x in [MolWt, MolLogP, NumAromaticRings, NumHDonors, NumHAcceptors]]\n",
    "        res = [mw, logp, num_arom_rings, hbd, hba]\n",
    "    else:\n",
    "        res = [None] * 5\n",
    "    return res"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b462fc8f",
   "metadata": {},
   "source": [
    "The molecules in this dataset have a number of salts and other functionality that can create issues with descriptor calcuations.  In order to avoid some of these problems we can use the function below to standardize the molecules. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e59d8c30",
   "metadata": {},
   "outputs": [],
   "source": [
    "def standardize(smiles):\n",
    "    # Code borrowed from https://bitsilla.com/blog/2021/06/standardizing-a-molecule-using-rdkit/\n",
    "    # follows the steps in\n",
    "    # https://github.com/greglandrum/RSC_OpenScience_Standardization_202104/blob/main/MolStandardize%20pieces.ipynb\n",
    "    # as described **excellently** (by Greg) in\n",
    "    # https://www.youtube.com/watch?v=eWTApNX8dJQ\n",
    "    mol = Chem.MolFromSmiles(smiles)\n",
    "     \n",
    "    # removeHs, disconnect metal atoms, normalize the molecule, reionize the molecule\n",
    "    clean_mol = rdMolStandardize.Cleanup(mol) \n",
    "     \n",
    "    # if many fragments, get the \"parent\" (the actual mol we are interested in) \n",
    "    parent_clean_mol = rdMolStandardize.FragmentParent(clean_mol)\n",
    "         \n",
    "    # try to neutralize molecule\n",
    "    uncharger = rdMolStandardize.Uncharger() # annoying, but necessary as no convenience method exists\n",
    "    uncharged_parent_clean_mol = uncharger.uncharge(parent_clean_mol)\n",
    "     \n",
    "    # note that no attempt is made at reionization at this step\n",
    "    # nor at ionization at some pH (rdkit has no pKa caculator)\n",
    "    # the main aim to to represent all molecules from different sources\n",
    "    # in a (single) standard way, for use in ML, catalogue, etc.\n",
    "     \n",
    "    te = rdMolStandardize.TautomerEnumerator() # idem\n",
    "    taut_uncharged_parent_clean_mol = te.Canonicalize(uncharged_parent_clean_mol)\n",
    "     \n",
    "    return taut_uncharged_parent_clean_mol"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "static-statistics",
   "metadata": {
    "id": "static-statistics"
   },
   "source": [
    "### Reading the Input Data\n",
    "Read data from [\"ESOL:  Estimating Aqueous Solubility Directly from Molecular Structure\"](https://pubs.acs.org/doi/10.1021/ci034243x) as supplied by [Therapeutic Data Commons](https://tdcommons.ai/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bf4f2170",
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    "colab": {
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    },
    "executionInfo": {
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found local copy...\n",
      "Loading...\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "data = ADME(name = 'Solubility_AqSolDB')\n",
    "sol_df = data.get_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa73c290",
   "metadata": {},
   "source": [
    "Let's take a look at the input data, which has three columns\n",
    "- Drug_ID - the molecule name\n",
    "- Drug - the SMILES\n",
    "- Y - the log of the aqueous solubility in uM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "drawn-pierre",
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    "executionInfo": {
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   "outputs": [
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       "      <th>Drug_ID</th>\n",
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       "      <th>9978</th>\n",
       "      <td>tetracycline</td>\n",
       "      <td>CN(C)[C@@H]1C(O)=C(C(N)=O)C(=O)[C@@]2(O)C(O)=C...</td>\n",
       "      <td>-2.930000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9979</th>\n",
       "      <td>thymol</td>\n",
       "      <td>Cc1ccc(C(C)C)c(O)c1</td>\n",
       "      <td>-2.190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9980</th>\n",
       "      <td>verapamil</td>\n",
       "      <td>COc1ccc(CCN(C)CCCC(C#N)(c2ccc(OC)c(OC)c2)C(C)C...</td>\n",
       "      <td>-3.980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9981</th>\n",
       "      <td>warfarin</td>\n",
       "      <td>CC(=O)CC(c1ccccc1)c1c(O)c2ccccc2oc1=O</td>\n",
       "      <td>-4.780000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9982 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Drug_ID  \\\n",
       "0            N,N,N-trimethyloctadecan-1-aminium bromide   \n",
       "1                              Benzo[cd]indol-2(1H)-one   \n",
       "2                                  4-chlorobenzaldehyde   \n",
       "3     zinc bis[2-hydroxy-3,5-bis(1-phenylethyl)benzo...   \n",
       "4     4-({4-[bis(oxiran-2-ylmethyl)amino]phenyl}meth...   \n",
       "...                                                 ...   \n",
       "9977                                         tetracaine   \n",
       "9978                                       tetracycline   \n",
       "9979                                             thymol   \n",
       "9980                                          verapamil   \n",
       "9981                                           warfarin   \n",
       "\n",
       "                                                   Drug         Y  \n",
       "0                   CCCCCCCCCCCCCCCCCC[N+](C)(C)C.[Br-] -3.616127  \n",
       "1                                  O=C1Nc2cccc3cccc1c23 -3.254767  \n",
       "2                                       O=Cc1ccc(Cl)cc1 -2.177078  \n",
       "3     CC(c1ccccc1)c1cc(C(=O)[O-])c(O)c(C(C)c2ccccc2)... -3.924409  \n",
       "4     c1cc(N(CC2CO2)CC2CO2)ccc1Cc1ccc(N(CC2CO2)CC2CO... -4.662065  \n",
       "...                                                 ...       ...  \n",
       "9977                       CCCCNc1ccc(C(=O)OCCN(C)C)cc1 -3.010000  \n",
       "9978  CN(C)[C@@H]1C(O)=C(C(N)=O)C(=O)[C@@]2(O)C(O)=C... -2.930000  \n",
       "9979                                Cc1ccc(C(C)C)c(O)c1 -2.190000  \n",
       "9980  COc1ccc(CCN(C)CCCC(C#N)(c2ccc(OC)c(OC)c2)C(C)C... -3.980000  \n",
       "9981              CC(=O)CC(c1ccccc1)c1c(O)c2ccccc2oc1=O -4.780000  \n",
       "\n",
       "[9982 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sol_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b90ba5ea",
   "metadata": {},
   "source": [
    "I don't like the names of the columns, let's rename them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b894f56a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "executionInfo": {
