Tutorials/Part3_Fingerprints_and_classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fingerprints and Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Nikolas Fechner\n",
    "##### Novartis Institutes for BioMedical Research, Novartis Campus, 4002 Basel, Switzerland"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n",
      "RDKit version:  2016.03.4\n",
      "Pandas version: 0.18.1\n",
      "Scikit-Learn version: 0.17.1\n",
      "Numpy version: 1.11.1\n",
      "MatplotLib version: 1.5.3\n",
      "Seaborn version 0.7.1\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "from rdkit import rdBase\n",
    "import matplotlib as mpl\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "print('RDKit version: ',rdBase.rdkitVersion)\n",
    "print('Pandas version:', pd.__version__)\n",
    "print('Scikit-Learn version:', sklearn.__version__)\n",
    "print('Numpy version:', np.__version__)\n",
    "print('MatplotLib version:', mpl.__version__)\n",
    "print('Seaborn version',sns.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Publicly available binary Ames test (mutagenicity) data compiled in \"Benchmark Data Set for in Silico Prediction of Ames Mutagenicity\", Katja Hansen et al., JCIM, 2009, 49(9),pp 2077-2081\n",
    "\n",
    "Data available from http://doc.ml.tu-berlin.de/toxbenchmark/smiles_cas_N6512.smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Smiles</th>\n",
       "      <th>CAS</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>O=C1c2ccccc2C(=O)c3c1ccc4c3[nH]c5c6C(=O)c7cccc...</td>\n",
       "      <td>2475-33-4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NNC(=O)CNC(=O)\\C=N\\#N</td>\n",
       "      <td>820-75-7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>O=C1NC(=O)\\C(=N/#N)\\C=N1</td>\n",
       "      <td>2435-76-9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NC(=O)CNC(=O)\\C=N\\#N</td>\n",
       "      <td>817-99-2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CCCCN(CC(O)C1=C\\C(=N/#N)\\C(=O)C=C1)N=O</td>\n",
       "      <td>116539-70-9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Smiles           CAS  Class\n",
       "0  O=C1c2ccccc2C(=O)c3c1ccc4c3[nH]c5c6C(=O)c7cccc...    2475-33-4       0\n",
       "1                             NNC(=O)CNC(=O)\\C=N\\#N      820-75-7       1\n",
       "2                          O=C1NC(=O)\\C(=N/#N)\\C=N1     2435-76-9       1\n",
       "3                              NC(=O)CNC(=O)\\C=N\\#N      817-99-2       1\n",
       "4            CCCCN(CC(O)C1=C\\C(=N/#N)\\C(=O)C=C1)N=O   116539-70-9       1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('smiles_cas_N6512.smi',delimiter='\\t',names=['Smiles','CAS','Class'])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As real-valued descriptors have been covered in the last example, this one will be using fingerprints. For scikit-learn we do need standard python/numpy collections, thus the ExplicitBitVect objects returm by RDKit have to be coverted first.\n",
    "\n",
    "Returning 'None' in the case of an error allows for simple post-filtering with pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from rdkit import Chem\n",
    "from rdkit.Chem import AllChem\n",
    "from rdkit.Chem import PandasTools\n",
    "from rdkit import DataStructs\n",
    "\n",
    "def computeMorganFP(mol, depth=2, nBits=2048):\n",
    "    a = np.zeros(nBits)\n",
    "    try:\n",
    "      DataStructs.ConvertToNumpyArray(AllChem.GetMorganFingerprintAsBitVect(mol,depth,nBits),a)\n",
    "    except:\n",
    "      return None\n",
    "    return a\n",
    "\n",
    "def computeRDKitFP(mol, maxLength=7, nBits=2048):\n",
    "    a = np.zeros(nBits)\n",
    "    try:\n",
    "      DataStructs.ConvertToNumpyArray(Chem.RDKFingerprint(mol,maxPath=maxLength, fpSize=nBits),a)\n",
    "    except:\n",
    "      return None\n",
    "    return a\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, first we have to get rid of a couple of problematic smiles:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Smiles</th>\n",
       "      <th>CAS</th>\n",
       "      <th>Class</th>\n",
       "      <th>Molecule</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NNC(=O)CNC(=O)\\C=N\\#N</td>\n",
       "      <td>820-75-7</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>O=C1NC(=O)\\C(=N/#N)\\C=N1</td>\n",
       "      <td>2435-76-9</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NC(=O)CNC(=O)\\C=N\\#N</td>\n",
