tutorial_04_decision_tree.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "considerable-disco",
   "metadata": {},
   "source": [
    "**Building a Simple Decision Tree**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "monthly-valuable",
   "metadata": {},
   "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 # descision 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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "worth-bernard",
   "metadata": {},
   "source": [
    "Enable Pandas **progress_apply**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "undefined-booking",
   "metadata": {},
   "outputs": [],
   "source": [
    "tqdm.pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "charitable-break",
   "metadata": {},
   "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": 3,
   "id": "spanish-decade",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_descriptors(smi):\n",
    "    mol = Chem.MolFromSmiles(smi)\n",
    "    if mol:\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": "static-statistics",
   "metadata": {},
   "source": [
    "Read data from [\"ESOL:  Estimating Aqueous Solubility Directly from Molecular Structure\"](https://pubs.acs.org/doi/10.1021/ci034243x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "drawn-pierre",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"delaney.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abstract-outline",
   "metadata": {},
   "source": [
    "Get the dataframe column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "significant-italy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Compound ID', 'measured log(solubility:mol/L)',\n",
       "       'ESOL predicted log(solubility:mol/L)', 'SMILES'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "genetic-communication",
   "metadata": {},
   "source": [
    "Change the name of column 1 to \"LogS\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "exposed-tomorrow",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = list(df.columns)\n",
    "cols[1] = 'LogS'\n",
    "df.columns = cols"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "motivated-postcard",
   "metadata": {},
   "source": [
    "Add a new column \"IsSol\" to indicate whether a molecule's solubility is greater than 200uM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "julian-representative",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['IsSol'] = df.LogS > math.log10(200 * 1e-6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "certified-passage",
   "metadata": {},
   "source": [
    "Plot solubility as a sanity check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cloudy-provider",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x157bd0d00>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 421.25x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(x='LogS',hue=\"IsSol\",data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ruled-church",
   "metadata": {},
   "source": [
    "Add the descriptors to the dataframe.  Note that all of the descriptors are going into one column called \"desc\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "affecting-solomon",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1144/1144 [00:00<00:00, 2196.84it/s]\n"
     ]
    }
   ],
   "source": [
    "df['desc'] = df.SMILES.progress_apply(calc_descriptors)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ordinary-romania",
   "metadata": {},
   "source": [
    "Split the descriptors into their on own columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "racial-spell",
   "metadata": {},
   "outputs": [],
   "source": [
    "desc_cols = ['MW','LogP','NumAromatic','HBD','HBA']\n",
    "df[desc_cols] = df.desc.to_list()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hydraulic-chapter",
   "metadata": {},
   "source": [
    "We no longer need the \"desc\" column, let's get rid of it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "assured-interaction",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(\"desc\",axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "green-deviation",
   "metadata": {},
   "source": [
    "Let's check the dataframe to make sure it's ok. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dried-guitar",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Compound ID</th>\n",
       "      <th>LogS</th>\n",
       "      <th>ESOL predicted log(solubility:mol/L)</th>\n",
       "      <th>SMILES</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>1,1,1,2-Tetrachloroethane</td>\n",
       "      <td>-2.180</td>\n",
       "      <td>-2.794</td>\n",
       "      <td>ClCC(Cl)(Cl)Cl</td>\n",
       "      <td>True</td>\n",
       "      <td>167.850</td>\n",
       "      <td>2.59540</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1,1,1-Trichloroethane</td>\n",
       "      <td>-2.000</td>\n",
       "      <td>-2.232</td>\n",
       "      <td>CC(Cl)(Cl)Cl</td>\n",
       "      <td>True</td>\n",
       "      <td>133.405</td>\n",
       "      <td>2.37650</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1,1,2,2-Tetrachloroethane</td>\n",
       "      <td>-1.740</td>\n",
       "      <td>-2.549</td>\n",
       "      <td>ClC(Cl)C(Cl)Cl</td>\n",
       "      <td>True</td>\n",
       "      <td>167.850</td>\n",
       "      <td>2.59380</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1,1,2-Trichloroethane</td>\n",
       "      <td>-1.480</td>\n",
       "      <td>-1.961</td>\n",
       "      <td>ClCC(Cl)Cl</td>\n",
       "      <td>True</td>\n",
       "      <td>133.405</td>\n",
       "      <td>2.02890</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1,1,2-Trichlorotrifluoroethane</td>\n",