     "elapsed": 260,
     "status": "ok",
     "timestamp": 1644717969164,
     "user": {
      "displayName": "",
      "photoUrl": "",
      "userId": ""
     },
     "user_tz": 300
    },
    "id": "b894f56a",
    "outputId": "4252db1f-43b5-43c4-c439-0d0d7ebe89bb"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>SMILES</th>\n",
       "      <th>LogS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>N,N,N-trimethyloctadecan-1-aminium bromide</td>\n",
       "      <td>CCCCCCCCCCCCCCCCCC[N+](C)(C)C.[Br-]</td>\n",
       "      <td>-3.616127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Benzo[cd]indol-2(1H)-one</td>\n",
       "      <td>O=C1Nc2cccc3cccc1c23</td>\n",
       "      <td>-3.254767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4-chlorobenzaldehyde</td>\n",
       "      <td>O=Cc1ccc(Cl)cc1</td>\n",
       "      <td>-2.177078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>zinc bis[2-hydroxy-3,5-bis(1-phenylethyl)benzo...</td>\n",
       "      <td>CC(c1ccccc1)c1cc(C(=O)[O-])c(O)c(C(C)c2ccccc2)...</td>\n",
       "      <td>-3.924409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4-({4-[bis(oxiran-2-ylmethyl)amino]phenyl}meth...</td>\n",
       "      <td>c1cc(N(CC2CO2)CC2CO2)ccc1Cc1ccc(N(CC2CO2)CC2CO...</td>\n",
       "      <td>-4.662065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9977</th>\n",
       "      <td>tetracaine</td>\n",
       "      <td>CCCCNc1ccc(C(=O)OCCN(C)C)cc1</td>\n",
       "      <td>-3.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9978</th>\n",
       "      <td>tetracycline</td>\n",
       "      <td>CN(C)[C@@H]1C(O)=C(C(N)=O)C(=O)[C@@]2(O)C(O)=C...</td>\n",
       "      <td>-2.930000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9979</th>\n",
       "      <td>thymol</td>\n",
       "      <td>Cc1ccc(C(C)C)c(O)c1</td>\n",
       "      <td>-2.190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9980</th>\n",
       "      <td>verapamil</td>\n",
       "      <td>COc1ccc(CCN(C)CCCC(C#N)(c2ccc(OC)c(OC)c2)C(C)C...</td>\n",
       "      <td>-3.980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9981</th>\n",
       "      <td>warfarin</td>\n",
       "      <td>CC(=O)CC(c1ccccc1)c1c(O)c2ccccc2oc1=O</td>\n",
       "      <td>-4.780000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9982 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   Name  \\\n",
       "0            N,N,N-trimethyloctadecan-1-aminium bromide   \n",
       "1                              Benzo[cd]indol-2(1H)-one   \n",
       "2                                  4-chlorobenzaldehyde   \n",
       "3     zinc bis[2-hydroxy-3,5-bis(1-phenylethyl)benzo...   \n",
       "4     4-({4-[bis(oxiran-2-ylmethyl)amino]phenyl}meth...   \n",
       "...                                                 ...   \n",
       "9977                                         tetracaine   \n",
       "9978                                       tetracycline   \n",
       "9979                                             thymol   \n",
       "9980                                          verapamil   \n",
       "9981                                           warfarin   \n",
       "\n",
       "                                                 SMILES      LogS  \n",
       "0                   CCCCCCCCCCCCCCCCCC[N+](C)(C)C.[Br-] -3.616127  \n",
       "1                                  O=C1Nc2cccc3cccc1c23 -3.254767  \n",
       "2                                       O=Cc1ccc(Cl)cc1 -2.177078  \n",
       "3     CC(c1ccccc1)c1cc(C(=O)[O-])c(O)c(C(C)c2ccccc2)... -3.924409  \n",
       "4     c1cc(N(CC2CO2)CC2CO2)ccc1Cc1ccc(N(CC2CO2)CC2CO... -4.662065  \n",
       "...                                                 ...       ...  \n",
       "9977                       CCCCNc1ccc(C(=O)OCCN(C)C)cc1 -3.010000  \n",
       "9978  CN(C)[C@@H]1C(O)=C(C(N)=O)C(=O)[C@@]2(O)C(O)=C... -2.930000  \n",
       "9979                                Cc1ccc(C(C)C)c(O)c1 -2.190000  \n",
       "9980  COc1ccc(CCN(C)CCCC(C#N)(c2ccc(OC)c(OC)c2)C(C)C... -3.980000  \n",
       "9981              CC(=O)CC(c1ccccc1)c1c(O)c2ccccc2oc1=O -4.780000  \n",
       "\n",
       "[9982 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sol_df.columns = [\"Name\",\"SMILES\",\"LogS\"]\n",
    "sol_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb19fdaa",
   "metadata": {},
   "source": [
    "Now let's reorder the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "significant-italy",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "executionInfo": {
     "elapsed": 280,
     "status": "ok",
     "timestamp": 1644717971042,
     "user": {
      "displayName": "",
      "photoUrl": "",
      "userId": ""
     },
     "user_tz": 300
    },
    "id": "significant-italy",
    "outputId": "4f7bf080-387a-453c-cce4-d7c1ce756883"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>SMILES</th>\n",
       "      <th>Name</th>\n",
       "      <th>LogS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CCCCCCCCCCCCCCCCCC[N+](C)(C)C.[Br-]</td>\n",
       "      <td>N,N,N-trimethyloctadecan-1-aminium bromide</td>\n",
       "      <td>-3.616127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>O=C1Nc2cccc3cccc1c23</td>\n",
       "      <td>Benzo[cd]indol-2(1H)-one</td>\n",
       "      <td>-3.254767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>O=Cc1ccc(Cl)cc1</td>\n",
       "      <td>4-chlorobenzaldehyde</td>\n",
       "      <td>-2.177078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CC(c1ccccc1)c1cc(C(=O)[O-])c(O)c(C(C)c2ccccc2)...</td>\n",
       "      <td>zinc bis[2-hydroxy-3,5-bis(1-phenylethyl)benzo...</td>\n",
       "      <td>-3.924409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>c1cc(N(CC2CO2)CC2CO2)ccc1Cc1ccc(N(CC2CO2)CC2CO...</td>\n",
       "      <td>4-({4-[bis(oxiran-2-ylmethyl)amino]phenyl}meth...</td>\n",
       "      <td>-4.662065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9977</th>\n",
       "      <td>CCCCNc1ccc(C(=O)OCCN(C)C)cc1</td>\n",
       "      <td>tetracaine</td>\n",
       "      <td>-3.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9978</th>\n",
       "      <td>CN(C)[C@@H]1C(O)=C(C(N)=O)C(=O)[C@@]2(O)C(O)=C...</td>\n",
       "      <td>tetracycline</td>\n",
       "      <td>-2.930000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9979</th>\n",
       "      <td>Cc1ccc(C(C)C)c(O)c1</td>\n",
       "      <td>thymol</td>\n",
       "      <td>-2.190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9980</th>\n",
       "      <td>COc1ccc(CCN(C)CCCC(C#N)(c2ccc(OC)c(OC)c2)C(C)C...</td>\n",
       "      <td>verapamil</td>\n",
       "      <td>-3.980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9981</th>\n",