       "      <td>817-99-2</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CCCCN(CC(O)C1=C\\C(=N/#N)\\C(=O)C=C1)N=O</td>\n",
       "      <td>116539-70-9</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NC(COC(=O)\\C=N/#N)C(=O)O</td>\n",
       "      <td>115-02-6</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CCN(CC(O)C1=CC(=O)\\C(=N\\#N)\\C=C1)N=O</td>\n",
       "      <td>122341-55-3</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "                                    Smiles           CAS  Class Molecule\n",
       "1                   NNC(=O)CNC(=O)\\C=N\\#N      820-75-7       1     None\n",
       "2                O=C1NC(=O)\\C(=N/#N)\\C=N1     2435-76-9       1     None\n",
       "3                    NC(=O)CNC(=O)\\C=N\\#N      817-99-2       1     None\n",
       "4  CCCCN(CC(O)C1=C\\C(=N/#N)\\C(=O)C=C1)N=O   116539-70-9       1     None\n",
       "5                NC(COC(=O)\\C=N/#N)C(=O)O      115-02-6       1     None\n",
       "6    CCN(CC(O)C1=CC(=O)\\C(=N\\#N)\\C=C1)N=O   122341-55-3       1     None"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PandasTools.AddMoleculeColumnToFrame(frame=data, smilesCol='Smiles', molCol='Molecule')\n",
    "problematicSmiles = data.ix[data['Molecule'].map(lambda x: x is None)]\n",
    "problematicSmiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data = data.ix[data['Molecule'].map(lambda x: x is not None)]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6506 compounds left for modelling. Given that we have [0,1]-class labels the \"mean\" of 0.54 corresponds to the fraction of '1's in the data. Hence the classes or fairly balanced, which is beneficial for all kinds of classification algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6506.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.537504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.498630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "             Class\n",
       "count  6506.000000\n",
       "mean      0.537504\n",
       "std       0.498630\n",
       "min       0.000000\n",
       "25%       0.000000\n",
       "50%       1.000000\n",
       "75%       1.000000\n",
       "max       1.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data['Morgan2FP'] = data['Molecule'].map(computeMorganFP)\n",
    "data['RDKit7FP'] = data['Molecule'].map(computeRDKitFP)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the sake of simplicity, we will just do one random, stratified 1:1 training/test split:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'test': array([   0,    5,    9, ..., 6499, 6500, 6501]),\n",
       "  'train': array([   1,    2,    3, ..., 6503, 6504, 6505])},\n",
       " {'test': array([   1,    2,    3, ..., 6503, 6504, 6505]),\n",
       "  'train': array([   0,    5,    9, ..., 6499, 6500, 6501])}]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cross_validation import StratifiedKFold\n",
    "cv = StratifiedKFold(data['Class'], n_folds = 2, shuffle = True, random_state=42)\n",
    "folds = []\n",
    "for trainIDs,testIDs in cv:\n",
    "  folds.append({'train':trainIDs,'test':testIDs})\n",
    "folds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "trainSet = data.iloc[folds[0]['train']]\n",
    "testSet = data.iloc[folds[0]['test']]\n",
    "\n",
    "#double check that training and test set are disjoint\n",
    "s1 = set(trainSet['Smiles'])\n",
    "s2 = set(testSet['Smiles'])\n",
    "assert len(s1.intersection(s2)) == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def confusion_matrix_summary(acts,preds):\n",
    "    '''Simple classification evaluation based on discrete, exact class comparison'''\n",
    "    from io import StringIO\n",
    "    file_str = StringIO()\n",
    "    vTab=metrics.confusion_matrix(acts,preds)\n",
    "    #print vTab\n",
    "    nResultCodes=len(vTab)\n",
    "    file_str.write('\\n\\tResults Table (experiment in rows):\\n')\n",
    "    colCounts = numpy.sum(vTab,0)\n",
    "    rowCounts = numpy.sum(vTab,1)\n",
    "    print\n",
    "    for i in range(nResultCodes):\n",
    "        if rowCounts[i]==0: rowCounts[i]=1\n",
    "        row = vTab[i]\n",
    "        file_str.write('    ')\n",
    "        for j in range(nResultCodes):\n",
    "            entry = row[j]\n",
    "            file_str.write(' % 6d'%entry),\n",
    "        file_str.write('     | % 4.2f\\n'%(100.*vTab[i,i]/rowCounts[i]))\n",
    "    file_str.write('    ')\n",
    "    for i in range(nResultCodes):\n",