       "      <td>-3.040</td>\n",
       "      <td>-3.077</td>\n",
       "      <td>FC(F)(Cl)C(F)(Cl)Cl</td>\n",
       "      <td>True</td>\n",
       "      <td>187.375</td>\n",
       "      <td>2.91890</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>1139</th>\n",
       "      <td>vamidothion</td>\n",
       "      <td>1.144</td>\n",
       "      <td>-1.446</td>\n",
       "      <td>CNC(=O)C(C)SCCSP(=O)(OC)(OC)</td>\n",
       "      <td>True</td>\n",
       "      <td>287.343</td>\n",
       "      <td>1.98820</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1140</th>\n",
       "      <td>Vinclozolin</td>\n",
       "      <td>-4.925</td>\n",
       "      <td>-4.377</td>\n",
       "      <td>CC1(OC(=O)N(C1=O)c2cc(Cl)cc(Cl)c2)C=C</td>\n",
       "      <td>False</td>\n",
       "      <td>286.114</td>\n",
       "      <td>3.42130</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1141</th>\n",
       "      <td>Warfarin</td>\n",
       "      <td>-3.893</td>\n",
       "      <td>-3.913</td>\n",
       "      <td>CC(=O)CC(c1ccccc1)c3c(O)c2ccccc2oc3=O</td>\n",
       "      <td>False</td>\n",
       "      <td>308.333</td>\n",
       "      <td>3.60960</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1142</th>\n",
       "      <td>Xipamide</td>\n",
       "      <td>-3.790</td>\n",
       "      <td>-3.642</td>\n",
       "      <td>Cc1cccc(C)c1NC(=O)c2cc(c(Cl)cc2O)S(N)(=O)=O</td>\n",
       "      <td>False</td>\n",
       "      <td>354.815</td>\n",
       "      <td>2.56214</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1143</th>\n",
       "      <td>XMC</td>\n",
       "      <td>-2.581</td>\n",
       "      <td>-2.688</td>\n",
       "      <td>CNC(=O)Oc1cc(C)cc(C)c1</td>\n",
       "      <td>True</td>\n",
       "      <td>179.219</td>\n",
       "      <td>2.02164</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1144 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Compound ID   LogS  \\\n",
       "0          1,1,1,2-Tetrachloroethane -2.180   \n",
       "1              1,1,1-Trichloroethane -2.000   \n",
       "2          1,1,2,2-Tetrachloroethane -1.740   \n",
       "3              1,1,2-Trichloroethane -1.480   \n",
       "4     1,1,2-Trichlorotrifluoroethane -3.040   \n",
       "...                              ...    ...   \n",
       "1139                     vamidothion  1.144   \n",
       "1140                     Vinclozolin -4.925   \n",
       "1141                        Warfarin -3.893   \n",
       "1142                        Xipamide -3.790   \n",
       "1143                             XMC -2.581   \n",
       "\n",
       "      ESOL predicted log(solubility:mol/L)  \\\n",
       "0                                   -2.794   \n",
       "1                                   -2.232   \n",
       "2                                   -2.549   \n",
       "3                                   -1.961   \n",
       "4                                   -3.077   \n",
       "...                                    ...   \n",
       "1139                                -1.446   \n",
       "1140                                -4.377   \n",
       "1141                                -3.913   \n",
       "1142                                -3.642   \n",
       "1143                                -2.688   \n",
       "\n",
       "                                           SMILES  IsSol       MW     LogP  \\\n",
       "0                                  ClCC(Cl)(Cl)Cl   True  167.850  2.59540   \n",
       "1                                    CC(Cl)(Cl)Cl   True  133.405  2.37650   \n",
       "2                                  ClC(Cl)C(Cl)Cl   True  167.850  2.59380   \n",
       "3                                      ClCC(Cl)Cl   True  133.405  2.02890   \n",
       "4                             FC(F)(Cl)C(F)(Cl)Cl   True  187.375  2.91890   \n",
       "...                                           ...    ...      ...      ...   \n",
       "1139                 CNC(=O)C(C)SCCSP(=O)(OC)(OC)   True  287.343  1.98820   \n",
       "1140        CC1(OC(=O)N(C1=O)c2cc(Cl)cc(Cl)c2)C=C  False  286.114  3.42130   \n",
       "1141       CC(=O)CC(c1ccccc1)c3c(O)c2ccccc2oc3=O   False  308.333  3.60960   \n",
       "1142  Cc1cccc(C)c1NC(=O)c2cc(c(Cl)cc2O)S(N)(=O)=O  False  354.815  2.56214   \n",
       "1143                       CNC(=O)Oc1cc(C)cc(C)c1   True  179.219  2.02164   \n",
       "\n",
       "      NumAromatic  HBD  HBA  \n",
       "0               0    0    0  \n",
       "1               0    0    0  \n",
       "2               0    0    0  \n",
       "3               0    0    0  \n",
       "4               0    0    0  \n",
       "...           ...  ...  ...  \n",
       "1139            0    1    6  \n",
       "1140            1    0    3  \n",
       "1141            3    1    4  \n",
       "1142            2    3    4  \n",
       "1143            1    1    2  \n",
       "\n",
       "[1144 rows x 10 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "choice-scroll",
   "metadata": {},
   "source": [
    "Split the data into training and test sets. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "indian-payroll",
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test = train_test_split(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "strong-hearing",
   "metadata": {},
   "source": [
    "Split the training and test sets into X and y variables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "simplified-legislation",
   "metadata": {},
   "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": {},
   "source": [