       "      <td>CC(=O)CC(c1ccccc1)c1c(O)c2ccccc2oc1=O</td>\n",
       "      <td>warfarin</td>\n",
       "      <td>-4.780000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9982 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 SMILES  \\\n",
       "0                   CCCCCCCCCCCCCCCCCC[N+](C)(C)C.[Br-]   \n",
       "1                                  O=C1Nc2cccc3cccc1c23   \n",
       "2                                       O=Cc1ccc(Cl)cc1   \n",
       "3     CC(c1ccccc1)c1cc(C(=O)[O-])c(O)c(C(C)c2ccccc2)...   \n",
       "4     c1cc(N(CC2CO2)CC2CO2)ccc1Cc1ccc(N(CC2CO2)CC2CO...   \n",
       "...                                                 ...   \n",
       "9977                       CCCCNc1ccc(C(=O)OCCN(C)C)cc1   \n",
       "9978  CN(C)[C@@H]1C(O)=C(C(N)=O)C(=O)[C@@]2(O)C(O)=C...   \n",
       "9979                                Cc1ccc(C(C)C)c(O)c1   \n",
       "9980  COc1ccc(CCN(C)CCCC(C#N)(c2ccc(OC)c(OC)c2)C(C)C...   \n",
       "9981              CC(=O)CC(c1ccccc1)c1c(O)c2ccccc2oc1=O   \n",
       "\n",
       "                                                   Name      LogS  \n",
       "0            N,N,N-trimethyloctadecan-1-aminium bromide -3.616127  \n",
       "1                              Benzo[cd]indol-2(1H)-one -3.254767  \n",
       "2                                  4-chlorobenzaldehyde -2.177078  \n",
       "3     zinc bis[2-hydroxy-3,5-bis(1-phenylethyl)benzo... -3.924409  \n",
       "4     4-({4-[bis(oxiran-2-ylmethyl)amino]phenyl}meth... -4.662065  \n",
       "...                                                 ...       ...  \n",
       "9977                                         tetracaine -3.010000  \n",
       "9978                                       tetracycline -2.930000  \n",
       "9979                                             thymol -2.190000  \n",
       "9980                                          verapamil -3.980000  \n",
       "9981                                           warfarin -4.780000  \n",
       "\n",
       "[9982 rows x 3 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sol_df = sol_df[['SMILES','Name','LogS']]\n",
    "sol_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "506a165f",
   "metadata": {},
   "source": [
    "Before we calculate descriptors, we're going to standardize the molecules. This process removes counterions, neutralizes molecules, and generates a standard tautomer.  This process also generates **a lot** of logging output.  We don't want to look at that output, so we're going to use the function [BlockLogs](https://www.rdkit.org/docs/source/rdkit.rdBase.html) to temporarily turn off logging.  Once we're finished we delete the variable **block** and logging is back on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "21505088",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 9982/9982 [04:44<00:00, 35.12it/s]\n"
     ]
    }
   ],
   "source": [
    "block = BlockLogs()\n",
    "sol_df['mol'] = sol_df.SMILES.progress_apply(standardize)\n",
    "del block"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "motivated-postcard",
   "metadata": {
    "id": "motivated-postcard"
   },
   "source": [
    "Add a new column \"IsSol\" to indicate whether a molecule's solubility is greater than 200uM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "julian-representative",
   "metadata": {
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1644717974138,
     "user": {
      "displayName": "",
      "photoUrl": "",
      "userId": ""
     },
     "user_tz": 300
    },
    "id": "julian-representative"
   },
   "outputs": [],
   "source": [
    "sol_df['IsSol'] = sol_df.LogS > math.log10(200 * 1e-6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "certified-passage",
   "metadata": {
    "id": "certified-passage"
   },
   "source": [
    "Plot solubility as a sanity check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cloudy-provider",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 387
    },
    "executionInfo": {
     "elapsed": 1141,
     "status": "ok",
     "timestamp": 1644717983139,
     "user": {
      "displayName": "",
      "photoUrl": "",
      "userId": ""
     },
     "user_tz": 300
    },
    "id": "cloudy-provider",
    "outputId": "3e7fdcef-e0ac-4f9d-9e24-97c8a37f3d50"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x109785070>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 460.65x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context('talk') # Set the fonts the way I like them\n",
    "sns.displot(x='LogS',hue=\"IsSol\",data=sol_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ruled-church",
   "metadata": {
    "id": "ruled-church"
   },
   "source": [
    "Add the descriptors to the dataframe.  Note that all the descriptors are going into one column called \"desc\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "affecting-solomon",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4984,
     "status": "ok",
     "timestamp": 1644717991328,
     "user": {
      "displayName": "",
      "photoUrl": "",
      "userId": ""
     },
     "user_tz": 300
    },
    "id": "affecting-solomon",
    "outputId": "e3e92b5e-d608-484b-9273-23958db1cba6"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 9982/9982 [00:05<00:00, 1926.10it/s]\n"
     ]
    }
   ],
   "source": [
    "sol_df['desc'] = sol_df.mol.progress_apply(calc_descriptors)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ordinary-romania",
   "metadata": {
    "id": "ordinary-romania"
   },
   "source": [
    "The function call in the cell above put all the descriptors into a single column called **desc**.  We would like split the descriptors so that each is in its own column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "racial-spell",
   "metadata": {
    "executionInfo": {
     "elapsed": 264,
     "status": "ok",
     "timestamp": 1644718012997,
     "user": {
      "displayName": "",
      "photoUrl": "",
      "userId": ""
     },
     "user_tz": 300
    },
    "id": "racial-spell"
   },
   "outputs": [],
   "source": [
    "desc_cols = ['MW','LogP','NumAromatic','HBD','HBA']\n",
    "sol_df[desc_cols] = sol_df.desc.to_list()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hydraulic-chapter",
   "metadata": {
    "id": "hydraulic-chapter"
   },
   "source": [
    "We no longer need the \"desc\" column, let's get rid of it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "assured-interaction",
   "metadata": {
    "executionInfo": {
     "elapsed": 268,
     "status": "ok",
     "timestamp": 1644718016693,
     "user": {
      "displayName": "",
      "photoUrl": "",
      "userId": ""
     },
     "user_tz": 300
    },
    "id": "assured-interaction"