    "        file_str.write('-------')\n",
    "    file_str.write('\\n')\n",
    "    file_str.write('    '),\n",
    "    for i in range(nResultCodes):\n",
    "        if colCounts[i]==0: colCounts[i]=1\n",
    "        file_str.write(' % 6.2f'%(100.*vTab[i,i]/colCounts[i])),\n",
    "    file_str.write('\\n')\n",
    "    return file_str.getvalue()\n",
    "\n",
    "def createROCPlot(pl,observations,probabilities,caption):\n",
    "    '''Create an ROC curve'''\n",
    "    fpr, tpr, thresholds = metrics.roc_curve(observations, probabilities)\n",
    "    roc_auc = metrics.auc(fpr, tpr)\n",
    "    print(\"Area under the ROC curve : \",roc_auc)\n",
    "    pl.plot(fpr, tpr, label='ROC curve (area = {0})'.format(roc_auc))\n",
    "    pl.plot([0, 1], [0, 1], 'k--')\n",
    "    pl.set_xlim([0.0, 1.0])\n",
    "    pl.set_ylim([0.0, 1.0])\n",
    "    pl.set_xlabel('False Positive Rate')\n",
    "    pl.set_ylabel('True Positive Rate')\n",
    "    pl.set_title(caption)\n",
    "    \n",
    "def createPRPlot(pl,observations,probabilities,caption):\n",
    "    '''Create a Precision/Recall Curve'''\n",
    "    prec, rec, thresholds = metrics.precision_recall_curve(observations, probabilities)\n",
    "    accuracies = []\n",
    "    pl.plot(prec, rec, label='Recision/Recall')\n",
    "    pl.plot([0, 1], [0, 1], 'k--')\n",
    "    pl.set_xlim([0.0, 1.0])\n",
    "    pl.set_ylim([0.0, 1.0])\n",
    "    pl.set_xlabel('Precision')\n",
    "    pl.set_ylabel('Recall')\n",
    "    pl.set_title(caption)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the first example, a simple Bernoulli Naive Bayes model is created and evaluated regarding its categorical prediction accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Morgan Fingerprints\n",
      "\n",
      "\tResults Table (experiment in rows):\n",
      "       1194    311     |  79.34\n",
      "        571   1178     |  67.35\n",
      "    --------------\n",
      "      67.65  79.11\n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.68      0.79      0.73      1505\n",
      "          1       0.79      0.67      0.73      1749\n",
      "\n",
      "avg / total       0.74      0.73      0.73      3254\n",
      "\n",
      "------------------------------------------------------------\n",
      "RDKit Fingerprints\n",
      "\n",
      "\tResults Table (experiment in rows):\n",
      "       1015    490     |  67.44\n",
      "        790    959     |  54.83\n",
      "    --------------\n",
      "      56.23  66.18\n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.56      0.67      0.61      1505\n",
      "          1       0.66      0.55      0.60      1749\n",
      "\n",
      "avg / total       0.62      0.61      0.61      3254\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import BernoulliNB\n",
    "from sklearn import metrics\n",
    "morgan_bnb = BernoulliNB(fit_prior=False)\n",
    "morgan_bnb.fit(list(trainSet['Morgan2FP']),trainSet['Class'])\n",
    "morgan_predictions = morgan_bnb.predict(list(testSet['Morgan2FP']))\n",
    "print('Morgan Fingerprints')\n",
    "print(confusion_matrix_summary(testSet['Class'],morgan_predictions))\n",
    "print( metrics.classification_report(testSet['Class'],morgan_predictions))\n",
    "\n",
    "\n",
    "print('------------------------------------------------------------')\n",
    "\n",
    "rd_bnb = BernoulliNB(fit_prior=False)\n",
    "rd_bnb.fit(list(trainSet['RDKit7FP']),trainSet['Class'])\n",
    "rd_predictions = rd_bnb.predict(list(testSet['RDKit7FP']))\n",
    "print('RDKit Fingerprints')\n",
    "print(confusion_matrix_summary(testSet['Class'],rd_predictions))\n",
    "print( metrics.classification_report(testSet['Class'],rd_predictions))\n",
    "\n",
    "\n",
    "morgan_probabilities = [p[1] for p in morgan_bnb.predict_proba(list(testSet['Morgan2FP']))]\n",
    "rd_probabilities = [p[1] for p in rd_bnb.predict_proba(list(testSet['RDKit7FP']))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The simple classification evaluation assumes that a sample is assigned to either one or the other class, ignoring any degree of uncertainty. However, in many cases there are actually quantitative estimates used for this decision (e.g. class probabilities) and the predicted class is simply the one with the largest score.\n",
    "\n",