    "Create and train a classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "continuing-reasoning",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=2)"
      ]
     },
     "execution_count": 15,
     "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": {},
   "source": [
    "Predict on the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "measured-tribe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC = 0.87\n",
      "Matthews Correlation Coefficient = 0.75\n",
      "Cohen's Kappa = 0.75\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": {},
   "source": [
    "Plot a confusion matrix to show the classifier performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "danish-implementation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x157eadf10>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\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": {},
   "source": [
    "Plot an ROC cure to show the classifier performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "educational-relay",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x157e7aaf0>"
      ]
     },
     "execution_count": 18,
     "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": {},
   "source": [
    "Use the default view from SciKit Learn to plot the decision tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "physical-ordering",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(279.0, 453.0, 'LogP <= 3.11\\ngini = 0.453\\nsamples = 858\\nvalue = [298, 560]'),\n",
       " Text(139.5, 271.8, 'MW <= 268.077\\ngini = 0.193\\nsamples = 592\\nvalue = [64, 528]'),\n",
       " Text(69.75, 90.59999999999997, 'gini = 0.117\\nsamples = 515\\nvalue = [32, 483]'),\n",
       " Text(209.25, 90.59999999999997, 'gini = 0.486\\nsamples = 77\\nvalue = [32, 45]'),\n",
       " Text(418.5, 271.8, 'LogP <= 3.769\\ngini = 0.212\\nsamples = 266\\nvalue = [234, 32]'),\n",
       " Text(348.75, 90.59999999999997, 'gini = 0.417\\nsamples = 98\\nvalue = [69, 29]'),\n",
       " Text(488.25, 90.59999999999997, 'gini = 0.035\\nsamples = 168\\nvalue = [165, 3]')]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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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": {},
   "source": [
    "Use dtreeviz to plot the decision tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "appropriate-trinidad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
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       "<g id=\"graph0\" class=\"graph\" transform=\"scale(2 2) rotate(0) translate(4 291)\">\n",
       "<title>G</title>\n",
       "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-291 356.5,-291 356.5,4 -4,4\" />\n",
       "<g id=\"clust4\" class=\"cluster\">\n",
       "<title>cluster_legend</title>\n",
       "</g>\n",
       "\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>node1</title>\n",
       "<svg height=\"56px\" version=\"1.1\" viewBox=\"0 0 206.41 74.477931\" width=\"155px\" preserveAspectRatio=\"xMinYMin meet\" x=\"16.5\" y=\"-160\">\n",
       " <metadata>\n",
       "  <rdf:RDF>\n",
       "   <ns2:Work>\n",
       "    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\" />\n",
       "    <dc:date>2021-08-14T20:29:31.827642</dc:date>\n",
       "    <dc:format>image/svg+xml</dc:format>\n",
       "    <dc:creator>\n",
       "     <ns2:Agent>\n",
       "      <dc:title>Matplotlib v3.3.4, https://matplotlib.org/</dc:title>\n",
       "     </ns2:Agent>\n",
       "    </dc:creator>\n",
       "   </ns2:Work>\n",
       "  </rdf:RDF>\n",
       " </metadata>\n",
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      ],
      "text/plain": [
       "<dtreeviz.trees.DTreeViz at 0x157f604c0>"
      ]
     },
     "execution_count": 20,
     "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": {},
   "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": 21,
   "id": "painful-median",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['fake'] = df.IsSol.sample(frac=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "functional-premises",
   "metadata": {},
   "source": [
    "Split the dataframe into training a test sets. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "early-invite",
   "metadata": {},
   "outputs": [],
   "source": [
    "fake_train, fake_test = train_test_split(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "immune-ambassador",
   "metadata": {},
   "source": [
    "Split the training and test sets into X and y variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "numerical-blake",
   "metadata": {},
   "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": {},
   "source": [
    "Build a classifer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "explicit-moscow",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=2)"
      ]
     },
     "execution_count": 24,
     "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": {},
   "source": [
    "Generate the predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "celtic-adelaide",
   "metadata": {},
   "outputs": [],
   "source": [
    "fake_pred = fake_cls.predict(fake_test_X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "backed-syndicate",
   "metadata": {},
   "source": [
    "Calculate the AUC score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "desperate-tribute",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5428993258677364"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(fake_test_y,pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "vocational-diabetes",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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