   },
   "outputs": [],
   "source": [
    "sol_df.drop(\"desc\",axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "green-deviation",
   "metadata": {
    "id": "green-deviation"
   },
   "source": [
    "Let's check the dataframe to make sure it's ok. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "dried-guitar",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "executionInfo": {
     "elapsed": 632,
     "status": "ok",
     "timestamp": 1644718019994,
     "user": {
      "displayName": "",
      "photoUrl": "",
      "userId": ""
     },
     "user_tz": 300
    },
    "id": "dried-guitar",
    "outputId": "922b62c0-357b-4cf8-aede-a399f97be640"
   },
   "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>SMILES</th>\n",
       "      <th>Name</th>\n",
       "      <th>LogS</th>\n",
       "      <th>mol</th>\n",
       "      <th>IsSol</th>\n",
       "      <th>MW</th>\n",
       "      <th>LogP</th>\n",
       "      <th>NumAromatic</th>\n",
       "      <th>HBD</th>\n",
       "      <th>HBA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CCCCCCCCCCCCCCCCCC[N+](C)(C)C.[Br-]</td>\n",
       "      <td>N,N,N-trimethyloctadecan-1-aminium bromide</td>\n",
       "      <td>-3.616127</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x1702c2b80&gt;</td>\n",
       "      <td>True</td>\n",
       "      <td>312.606</td>\n",
       "      <td>6.95410</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>O=C1Nc2cccc3cccc1c23</td>\n",
       "      <td>Benzo[cd]indol-2(1H)-one</td>\n",
       "      <td>-3.254767</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x1702c2b20&gt;</td>\n",
       "      <td>True</td>\n",
       "      <td>169.183</td>\n",
       "      <td>2.40550</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>O=Cc1ccc(Cl)cc1</td>\n",
       "      <td>4-chlorobenzaldehyde</td>\n",
       "      <td>-2.177078</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x1702c2ac0&gt;</td>\n",
       "      <td>True</td>\n",
       "      <td>140.569</td>\n",
       "      <td>2.15250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CC(c1ccccc1)c1cc(C(=O)[O-])c(O)c(C(C)c2ccccc2)...</td>\n",
       "      <td>zinc bis[2-hydroxy-3,5-bis(1-phenylethyl)benzo...</td>\n",
       "      <td>-3.924409</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x1702c29a0&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>346.426</td>\n",
       "      <td>5.39400</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>c1cc(N(CC2CO2)CC2CO2)ccc1Cc1ccc(N(CC2CO2)CC2CO...</td>\n",
       "      <td>4-({4-[bis(oxiran-2-ylmethyl)amino]phenyl}meth...</td>\n",
       "      <td>-4.662065</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x1702c2a60&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>422.525</td>\n",
       "      <td>2.48540</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9977</th>\n",
       "      <td>CCCCNc1ccc(C(=O)OCCN(C)C)cc1</td>\n",
       "      <td>tetracaine</td>\n",
       "      <td>-3.010000</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x16f7453a0&gt;</td>\n",
       "      <td>True</td>\n",
       "      <td>264.369</td>\n",
       "      <td>2.61700</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9978</th>\n",
       "      <td>CN(C)[C@@H]1C(O)=C(C(N)=O)C(=O)[C@@]2(O)C(O)=C...</td>\n",
       "      <td>tetracycline</td>\n",
       "      <td>-2.930000</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x16f745400&gt;</td>\n",
       "      <td>True</td>\n",
       "      <td>444.440</td>\n",
       "      <td>-1.46800</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9979</th>\n",
       "      <td>Cc1ccc(C(C)C)c(O)c1</td>\n",
       "      <td>thymol</td>\n",
       "      <td>-2.190000</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x16f745460&gt;</td>\n",
       "      <td>True</td>\n",
       "      <td>150.221</td>\n",
       "      <td>2.82402</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9980</th>\n",
       "      <td>COc1ccc(CCN(C)CCCC(C#N)(c2ccc(OC)c(OC)c2)C(C)C...</td>\n",
       "      <td>verapamil</td>\n",
       "      <td>-3.980000</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x16f7454c0&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>454.611</td>\n",
       "      <td>5.09308</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9981</th>\n",
       "      <td>CC(=O)CC(c1ccccc1)c1c(O)c2ccccc2oc1=O</td>\n",
       "      <td>warfarin</td>\n",
       "      <td>-4.780000</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x16f745520&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>308.333</td>\n",
       "      <td>3.60960</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9982 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 SMILES  \\\n",
       "0                   CCCCCCCCCCCCCCCCCC[N+](C)(C)C.[Br-]   \n",
       "1                                  O=C1Nc2cccc3cccc1c23   \n",
       "2                                       O=Cc1ccc(Cl)cc1   \n",
       "3     CC(c1ccccc1)c1cc(C(=O)[O-])c(O)c(C(C)c2ccccc2)...   \n",
       "4     c1cc(N(CC2CO2)CC2CO2)ccc1Cc1ccc(N(CC2CO2)CC2CO...   \n",
       "...                                                 ...   \n",
       "9977                       CCCCNc1ccc(C(=O)OCCN(C)C)cc1   \n",
       "9978  CN(C)[C@@H]1C(O)=C(C(N)=O)C(=O)[C@@]2(O)C(O)=C...   \n",
       "9979                                Cc1ccc(C(C)C)c(O)c1   \n",
       "9980  COc1ccc(CCN(C)CCCC(C#N)(c2ccc(OC)c(OC)c2)C(C)C...   \n",
       "9981              CC(=O)CC(c1ccccc1)c1c(O)c2ccccc2oc1=O   \n",
       "\n",
       "                                                   Name      LogS  \\\n",
       "0            N,N,N-trimethyloctadecan-1-aminium bromide -3.616127   \n",
       "1                              Benzo[cd]indol-2(1H)-one -3.254767   \n",
       "2                                  4-chlorobenzaldehyde -2.177078   \n",
       "3     zinc bis[2-hydroxy-3,5-bis(1-phenylethyl)benzo... -3.924409   \n",
       "4     4-({4-[bis(oxiran-2-ylmethyl)amino]phenyl}meth... -4.662065   \n",
       "...                                                 ...       ...   \n",
       "9977                                         tetracaine -3.010000   \n",
       "9978                                       tetracycline -2.930000   \n",
       "9979                                             thymol -2.190000   \n",
       "9980                                          verapamil -3.980000   \n",
       "9981                                           warfarin -4.780000   \n",
       "\n",
       "                                                mol  IsSol       MW     LogP  \\\n",
       "0     <rdkit.Chem.rdchem.Mol object at 0x1702c2b80>   True  312.606  6.95410   \n",