    "While in the current, fairly balanced example this approach might lead to reasonable results, in other scenarios a more detailed evaluation of the quantitative estimates might make sense. This is especially true if either the classes themself are unbalanced, or the risk of a misclassification is assymetrical.\n",
    "\n",
    "In such situations, ROC or Precision/Recall curves are useful tools to get a more detailed evaluation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Area under the ROC curve :  0.803087478559\n",
      "Area under the ROC curve :  0.624926061214\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA+wAAAIHCAYAAAAFLdZ9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1249b0b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA+wAAAIHCAYAAAAFLdZ9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nABgSdgAAABgSdgAAABgKy+KyCK/la+IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13c2c6438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure,(ax1,ax2) = pyplot.subplots(1,2)\n",
    "figure.set_size_inches(10,5)\n",
    "figure.tight_layout()\n",
    "\n",
    "createROCPlot(ax1,testSet['Class'],morgan_probabilities,'Morgan FP')\n",
    "createROCPlot(ax2,testSet['Class'],rd_probabilities,'RDKit FP')\n",
    "\n",
    "figure,(ax1,ax2) = pyplot.subplots(1,2)\n",
    "figure.set_size_inches(10,5)\n",
    "figure.tight_layout()\n",
    "\n",
    "createPRPlot(ax1,testSet['Class'],morgan_probabilities,'Morgan FP')\n",
    "createPRPlot(ax2,testSet['Class'],rd_probabilities,'RDKit FP')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As with regression, there are many different machine learning algorithms available for classification as well. One of the most versatile and robust approach is a Random Forest. However, it has a significant number of parameters and is also quite sensitive to different parameterization. Consequently, wrapping this in an automated grid search makes sense."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 108 candidates, totalling 1080 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  49 tasks       | elapsed:   26.8s\n",
      "[Parallel(n_jobs=1)]: Done 199 tasks       | elapsed:  2.0min\n",
      "[Parallel(n_jobs=1)]: Done 449 tasks       | elapsed:  5.3min\n",
      "[Parallel(n_jobs=1)]: Done 799 tasks       | elapsed: 11.7min\n",
      "[Parallel(n_jobs=1)]: Done 1080 out of 1080 | elapsed: 18.3min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: %0.2f 0.765682656827\n",
      "Training set performance using best parameters (%s) {'min_samples_split': 2, 'max_depth': 20, 'min_samples_leaf': 1, 'n_estimators': 200}\n",
      "Morgan Fingerprints\n",
      "\n",
      "\tResults Table (experiment in rows):\n",
      "       1208    297     |  80.27\n",
      "        454   1295     |  74.04\n",
      "    --------------\n",
      "      72.68  81.34\n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.73      0.80      0.76      1505\n",
      "          1       0.81      0.74      0.78      1749\n",
      "\n",
      "avg / total       0.77      0.77      0.77      3254\n",
      "\n",
      "------------------------------------------------------------\n",
      "Fitting 10 folds for each of 108 candidates, totalling 1080 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  49 tasks       | elapsed:   30.0s\n",
      "[Parallel(n_jobs=1)]: Done 199 tasks       | elapsed:  2.3min\n",
      "[Parallel(n_jobs=1)]: Done 449 tasks       | elapsed:  5.9min\n",
      "[Parallel(n_jobs=1)]: Done 799 tasks       | elapsed: 12.3min\n",
      "[Parallel(n_jobs=1)]: Done 1080 out of 1080 | elapsed: 17.9min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: %0.2f 0.774600246002\n",
      "Training set performance using best parameters (%s) {'min_samples_split': 8, 'max_depth': 20, 'min_samples_leaf': 1, 'n_estimators': 200}\n",
      "RDKit Fingerprints\n",
      "\n",
      "\tResults Table (experiment in rows):\n",
      "       1209    296     |  80.33\n",
      "        420   1329     |  75.99\n",
      "    --------------\n",
      "      74.22  81.78\n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.74      0.80      0.77      1505\n",
      "          1       0.82      0.76      0.79      1749\n",
      "\n",
      "avg / total       0.78      0.78      0.78      3254\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import KFold\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "params = {'max_depth':[5,10,20],'min_samples_split':[2,8,32],'min_samples_leaf':[1,2,5,10],'n_estimators':[50,100,200]}\n",
    "cv = KFold(n=len(trainSet),n_folds=10,shuffle=True)\n",