       "1     <rdkit.Chem.rdchem.Mol object at 0x1702c2b20>   True  169.183  2.40550   \n",
       "2     <rdkit.Chem.rdchem.Mol object at 0x1702c2ac0>   True  140.569  2.15250   \n",
       "3     <rdkit.Chem.rdchem.Mol object at 0x1702c29a0>  False  346.426  5.39400   \n",
       "4     <rdkit.Chem.rdchem.Mol object at 0x1702c2a60>  False  422.525  2.48540   \n",
       "...                                             ...    ...      ...      ...   \n",
       "9977  <rdkit.Chem.rdchem.Mol object at 0x16f7453a0>   True  264.369  2.61700   \n",
       "9978  <rdkit.Chem.rdchem.Mol object at 0x16f745400>   True  444.440 -1.46800   \n",
       "9979  <rdkit.Chem.rdchem.Mol object at 0x16f745460>   True  150.221  2.82402   \n",
       "9980  <rdkit.Chem.rdchem.Mol object at 0x16f7454c0>  False  454.611  5.09308   \n",
       "9981  <rdkit.Chem.rdchem.Mol object at 0x16f745520>  False  308.333  3.60960   \n",
       "\n",
       "      NumAromatic  HBD  HBA  \n",
       "0             0.0  0.0  0.0  \n",
       "1             2.0  1.0  1.0  \n",
       "2             1.0  0.0  1.0  \n",
       "3             3.0  2.0  2.0  \n",
       "4             2.0  0.0  6.0  \n",
       "...           ...  ...  ...  \n",
       "9977          1.0  1.0  4.0  \n",
       "9978          1.0  4.0  9.0  \n",
       "9979          1.0  1.0  1.0  \n",
       "9980          2.0  0.0  6.0  \n",
       "9981          3.0  1.0  4.0  \n",
       "\n",
       "[9982 rows x 10 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sol_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "choice-scroll",
   "metadata": {
    "id": "choice-scroll"
   },
   "source": [
    "In order to build a machine learning model, we will split the data into training and test sets. By default, the function train_test_split from scikit_learn puts 80% of the data into the training set and 20% into the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "indian-payroll",
   "metadata": {
    "executionInfo": {
     "elapsed": 270,
     "status": "ok",
     "timestamp": 1644718023249,
     "user": {
      "displayName": "",
      "photoUrl": "",
      "userId": ""
     },
     "user_tz": 300
    },
    "id": "indian-payroll"
   },
   "outputs": [],
   "source": [
    "train, test = train_test_split(sol_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "strong-hearing",
   "metadata": {
    "id": "strong-hearing"
   },
   "source": [
    "Our data is currently in a Pandas dataframe.  I find it easier to build models with X and y variables. We'll split the training and test sets into X and y variables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "simplified-legislation",
   "metadata": {
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1644718026121,
     "user": {
      "displayName": "",
      "photoUrl": "",
      "userId": ""
     },
     "user_tz": 300
    },
    "id": "simplified-legislation"
   },
   "outputs": [],
   "source": [
    "train_X = train[desc_cols]\n",
    "train_y = train.IsSol\n",
    "test_X = test[desc_cols]\n",
    "test_y = test.IsSol"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "temporal-mitchell",
   "metadata": {
    "id": "temporal-mitchell"
   },
   "source": [
    "Create and train a classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "continuing-reasoning",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 292,
     "status": "ok",
     "timestamp": 1644718036255,
     "user": {
      "displayName": "",
      "photoUrl": "",
      "userId": ""
     },
     "user_tz": 300
    },
    "id": "continuing-reasoning",
    "outputId": "f63e94ca-96e7-481e-9e36-ce81f65c6fae"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=2)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cls = DecisionTreeClassifier(max_depth=2)\n",
    "cls.fit(train_X,train_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "concrete-heath",
   "metadata": {
    "id": "concrete-heath"
   },
   "source": [
    "Predict on the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "measured-tribe",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 264,
     "status": "ok",
     "timestamp": 1644718040012,
     "user": {
      "displayName": "",
      "photoUrl": "",
      "userId": ""
     },
     "user_tz": 300
    },
    "id": "measured-tribe",
    "outputId": "eb117b26-ef69-4d46-8843-c90d340dd6d1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC = 0.82\n",
      "Matthews Correlation Coefficient = 0.65\n",
      "Cohen's Kappa = 0.65\n"
     ]
    }
   ],
   "source": [
    "pred = cls.predict(test_X)\n",
    "auc, mcc, kappa = roc_auc_score(test_y, pred),matthews_corrcoef(test_y,pred),cohen_kappa_score(test_y,pred)\n",
    "print(f\"ROC AUC = {auc:.2f}\")\n",
    "print(f\"Matthews Correlation Coefficient = {mcc:.2f}\")\n",
    "print(f\"Cohen\\'s Kappa = {kappa:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "accompanied-playing",
   "metadata": {
    "id": "accompanied-playing"
   },
   "source": [
    "Plot a confusion matrix to show the classifier performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "danish-implementation",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "executionInfo": {
     "elapsed": 600,
     "status": "ok",
     "timestamp": 1644718044618,
     "user": {
      "displayName": "",
      "photoUrl": "",
      "userId": ""
     },
     "user_tz": 300
    },
    "id": "danish-implementation",
    "outputId": "b92a390d-ee0d-40d0-dde0-ee48d8be7375"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x16e655fa0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ddqri3kkZV9S7epz+s4WcefEiXnmiD7+7Yiipuqg3bmjP2xO34ltHrmbI8PXM/SxMS+3ctZIjT13Owrmdmflht5zLbev8ITvptcqgAFQRvrokt4nPy6UQMysDJkuqIozU7wTMBJ4nLAuflc3KwS3duYe8y8htl/PuFwNZWtydrp3K+dawBey5w2KmL+jLo2+MqnPM8XuFAeanktYmJOrSsZx7LniCKZ8MZvHqnnTvXMYRu37OiEEruPOFvXl3Tnz6qPPt2DOXcuYli1i2qCMfvNGTg45fVWt/8aoOvP966K2855avseu3Srjh/s8Y/48BbFjXnm+fupw+/cq47n+HkdgKyKXctqzCg0JarTIomNlaSa8TBn1XAAsI4wDHZDpW0jjCIo2JhFvFFhHGJoqBN6NsY4AjCDOZ7gBmA90JD8Y4Djgim6lbW4r35gxkSL81HL3HTAq7bqLKxJcrC7nzhb156LVdKKuo/WfQqaCCI3b9nGXF3Xhr1tfSllte2Y7Pl/bmyF0/p6jHBjaWF/Dpwq258O5jeLue41xmO44KC876bVPGZbfOqbP/o7d61Fy8l8zvzKWn7MwPrljAd/9vMQUdjC8+6cq1Pxhe6xYXuZbbVhnZdR+13E1SW5bMWu7UE25zsVuKMYWvAXcAB0dJEwlTUucB15vZ2CjfWBJucyHpGOACYBfC2MAawjjE9dFNparL70/oVjoGGEC4C+FsQiviV5bjL0ZScfsOnQt3O+2GXA5zLaz3o+9nzuRahUml4dkw5ZZi/m2WJBUXdO9YePTzmTsdJnz7b1SsK2uWex+1Ji3aUjCze9m8OCN535fASSl2KSnfWDYvCsHMniMs2Mj03kuB87Otq3Ou7fCB5vRaZfeRc841JQ8K6XlQcM7Fjq9TSM+DgnMuXgwqsnjITlxHmj0oOOdiJttbY8ezNeFBwTkXKz4ltX5pg4Kk6xpQnpnZLxtRH+eca3JxXMWdrfpaCmMbUJ4BHhScc62aDzSnV19QGNJstXDOuWbkU1LTSxsUzGx+c1bEOeeaS2U2s49iqkEDzZI6EW4hsSK66Zxzzm0xfEwhvZzCpaTdJU0iPNtgAbB/lN5X0kRJhzVBHZ1zLq+qTBm3uMo6KEjaFXgN2B64P3GfmS0nPPjm7HxWzjnnmoJZ5i2ucuk+GgcsBnYjPBrz3KT9E4Hv5qlezjnXZHz2UXq5BIUDgF+b2bpoTCHZAmBgfqrlnHNNw8huoDmujYVcgkJnwjMH0vEntDvntghx7h7KJJeg8AWwRz37DwFmNK46zjnX9Hz2UXq5zD56EDgzaYaRAUi6FDgKeCCPdXPOuSZhpoxbXOXSUvgtcDjwIvAZISDcJmlroD/wMnBn3mvonHP5ZFmuaI5pF1PWLYVokdrhwGVAKbAR2BFYCVwBHGtmVU1RSeecyyefkppeTovXzKzCzG4zsz3NrJuZdTWzb5jZrWZW0VSVdM65/BFVVe0ybo15noKkQZJ+L+l1SeskmaSD0uQ9XNJbkkolLZf0V0m9UuTrLukOSUuivNMkHd+YMlPxG4A452LHstgaaQfgNGAdYQ1XSlGgmAB8CRxH6Ik5HnhOUvL1eTxwBnAtcAxhYs94SUc3osw6crr3kaTOwIXAScDQKHlOVNk/mFlpLuU551xLaIaB5FfNrC+ApBMJF+VUbgamA6dWd79LWgK8BJwCPBKlHQ0cBow2s/FR2mTCdfhWQhDIqcx0crnNxdbAO8BvgBHAIsIK5xFR2jtRHueca92auKmQzfiqpG2AvYAHEvOb2cuE6+vJCdlPIqwTeyohnwH3AcMl7dSAMlPKpfvoFmAn4OdAXzPb3cx2A/oClxKCwy05lOeccy0ihymphZKK69saUY2R0ev0FPs+TthfnXdGimDzUVJZuZSZUi7dR8cBd5vZ7YmJ0ayk2yTtTIhmzjnXqlVVtYp1CEXR6+oU+1YDuyflnZUmX2JZuZSZUi5BoSPwXj37pwGn5lCec861jOzHFErMrFcT1gTSd1Ylp9fXqZVt3owdY7kEhXeoP8rsAUzNoTznnGsRrWQdwqrotSjFvt7U/ra/qp58JOTNpcyUchlTuBT4jqSfSepQnSipQNJFwOgoj3POtW7NMCc1C59Er6n6+UdRe1zgE2BEiimlo6LX6Qn5si0zpbRBQdKkxI0w7WkVcDuwXNK7kqYBK4DfRftuzfSGzjnXkozsBpqbOi6Y2UJCt/sZiRd7SYcC2wBPJGQfD/QijO0mOguYaWYzGlBmSvV1Hw0ldbxcEL1WN1uKo60Dm9cuOOdc69UMLQFJ34n+uVf0eqCkPsB6M3s+SruSsH7gIUl3EZ5JcxPwNvBoQnETgMnA3ZKKgLmEJ13uD5yQ9NbZlplS2qBgZoMzHeycc1sia57ZR8kX4LHR63xgMICZTZJ0LHA98BzwFfAkcIWZVVYfaGYWLYK7Mdp6EVY0jzazZxLfJNsy08lpRbNzzrUNTR8ULMtl02b2AvBCFvnWAhdEW17KTMWDgnMuXrIdSG4dM5SaXa73PtoeuAT4JrAVdQeqzcy2z1PdnHOuacT0gp+NrIOCpFHA60AnYCZhUPkTwnzY/oTHdS5sgjo651x+xfjJapnksk5hHFAGfAM4NEq7yMwGAj8mDHz8NK+1c865JuAP2Ukvl6CwP3CXmc1kc+NLAGb2N+B5wt1SnXOudatS5i2mcgkKPQhdRBBaDADdEva/QQgczjnXqskyb3GVy0DzMsLYAWb2laT1hGc0V9sKaJ/HujnnXNOI8UU/k1yCwgdsXpkH8B/gIklTCS2OC4AP81c155xrIj7QnFYu3UcPAkWSukQ/jwEKCUuvJxIGmq/Ja+2cc64ptI4b4rVKWbcUzOwREp7taWbvJzxYpxJ43szm5L+KzjmXZxkflhlfjVrRbGZfAnfkqS7OOdc8vPsoLb/NhXMuduI8uyiTtEFB0j8aUJ6Z2f82oj7OOdf0PCikVV9L4ZwGlGeABwXnnNtC1fc8hVxmJjlA5ZX0euDNlq6Gy8Hziz9o6Sq4LPUelvFRAFkR2XUfiXg2KHxMwTkXL0Z2t7GIY0TAg4JzLo5iesHPhgcF51zs+Oyj9DwoOOfix4NCWh4UnHPx40EhLQ8KzrnY8e6j9DwoOOfiJ8YP0ckk56AgaQjhcZz9gH+Z2TxJHQnPWlhqZmX1FuCccy3MWwrp5bRATdJNwCzgLsIzm4dGuzoDM4Dz81o755xrCn7r7LSyDgqSfgxcDvwJOILo+cwAZrYWeBo4Lt8VdM65fPPHcaaXS0vhfGC8mV0MvJ9i/0fAsHxUyjnnmpS3FNLKJSjsCLxcz/4VQJ/GVcc555qeqjJvjSpfuleS1bP1j/JNSbP/4RRldpd0h6QlkkolTZN0fONqWlcuA80bgW717N8OKG5UbZxzrm34JfCXpLQOwIvAR2a2NCF9NnBWUt6VKcocD+wOXAHMJdzJeryk48xsQj4qDbkFhamER2/emrxDUmfgTOCNPNXLOeeaThN3D5nZF8AXiWmSRgNdgLuTsm8ws7fqK0/S0cBhwGgzGx+lTSZM9rkVyFtQyKX76BZgX0kPALtEaf0lHQlMAQYBv81XxZxzrklkMcisphlXOBfYQMKz7nNwElACPFWdYGYG3AcMl7RTXmpIDi0FM3tF0k+A3wOnR8kPRK9lwHlm5g8TcM61ftlf8AslFddblFmvTIVIGgAcRVjbtTZp9zBJa4AehG6h+4CbzKw8Ic9IYIaZJY92fJS4P1M9spHT4jUzu0vS08ApwHDCtNTZwL/NbFE+KuScc02u+WcXnQ20p27X0WvAw8BnQHfgRMIasD0IrYNqRYQ1YslWJ+zPi5xXNEcDJH/IVwWcc6655TC7qCSblkAWzgE+N7NXExPNbExSvmclLQOukbS/mb2emL2e8vMW5vyRm8652GnOxWuS9ies4bony0Pui173TUhbRerWQO/odXWKfQ2SdUtB0qQsspmZHdqI+jjnXNNr3u6jc4FKNl/sM6n+sp7YnvkEOFlSu6RxhVHR6/TGVXGzXLqPhlL3V1kADCCcxEpgfZ7q5ZxzTaeZgoKkboQx2BdzGHetXrOQOE11PPC/hFsJPZWUd6aZ5WWQGXKbfTQ4VbqkTsDPgR8AB+anWs4513Sa8d5GpxIGkP9Rpw7SAcBVwOPAfMLi4BMI19JHzSxx3dcEYDJwt6Qiwiyls4H9o2PyptHPUzCzTcCvo3myvwNOa3StnHOuKTVfUPgBoRfl6RT7lkSv4wi3CKoCZhK+ZNeazGNmJulE4MZo60WYgjrazJ7JZ4Xz+ZCd14Ff57E855xrEo29t1G2zOyAevZ9DhyTQ1lrgQuircnkMygMATrmsTznnGsaMb4Laia5zD7aNs2u3oR7clxIuN2Fc861anF+XkImubQU5pE+voqwIu/CxlbIOeeanAeFtHIJCuOo+6s0wqKJWcArKe7L4ZxzrY8HhbRymZI6tgnr4ZxzzSPbFcsxDRxZ3eYieuLPF5IubuL6OOdck/NnNKeXVUvBzNZFCybWNXF9nHOu6cX4op9JLjfEewvYs6kq4pxzzcay2GIql6BwFfBdST+QpKaqkHPONTXvPkqv3u6jaG3CCjMrJdzCYg3wd+BmSV8QHi2XyO+S6pxr/WJ80c8k05jCXOD7wENsvkvqgmhfvyasl3PONZnmus3FlihTUFC0pb1LqnPObUlEdt1Dce0jz+e9j5xzbsvg3UdpeVBwzsWPB4W0sgkKB0jKZeXz/Y2oj3PONbk4zy7KJJuL/Y+iLRMR4q8HBedc6+ZBIa1sgsJd1H5WqHPObdFU5VEhnWyCwmtm9mCT18Q555qJdx+l5wPNzrl4yfY2FjENHB4UnHOx4y2F9DwoOOfix4NCWvUGBTPL5YZ5zjm3RfDbXKTnLYWYOPWCZewwqpSv71LKgO3KWPplB87+5k71HrP3oWsZ/aMV7DCqlA6dqli5uAPvvdqDP/2/QXXy9uhVwfcuXM63jiyhz4ByNqxvx/zPOnP/Lf2ZPrV7U51Wm/DwH/ry+cddmf1RF5Yu6ES/QWXcP3VGyrwP/LY///xd/5T7fjhmEaf8ZEXa99m4Qfzo4OEs+7ITx52zggtuXFQnz6vPFPLEXX2ZM6Mz7drB0J1L+d7PlrH3oV817ORaKe8+Ss+DQkyce81S1q5uz+fTu9C9Z2XG/Gf8fClnXbaMaZN78MCt/dhU2o6+25QzZERpnbx9tynjlse/oHO3Sl58qDcL53SiW48qhu5UStGA8qY4nTblnl8PpMdWFewwspR1a9tndcyPr19EYe+KWmlf3yX5psW13X/LANauTv9f/pE/9uUfNw5kh5EbOOvypUgw6fGtuO6soVzxhwUcMnpNVnXbIphHhXRaVVCQso7fQ8xsXlPWpa05e5/hLF3QCYC/TppJ527pA8NuB3zFWZct476b+/Pg7ZlvhnvlHxfQvsD4yaHDWL28Q97qHBf3vjmDAduVAfCjg4excX3mwPCto0ro/7WyrN9j9kddGP/3rfnhtYu56/pt6uxfs6KAB37bn8HDS/n9c7MoiD7GE85dwU+PHMafrt2Gbx5eQrcebaPfxVsK6bW2MYN9k7ZXgaUp0pe0VAW3VNUBIRvf+9ly1qwo4OE/9AWgc9dK0sXrkd9cx8hvruffd27N6uUdaF9gdOrSNi4czaU6IORq/VftqKzInK+yEm6//GvsefBa9ju6JGWeGdO6UV7WjkNOWlMTEAAKOsDBJ61hXXEBb75Y2KB6tkr+5LW0WlVLwcxqrZyWtAbYlJyeTFInM9vUpJWLiU5dKhm1zzqmTurJUaet5oxLltJnQAUbS8VbLxXy5zEDKV65+aqx96FrAVixqCPX3zeXvQ5eS/sCWPhFR/51W38mPbFVS51Km/aTQ4exYV172rU3hu26gTMuWcpeh6Tu93/irq358vNOjPn7vLTllW8KN4pOFdCr0z57tyuHfadtdCE19UCzpIOAyWl2jzCzzxLyHg78EvgG8BUwHrjSzIqTyuwO3AicAvQCPgHGmdnT+ax7a2spZCRpiqQPJB0m6R1JG4Gro30maWyKY+ZJujcpbZCkf0haIqlM0ixJFzfHObRm2wwpo30BjNh9Az8Zt4jnHyzi+nMH89z9RRxwbDE3P/ZFrQvHoO1DLL74loX06FXBLRdty60/H0RFeTuu/OMCjjh1dUudSpvUrbCSo7+/kvN/tZCx98zh3KsXs3xRB8acOZSXHuldJ//SBR154Lf9OeOSZfV2N203bCMAH7zRo86+D6O0FYs75uksWp6qMm95ciV1ezrm1dQjBI8JwJfAccBlwPHAc5KSr8/jgTOAa4FjgBnAeElH5622tLKWQg4GAXcDNwCzCdE1a5IGAlOBdcA1hA/kcOC3korMbEx+q7vl6NI9jDX06lPBbZcN4oUHiwD47wuFbFjXnjMvXcbhp6zm2fv7ANC1e/jfs2FdO674zvZUlIe/4/8+X8i9b37GOVct4eV/b4VZXB9Zkl+jz6s7u+jI763mx4cM569jB3LAscV06bb5inbHVYPov20ZJ/94eb3lDhmxkd3/Zy1vvljI3385gCO+F4L5S4/0ZtrkEBQ2lm5x3yHTa76B5lkZejpuBqYDp5pZFYCkJcBLhBbBI1Ha0cBhwGgzGx+lTSY8EfNWQmDJiy31Uy4CTjezu8xssplNy/H4sUBX4EAzu8fMXjGzK4E/AJdLqvOVS1JxfRvQJjpcN0X/8SsrYeJjtbt+Xv53+HmXb63bnH9jyD/lyV41AQFgXUkBb73Uk6J+FTWtCdc0evau5JgzV7KupIAZ73SrSZ/4+Fa8958eXPibhbXGCdK55i/z2e/oYh77S1/OO3AE5x04glef6cVPb1gIQLcemWetbSlkmbcmr4O0DbAX8EB1QAAws5eBRcDJCdlPAkqApxLyGXAfMFxS/fPLc7ClthRWmNkbjTj+aGAisCLpWRETgIuBfchj5N2SrFwSrh7rStpTXlb7O0P1zKLuhZsvDisXh7Q1K+pedVYvD7/aHr3azsWkteoXdQ2VrA4zl8o2ib+OHcheh65lq77lLJobun5WLQ2f04av2rNobkcKe1fWfJ49elVy3d/nsWZFAQvndKJL1yqG7lxa01IYtEMbCu7NN5D8V0mPAeuB14BfmNm70b6R0ev0FMd9nLC/Ou+MxOAR+Shxfz4qvKUGhcbOPuoHjAbSTaLvk5xgZr3qK7CttBaKV3Zg2cIObD2wnE5dqmpaDgBbR2sOildu/rOZ+UFXjj17FX0G1O2v7pMiv2sai+aG2WVbbR2mI5VtbEfJqg5MfaWQqa/U/bOc+HhvJj7eO+WCt622rqgpB2DqxJ4A7H3I2qaqfvPKtiUQ8hRG/7fTZ0t9bSgBbgemAKuBEcBVwBuSDjSztwk9HkT7k60Gdk/4uQiYlSZf9f682FL/t6b7SDcBqeZeJv/CVgLvAb9IU87cBtarTZj42FacfvFyjv7+Ksb/beua9GPPWgXAO5N61qT994We/N+4dhwyupgHb+/Hxg3hm2rvvuV866i1LPyiI4vnZT8d1qVXWQEbN7SjW8/aXxaXL+rAs/f3oedWFey053ogTCO+9q66f8bFqwr449VheupRp61iyIiN9b7nrA+78MJDReyyb5h63FY09fMUzOx94P2EpNckPU1oFdxAGB+oyZ6umAw/Z7svJ1tqUEhnHrBLYoKkQ4Dk+yw8T/hQZplZG/n6U79DT15N30Hhm3thUQUFHYzTLloGwPKFHZj4+OZhlEfv7Mv+x5Rw3nWLGTR0E3NmdGbnvddz6MnFvP9ad/7zVK+avOtKCvjbuIFcfMtCfv/s57z4cG8KOlZx7FmrKOhg/OnaurfEcLW98thWLF8YundKVhVQUa6aRYN9B5XVTAMtXd+es/cZwb5HlrDt1zfRvVcFCz/vzAsPFVG6vh1X3zmPTl3CtaGgAxxwbN01CUu/DO8zYLtNdfbfd3N/Fs3pxLDdNtCtRyWff9yFFx8pok//ci6/Y36TnX+LyP4SWpKplyDrtzRbKuklwuwigFXRa6pv+b2p3YJYVU8+SN3aaJC2FhT+CYyTNA74D7ATcAGhKZdoDHAEoSl3B2EGU3dgB8K0sCPMrE11hB952mq+8a3a3/TOuXIpAB/+t1utoLBhXXsuPWkHzr58KfseWcKRp61m5ZIOPPT7vvzr9n5UVdWeSfT8v4pYu7o9p5y/grOvWEpVFXz6bld+89Ptag18utRefKiIj96s/b3lvpsHALDLvutqgkLHzlXsf3QJn73flTdfLKR0fXsKe1ew2wFfccr5yxm+W/23uchk+5GlvP9aD959tUd0W5MyTjh3Bd/72fJa40htQQuuaG7H5pD0SfQ6kjDbKNEo4L8JP38CnCypXdK4wqjoNdW4RIPIWvE9QCQ9CexqZoMT0qYAvcxs1xT5OxIWgZxGGBeYClxEGLGfYmbnJOTtD1xHmO87gBA4ZhNaEb+yHH8xkooL6FB4kE7I5TDXwl5c/EFLV8FlqfewOQAUl1Q2eH6zpOL2BZ0L9zvi+ox533jpF1RWbMxbSyG65nwCvGdmh0dp7xACxV4JU1IPBV4BTjOzh6O0Y4BngRPN7KmEMl8F+prZ8HzUEVp5S8HMTkyRdlA9+csIi0WuTNo1OEXepcD5jaqgc27L1MTfhSX9C5hDGLtcAwwnXJe6EC22jVxJaCU8JOkuYCBwE/A28GhCvgmEFdJ3SyoijHueDewP5PWbaKsOCs451xSaofvoY+B7wM+AboQxgSmEXoiarh4zmyTpWOB64DnCQtwngSsSu7DNzCSdSLjNxY2E21zMICxmeyafFfeg4JyLnWaYffQb4DdZ5n0BeCGLfGsJY6QXNK529fOg4JyLn9Y7lNriPCg452JFgLKYRxLXu3V5UHDOxY8/8iMtDwrOudjJpqUQVx4UnHPxku2T1WIaNzwoOOdip6lnH23JPCg45+LHu4/S8qDgnIudpn5G85bMg4JzLn68pZCWBwXnXPx4TEjLg4JzLnZU5f1H6XhQcM7Fj8eEtDwoOOdixrJcvBbPPiYPCs65+PGB5rQ8KDjn4seDQloeFJxz8WJkN6YQ07jhQcE5Fzs++yg9DwrOufjx7qO0PCg45+LHg0JaHhScc/HjvUdpeVBwzsWOP2QnPQ8Kzrn48aCQlgcF51z8VHr/UToeFJxz8eMthbQ8KDjn4seDQloeFJxz8ePPaE6rXUtXwDnnmpUBVpXF1vC3kHSopHslzZS0QdJCSU9IGpWUb4okS7E9nKLM7pLukLREUqmkaZKOb3gtU/OWgnMuZizLgeZGtSb+DygCbgM+BfoBVwDvSDrIzN5KyDsbOCvp+JUpyhwP7B6VMxc4Bxgv6Tgzm9CYyibyoOCci5+mH1P4qZktT0yQ9BLhYn45cHLCrg1JQaIOSUcDhwGjzWx8lDYZGArcCuQtKHj3kXMufswyb40qvnZAiNKKCa2CQQ0o8iSgBHgqoTwD7gOGS9qpYTWty4OCcy5+mjgopCJpa2AkMD1p1zBJayRVSJot6VpJHZLyjARmmFlyv9dHCfvzwruPnHPxk/2tswslFdeXwcx6ZSpEkoC7CF/Ef5uw6zXgYeAzoDtwIjAO2IPQOqhWBMxKUfTqhP154UHBORc/zb9O4RbCBf8HZvbp5mrYmKR8z0paBlwjaX8zez1hX32VztsJefeRcy5+Kqsyb0GJmfWqb8v0VpJuAC4FLjKze7Oo3X3R674JaatI3RroHb2uTrGvQTwoOOdix6wq45YPksYB1wBXmNkdWR5WfV1OrMQnwAhJydfs6nUPyeMUDeZBwTkXP1WWeWskSb8AxgBjzOyWHA6tXrOQOE11PNALOC5F3plmNqOh9UzmYwrOuXgxshtTaNyK5kuBscCzwCuS9knYvcnM3pd0AHAV8DgwH+gGnAD8AHjUzN5IOGYCMBm4W1IRYb3D2cD+0TF540HBORc/2c8+aqjqb/THRlui+cBgYEn08zigD6G7aCbwc+APiQeYmUk6Ebgx2noBMwiL2Z7JZ8U9KDjnYibbdQgNbyqY2UFZ5PkcOCaHMtcCF0Rbk/Gg4JyLHausbOkqtFoeFJxz8eO3zk7Lg4JzLn7yNOW0LfKg4JyLHfOWQloeFJxz8eMthbRk/qzSvJBUBaiA5JsbutasW08fcNxSlKytQoKqKlNDy4hubleYzf/TCsohus1FQ99vS+QthfypAtpVUL62pSuSZ4XRa0mL1qKJlLS1Tytoq59ZTzMa+xW/BGou+FnnjxNvKbh6Vd82OG7flrZk/pm5xvB7HznnnKvhQcE551wNDwrOOedqeFBwzjlXw4OCc865Gh4UnHPO1fCg4JxzroavU3DOOVfDWwrOOedqeFBwzjlXw4OCc865Gh4U2ihJ50iyNNtVWZYxVpIPOjWjej6z5G1wS9fVtU1+l9S27yxgdlLagpaoiMvKvkk/3wTsCJyUlL6kearj4saDQtv3sZl90NKVcNkxs7cSf5a0BtiUnJ5MUicz29SklXOx4N1HMSRpT0mPSJovqVTSHEn/kNQvi2NPkTRV0lpJ6yTNlnRbUp5BUXlLJJVJmiXp4iY7oZiRNEXSB5IOk/SOpI3A1dE+kzQ2xTHzJN2blOafk6vDWwptX3tJiZ+zAYOBGcCDwBpgW+DnwBuSdk73jVPSfsAjwJ3AtUAlMATYMyHPQGAqsA64BvgSOBz4raQiMxuT17OLr0HA3cANhO7Br3I52D8nl44HhbZvWtLP682sO/BYdUIUNF4F5gNHAU+lKWtfwuMJL0hK/3vCv8cCXYE9zKy63/sVSR2ByyXdZmarG3QmLlERcIKZvdHA48fin5NLwbuP2r4zgL0StgMk9ZT0y6i7oBQoJwQEgOH1lPU20EvSvyUdL6lPijxHAxOBFZIKqjdgAtAJ2CdP5xV3KxoREMA/J5eGtxTavhnJA82SngMOBK4H3iV0PbQD3gK6pCvIzF6TdBJwIfAo0EHSNOAXZvZ8lK0fMBrSPgQ3VSBxuWvs7CP/nFxKHhRiRlIv4NvAWDO7JSF9+2yON7MngSejbob9gOuApyWNNLOZwErgPeAXaYqY2/DauwTp1o9sInzTT1aU9LN/Ti4lDwrxUwUIKEtKPy+XQsysDJgsqQqYAuwEzASeBw4DZpnZ2kbX1uVqHrBLYoKkQ4DuSfn8c3IpeVCIGTNbK+l1wmDiCsJCtqOBYzIdK2kcsA2hL3oR4dvn5UAx8GaUbQxwBGEm0x2EmTHdgR2A44AjzKwyn+fkavknMC76rP5DCNYXACVJ+fxzcil5UIin04E7gFujnycSpiPOy3Dc24QLzC2EPuc1hHGI881sKYCZLZK0J6Fb6VpgAOGCNJvw7bQqnyfi6rgZ6AGcA1xGmHb6XZJmlPnn5NLx5yk455yr4VNSnXPO1fCg4JxzroYHBeecczU8KDjnnKvhQcE551wNDwrOOedqeFBwLUrS4ORnAKRKa00k3ZvtY0qj5xhMacR7TZE0r6HHZyjbkp+x4JwHhRiSdFCKZ/6uk/SupIsktW/pOjZUFFDGStq1pevi3JbIVzTH20OEWyULGEhYBXs7sDPwoxarVbiNdxegogHHDibc5G0e8EHeauRcTHhQiLf3zOyf1T9I+jPwKfBDSWPMbFmqgyT1MLOcnvSVCwvL7Dc2VfnOufS8+8jViO6W+Sah5TAUNveJS9pN0ouSSoCPqo+R9HVJDyQ853eepFskdUsuX9L+kt6Ingu9TNIfqXv3znrHFCSdLGmypGJJGyTNlHSHpI6SzgEmR1nvSegam5JwvCT9JOoq2yDpq6i8g1O8V+foXBZHdZ4q6Yjcfqt1STpC4RnZc6JyiyW9JOnAeo4ZKukpSSUKz8ceL2loinxZn59zqXhLwdWQJMJdMiHcb7/atsAkwoN1Hie6kEvaI0ovBv5KuHPqNwgP4dlP0oFmVh7l/SbwCuGBPjdFx3wPuD+H+t1AeJ7wDOA2woNmtgdOJtzY7VXgxijPXcBr0aGJLZ4HgNMIjyO9h/DsgTOAlyWNNrOnE/I+BJwIPAO8GL3XEzT+WQPnAL0J576QcOfZHwITJR1sZq8l5e9GCHZTgauBrwPnA/tI2q36ZoQNOD/n6jIz32K2AQcRHtJyHeFup1sT7sH/tyj9zYS886K0H6Yo50PgM6BHUvpJ0THnJKT9l/AMhx0T0joSLnRGeOhPdfrgFGl7R2mTgM5J7yc239zxoOT3TlGvHyWlFxCeZT03oZwjorz3JuU9MUq3LH/X84ApSWndUuTrRwjEE5LSp0Tvd3uac/lLQ84vSq9zfr755t1H8XY9sAJYTrjAnws8TbjwJVpN+NZZQ9IoQiB5EOgkqU/1BrwOrCdcWJHUF9gXeMrMZlWXYeFBPbdlWdczoterzazWeINFsijj+4SWypNJ9e1FaA0MJnwLh82/g1sSC7Dw5LmZWdY5JTNbX/1vSd0lFQGVhFuTfzPNYb9JKmN8VI8TE5JzOT/nUvLuo3i7i9AlZISL+CwzW50i3xdW94ErI6LX66MtlX7Ra3Xf92cp8szIsq5fj+r5YZb5UxlBeNZAygH0SD9gFqHOVdG/k30KDGtoJRQefXoDcCThgp0oVXArttpdRIn1OFFStyjQ5HJ+zqXkQSHeZpvZK1nk25AiTdHrrcALaY5bk5Q31QVPKdJSUZrjcyFCy+j0evJMT8hbXzkNq4DUnTD20Y0w/fdjwrf7KsJ4wSEpDkt33sn1yOX8nEvJg4JrqNnRa2UWgeWL6HVEin2p0lKZCRxF6LKaWk+++gLHbGBH4C0zW5fh/b4gdH/tCHyStG94hmPrcyhhTci5ZpbcJferNMdsJal/itbCcGB5QndULufnXEo+puAa6n3Ct87/SzM1skBSbwAzW054bOcJknZMyNMRuCTL93swer1RUqcU71f9rbn6Ytg7RRn3E/7mf53qDST1S/ix+vGVlyflOZFGdB0Rxg4g6Vt+NNU13XgCwFVJ+U+K6vFkQnIu5+dcSt5ScA1iZibpTMJsoI8k/YPwjborYVrraEJ3yL3RIT8nzKR5Q9Kf2DwlNau/QTObKukm4ErgXUmPAEuBIcB3CLOTigljFF8B50vaEKUtN7NJZvaYpHuACyTtDjxLmPEziDAQvgPR+IeZvSjpGeDsKLi9QJiS+mNCMByZ22+sxutRvW+VNJgwJXVX4ExCV9KoFMesBEZLGkj4HVZPSV0GjE34HWV9fs6l1dLTn3xr/o3N0zYvyyLvPJKmVCbt3w74S5SvDFgFvEv4tvq1pLz/Q5iaupEw4+lPhItrximpCftOA94gXPjXEwavbwc6JuQ5Gngveh9Lrj/hAvwasDbKM4+w/uDUpHxdCGMmS4FS4B3C4PC9NG5K6i6EILMmOo8pwAGpyo32zSNczJ+K6vxV9O8d0rxntufnU1J9q7NVz8l2zjnnfEzBOefcZh4UnHPO1fCg4JxzroYHBeecczU8KDjnnKvhQcE551wNDwrOOedqeFBwzjlXw4OCc865Gh4UnHPO1fj/w7t9ke8ebqMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(cls,test_X,test_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "second-senator",
   "metadata": {
    "id": "second-senator"
   },
   "source": [
    "Plot an ROC cure to show the classifier performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "educational-relay",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "executionInfo": {
     "elapsed": 966,
     "status": "ok",
     "timestamp": 1644718048484,
     "user": {
      "displayName": "",
      "photoUrl": "",
      "userId": ""
     },
     "user_tz": 300
    },
    "id": "educational-relay",
    "outputId": "ddd32b9c-b3b0-4447-bcd8-14e043d23aac"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x170406430>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc_curve(cls,test_X,test_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "seasonal-retro",
   "metadata": {
    "id": "seasonal-retro"
   },
   "source": [
    "Use the default view from SciKit Learn to plot the decision tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "physical-ordering",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 700