    "gs = GridSearchCV(RandomForestClassifier(), params, cv=cv,verbose=1,refit=True)\n",
    "gs.fit(list(trainSet['Morgan2FP']), trainSet['Class'])\n",
    "\n",
    "print('Best score: %0.2f',gs.best_score_)\n",
    "print('Training set performance using best parameters (%s)', gs.best_params_)\n",
    "best_morgan_treemodel = gs.best_estimator_\n",
    "#training set evaluation\n",
    "best_morgan_tree_prediction = best_morgan_treemodel.predict(list(testSet['Morgan2FP']))\n",
    "print('Morgan Fingerprints')\n",
    "print(confusion_matrix_summary(testSet['Class'],best_morgan_tree_prediction))\n",
    "print( metrics.classification_report(testSet['Class'],best_morgan_tree_prediction))\n",
    "\n",
    "\n",
    "print('------------------------------------------------------------')\n",
    "gs.fit(list(trainSet['RDKit7FP']), trainSet['Class'])\n",
    "print('Best score: %0.2f',gs.best_score_)\n",
    "print('Training set performance using best parameters (%s)', gs.best_params_)\n",
    "best_rd_treemodel = gs.best_estimator_\n",
    "#training set evaluation\n",
    "best_rd_tree_prediction = best_rd_treemodel.predict(list(testSet['RDKit7FP']))\n",
    "print('RDKit Fingerprints')\n",
    "print(confusion_matrix_summary(testSet['Class'],best_rd_tree_prediction))\n",
    "print( metrics.classification_report(testSet['Class'],best_rd_tree_prediction))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "            max_depth=20, max_features='auto', max_leaf_nodes=None,\n",
      "            min_samples_leaf=1, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, n_estimators=200, n_jobs=1,\n",
      "            oob_score=False, random_state=None, verbose=0,\n",
      "            warm_start=False)\n",
      "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "            max_depth=20, max_features='auto', max_leaf_nodes=None,\n",
      "            min_samples_leaf=1, min_samples_split=8,\n",
      "            min_weight_fraction_leaf=0.0, n_estimators=200, n_jobs=1,\n",
      "            oob_score=False, random_state=None, verbose=0,\n",
      "            warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "print(best_morgan_treemodel)\n",
    "print(best_rd_treemodel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interesting, while with Naive Bayes the Morgan2 fingerprint gave - probably significantly - better models, the Random Forest does seem to be able to use the information from both fingerprints comparably well and better than Naive Bayes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Area under the ROC curve :  0.848932945072\n",
      "Area under the ROC curve :  0.857312294258\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA+wAAAIHCAYAAAAFLdZ9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noh43i6pzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13c36a278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA+wAAAIHCAYAAAAFLdZ9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nzgYJFIYSVwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13f921128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_morgan_probabilities = [p[1] for p in best_morgan_treemodel.predict_proba(list(testSet['Morgan2FP']))]\n",
    "rf_rd_probabilities = [p[1] for p in best_rd_treemodel.predict_proba(list(testSet['RDKit7FP']))]\n",
    "\n",
    "figure,(ax1,ax2) = pyplot.subplots(1,2)\n",
    "figure.set_size_inches(10,5)\n",
    "figure.tight_layout()\n",
    "\n",
    "createROCPlot(ax1,testSet['Class'],rf_morgan_probabilities,'Morgan FP')\n",
    "createROCPlot(ax2,testSet['Class'],rf_rd_probabilities,'RDKit FP')\n",
    "\n",
    "figure,(ax1,ax2) = pyplot.subplots(1,2)\n",
    "figure.set_size_inches(10,5)\n",
    "figure.tight_layout()\n",
    "\n",
    "createPRPlot(ax1,testSet['Class'],rf_morgan_probabilities,'Morgan FP')\n",
    "createPRPlot(ax2,testSet['Class'],rf_rd_probabilities,'RDKit FP')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Similarity Map visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Riniker, S. & Landrum, G. A. \"Similarity maps - a visualization strategy for molecular fingerprints and machine-learning methods.\" Journal of Cheminformatics 5:43 (2013), DOI: http://dx.doi.org/10.1186/1758-2946-5-43"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarity maps offer a way to visualize which atoms of a molecule contributed in what way to the prediction by a model. Basically, this is achieved by creating a modified feature vector (in this case the binary fingerprint) as it would look like if a specified atom would not be present. Then, the quantitative change of the model prediction between the original fingerprint and the modified fingerprint is computed and regarded as the contribution of the toggled atom to the prediction.\n",
    "\n",
    "Some things that should be kept in mind: \n",
    "1. It is a local, atom-wise prediction contribution that is restricted to what kind of effects that atom can have on the features. One important implication is that structural contributions (functional groups, scaffolds,...) can only be recognised if these are present in the fingerprint patterns. This is especially relevant for long-range spatial arrangements.\n",
    "\n",
    "2. A positive atom-contribution means that removing the atom would give a lower prediction, which means, in the case of probabilities, a lower probability of the examined class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from rdkit.Chem.Draw import SimilarityMaps, rdMolDraw2D\n",
    "from IPython.display import HTML, display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Smiles</th>\n",
       "      <th>CAS</th>\n",
       "      <th>Class</th>\n",
       "      <th>Molecule</th>\n",
       "      <th>Morgan2FP</th>\n",
       "      <th>RDKit7FP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1779</th>\n",
       "      <td>COc1cc(C(O)c2ncc([N+](=O)[O-])n2C)c(O)c(c1)C(C)(C)C</td>\n",
       "      <td>149396-88-3</td>\n",
       "      <td>1</td>\n",
       "      <td><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></td>\n",
       "      <td>[0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...]</td>\n",
       "      <td>[1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, ...]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h3>Bernoulli Naive Bayes</h3>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><hr><h4>Morgan2 Fingerprint: CAS 149396-88-3 </h4><?xml version='1.0' encoding='iso-8859-1'?>\n",
       "<svg version='1.1' baseProfile='full'\n",
       "              xmlns:svg='http://www.w3.org/2000/svg'\n",
       "                      xmlns:rdkit='http://www.rdkit.org/xml'\n",
       "                      xmlns:xlink='http://www.w3.org/1999/xlink'\n",
       "                  xml:space='preserve'\n",
       "width='300px' height='300px' >\n",
       "<rect style='opacity:1.0;fill:#FFFFFF;stroke:none' width='300' height='300' x='0' y='0'> </rect>\n",
       "<ellipse cx='139.93' cy='221.435' rx='10.5324' ry='10.5324' style='fill:#F7F5F6;fill-rule:evenodd;stroke:#F7F5F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='171.071' cy='216.083' rx='10.5324' ry='10.5324' style='fill:#F8F2F5;fill-rule:evenodd;stroke:#F8F2F5;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='182.006' cy='186.439' rx='10.5324' ry='10.5324' style='fill:#F7F6F6;fill-rule:evenodd;stroke:#F7F6F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='161.801' cy='162.146' rx='10.5324' ry='10.5324' style='fill:#F7F5F6;fill-rule:evenodd;stroke:#F7F5F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='172.737' cy='132.502' rx='10.5324' ry='10.5324' style='fill:#F7F4F6;fill-rule:evenodd;stroke:#F7F4F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='152.531' cy='108.209' rx='10.5324' ry='10.5324' style='fill:#FDE0EF;fill-rule:evenodd;stroke:#FDE0EF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='163.467' cy='78.5646' rx='10.5324' ry='10.5324' style='fill:#F7F4F6;fill-rule:evenodd;stroke:#F7F4F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='121.391' cy='113.561' rx='10.5324' ry='10.5324' style='fill:#F9EFF4;fill-rule:evenodd;stroke:#F9EFF4;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='98.7569' cy='91.5135' rx='10.5324' ry='10.5324' style='fill:#FCE3F0;fill-rule:evenodd;stroke:#FCE3F0;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='70.7942' cy='106.226' rx='10.5324' ry='10.5324' style='fill:#F9EFF4;fill-rule:evenodd;stroke:#F9EFF4;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='76.1461' cy='137.367' rx='10.5324' ry='10.5324' style='fill:#F7F5F6;fill-rule:evenodd;stroke:#F7F5F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='54.0985' cy='160.001' rx='10.5324' ry='10.5324' style='fill:#F6F6F4;fill-rule:evenodd;stroke:#F6F6F4;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='23.4733' cy='152.224' rx='10.5324' ry='10.5324' style='fill:#F6F6F4;fill-rule:evenodd;stroke:#F6F6F4;