    },
    "executionInfo": {
     "elapsed": 800,
     "status": "ok",
     "timestamp": 1644718051832,
     "user": {
      "displayName": "",
      "photoUrl": "",
      "userId": ""
     },
     "user_tz": 300
    },
    "id": "physical-ordering",
    "outputId": "6f2c8a7c-cb9d-4684-c350-20c680c5ce84"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(279.0, 453.0, 'LogP <= 3.116\\ngini = 0.444\\nsamples = 7486\\nvalue = [2488, 4998]'),\n",
       " Text(139.5, 271.8, 'LogP <= 2.603\\ngini = 0.228\\nsamples = 5116\\nvalue = [673, 4443]'),\n",
       " Text(69.75, 90.59999999999997, 'gini = 0.17\\nsamples = 4468\\nvalue = [419, 4049]'),\n",
       " Text(209.25, 90.59999999999997, 'gini = 0.477\\nsamples = 648\\nvalue = [254, 394]'),\n",
       " Text(418.5, 271.8, 'HBD <= 4.5\\ngini = 0.359\\nsamples = 2370\\nvalue = [1815, 555]'),\n",
       " Text(348.75, 90.59999999999997, 'gini = 0.337\\nsamples = 2298\\nvalue = [1805, 493]'),\n",
       " Text(488.25, 90.59999999999997, 'gini = 0.239\\nsamples = 72\\nvalue = [10, 62]')]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (10,10)\n",
    "plot_tree(cls,feature_names=desc_cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "nuclear-preservation",
   "metadata": {
    "id": "nuclear-preservation"
   },
   "source": [
    "Use dtreeviz to plot the decision tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "appropriate-trinidad",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 710
    },
    "executionInfo": {
     "elapsed": 2673,
     "status": "ok",
     "timestamp": 1644718068112,
     "user": {
      "displayName": "",
      "photoUrl": "",
      "userId": ""
     },
     "user_tz": 300
    },
    "id": "appropriate-trinidad",
    "outputId": "1fcc465d-421d-4f2b-c3c2-3fa05e8aaeb8"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<dtreeviz.trees.DTreeViz at 0x7f83f0473110>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viz = dtreeviz(cls, train_X, train_y, feature_names = desc_cols, \n",
    "               target_name = \"Solubility\",class_names=[\"False\",\"True\"],scale=2)\n",
    "viz"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "concerned-warrior",
   "metadata": {
    "id": "concerned-warrior"
   },
   "source": [
    "As a final check we're going to see if we can fit a model to incorrect data.  If we shuffle the y values, we shouldn't be able to get a good model. \n",
    "\n",
    "Shuffle the y-variable as a sanity check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "painful-median",
   "metadata": {
    "executionInfo": {
     "elapsed": 282,
     "status": "ok",
     "timestamp": 1644718071690,
     "user": {
      "displayName": "",
      "photoUrl": "",
      "userId": ""
     },
     "user_tz": 300
    },
    "id": "painful-median"
   },
   "outputs": [],
   "source": [
    "sol_df['fake'] = sol_df.IsSol.sample(frac=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "functional-premises",
   "metadata": {
    "id": "functional-premises"
   },
   "source": [
    "Split the dataframe into training a test sets. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "early-invite",
   "metadata": {
    "executionInfo": {
     "elapsed": 284,
     "status": "ok",
     "timestamp": 1644718077367,
     "user": {
      "displayName": "",
      "photoUrl": "",
      "userId": ""
     },
     "user_tz": 300
    },
    "id": "early-invite"
   },
   "outputs": [],
   "source": [
    "fake_train, fake_test = train_test_split(sol_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "immune-ambassador",
   "metadata": {
    "id": "immune-ambassador"
   },
   "source": [
    "Split the training and test sets into X and y variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "numerical-blake",
   "metadata": {
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1644718079980,
     "user": {
      "displayName": "",
      "photoUrl": "",
      "userId": ""
     },
     "user_tz": 300
    },
    "id": "numerical-blake"
   },
   "outputs": [],
   "source": [
    "fake_train_X = fake_train[desc_cols]\n",
    "fake_train_y = fake_train.fake\n",
    "fake_test_X = fake_test[desc_cols]\n",
    "fake_test_y = fake_test.fake"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "verbal-permission",
   "metadata": {
    "id": "verbal-permission"
   },
   "source": [
    "Build a classifer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "explicit-moscow",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 262,
     "status": "ok",
     "timestamp": 1644718086198,
     "user": {
      "displayName": "",
      "photoUrl": "",
      "userId": ""
     },
     "user_tz": 300
    },
    "id": "explicit-moscow",
    "outputId": "a8ff4632-e352-45b7-8d00-1900a0ef2e8c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=2)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fake_cls = DecisionTreeClassifier(max_depth=2)\n",
    "fake_cls.fit(fake_train_X,fake_train_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "elegant-husband",
   "metadata": {
    "id": "elegant-husband"
   },
   "source": [
    "Generate the predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "celtic-adelaide",
   "metadata": {
    "executionInfo": {
     "elapsed": 272,
     "status": "ok",
     "timestamp": 1644718088841,
     "user": {
      "displayName": "",
      "photoUrl": "",
      "userId": ""
     },
     "user_tz": 300
    },
    "id": "celtic-adelaide"
   },
   "outputs": [],
   "source": [
    "fake_pred = fake_cls.predict(fake_test_X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "backed-syndicate",
   "metadata": {
    "id": "backed-syndicate"
   },
   "source": [
    "Calculate the AUC score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "desperate-tribute",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 267,
     "status": "ok",
     "timestamp": 1644718091505,
     "user": {
      "displayName": "",
      "photoUrl": "",
      "userId": ""
     },
     "user_tz": 300
    },
    "id": "desperate-tribute",
    "outputId": "4b3f13cc-bf5c-476a-81db-0cf75dc0f740"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5147169833232684"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(fake_test_y,pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "vocational-diabetes",
   "metadata": {
    "id": "vocational-diabetes"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "Copy of tutorial_02_classification_model.ipynb",
   "provenance": [
    {
     "file_id": "https://github.com/PatWalters/EFMC/blob/main/tutorial_02_classification_model.ipynb",
     "timestamp": 1644718189634
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