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='62.6762' cy='190.411' rx='10.5324' ry='10.5324' style='fill:#F6F6F4;fill-rule:evenodd;stroke:#F6F6F4;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='107.416' cy='141.9' rx='10.5324' ry='10.5324' style='fill:#FBE7F1;fill-rule:evenodd;stroke:#FBE7F1;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='122.129' cy='169.863' rx='10.5324' ry='10.5324' style='fill:#F8F0F4;fill-rule:evenodd;stroke:#F8F0F4;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='203.877' cy='127.15' rx='10.5324' ry='10.5324' style='fill:#FBE7F1;fill-rule:evenodd;stroke:#FBE7F1;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='214.813' cy='97.5054' rx='10.5324' ry='10.5324' style='fill:#F7F5F6;fill-rule:evenodd;stroke:#F7F5F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='224.082' cy='151.443' rx='10.5324' ry='10.5324' style='fill:#F9ECF3;fill-rule:evenodd;stroke:#F9ECF3;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='213.147' cy='181.087' rx='10.5324' ry='10.5324' style='fill:#F9EDF3;fill-rule:evenodd;stroke:#F9EDF3;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='255.223' cy='146.091' rx='10.5324' ry='10.5324' style='fill:#E48DBD;fill-rule:evenodd;stroke:#E48DBD;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='260.575' cy='177.231' rx='10.5324' ry='10.5324' style='fill:#FAEAF2;fill-rule:evenodd;stroke:#FAEAF2;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='249.871' cy='114.95' rx='10.5324' ry='10.5324' style='fill:#FAEAF2;fill-rule:evenodd;stroke:#FAEAF2;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='286.364' cy='140.739' rx='10.5324' ry='10.5324' style='fill:#FAEAF2;fill-rule:evenodd;stroke:#FAEAF2;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 139.93,221.435 153.041,219.182' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 153.041,219.182 166.152,216.929' style='fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 173.013,210.817 177.51,198.628' style='fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 177.51,198.628 182.006,186.439' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 182.006,186.439 161.801,162.146' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 183.834,178.754 169.69,161.749' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 182.006,186.439 213.147,181.087' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 161.801,162.146 172.737,132.502' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 172.737,132.502 152.531,108.209' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 172.737,132.502 203.877,127.15' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 178.478,137.927 200.276,134.181' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 152.531,108.209 157.028,96.02' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 157.028,96.02 161.524,83.8308' style='fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 152.531,108.209 121.391,113.561' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 121.391,113.561 112.356,104.76' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 112.356,104.76 103.321,95.9597' style='fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 114.271,115.448 107.947,109.287' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 107.947,109.287 101.622,103.127' style='fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 121.391,113.561 115.702,125.097' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 115.702,125.097 110.013,136.634' style='fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 94.1924,93.9151 82.4933,100.071' style='fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 82.4933,100.071 70.7942,106.226' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 70.7942,106.226 76.1461,137.367' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 77.8251,109.827 81.5714,131.626' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 76.1461,137.367 67.9437,145.788' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 67.9437,145.788 59.7413,154.208' style='fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 76.1461,137.367 89.499,139.303' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 89.499,139.303 102.852,141.238' style='fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 48.4658,155.31 38.8176,152.86' style='fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 38.8176,152.86 29.1695,150.41' style='fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 46.9104,161.436 37.2623,158.986' style='fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 37.2623,158.986 27.6141,156.536' style='fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 55.7325,165.794 58.3874,175.206' style='fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 58.3874,175.206 61.0423,184.619' style='fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 110.187,147.166 116.158,158.515' style='fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 116.158,158.515 122.129,169.863' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 203.877,127.15 208.374,114.961' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 208.374,114.961 212.87,102.772' style='fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 203.877,127.15 224.082,151.443' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 224.082,151.443 213.147,181.087' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 216.513,153.702 208.858,174.453' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 224.082,151.443 255.223,146.091' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 255.223,146.091 260.575,177.231' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 255.223,146.091 249.871,114.95' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 255.223,146.091 286.364,140.739' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<text x='166.152' y='221.35' style='font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000' ><tspan>O</tspan></text>\n",
       "<text x='153.984' y='83.8308' style='font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000' ><tspan>OH</tspan></text>\n",
       "<text x='94.1924' y='96.7797' style='font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#0000FF' ><tspan>N</tspan></text>\n",
       "<text x='47.6881' y='165.794' style='font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#0000FF' ><tspan>N</tspan><tspan style='baseline-shift:super;font-size:7.5px;'>+</tspan><tspan></tspan></text>\n",
       "<text x='18.5549' y='157.49' style='font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000' ><tspan>O</tspan></text>\n",
       "<text x='56.7051' y='196.204' style='font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000' ><tspan>O</tspan><tspan style='baseline-shift:super;font-size:7.5px;'>-</tspan><tspan></tspan></text>\n",
       "<text x='102.852' y='147.166' style='font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#0000FF' ><tspan>N</tspan></text>\n",
       "<text x='205.33' y='102.772' style='font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000' ><tspan>OH</tspan></text>\n",
       "<text x='-10.1489' y='288' style='font-size:12px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000' ><tspan>Class: 1, Prob(Class 1): 0.0001751968501903336</tspan></text>\n",
       "</svg>\n",
       "</html>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><hr><h4>RDKit Fingerprint: CAS 149396-88-3 </h4><?xml version='1.0' encoding='iso-8859-1'?>\n",
       "<svg version='1.1' baseProfile='full'\n",
       "              xmlns:svg='http://www.w3.org/2000/svg'\n",
       "                      xmlns:rdkit='http://www.rdkit.org/xml'\n",
       "                      xmlns:xlink='http://www.w3.org/1999/xlink'\n",
       "                  xml:space='preserve'\n",
       "width='300px' height='300px' >\n",
       "<rect style='opacity:1.0;fill:#FFFFFF;stroke:none' width='300' height='300' x='0' y='0'> </rect>\n",
       "<ellipse cx='139.93' cy='221.435' rx='10.5324' ry='10.5324' style='fill:#F6F6F6;fill-rule:evenodd;stroke:#F6F6F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='171.071' cy='216.083' rx='10.5324' ry='10.5324' style='fill:#F6F6F6;fill-rule:evenodd;stroke:#F6F6F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='182.006' cy='186.439' rx='10.5324' ry='10.5324' style='fill:#F6F6F6;fill-rule:evenodd;stroke:#F6F6F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='161.801' cy='162.146' rx='10.5324' ry='10.5324' style='fill:#F6F6F6;fill-rule:evenodd;stroke:#F6F6F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='172.737' cy='132.502' rx='10.5324' ry='10.5324' style='fill:#276419;fill-rule:evenodd;stroke:#276419;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='152.531' cy='108.209' rx='10.5324' ry='10.5324' style='fill:#276419;fill-rule:evenodd;stroke:#276419;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='163.467' cy='78.5646' rx='10.5324' ry='10.5324' style='fill:#F6F6F6;fill-rule:evenodd;stroke:#F6F6F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='121.391' cy='113.561' rx='10.5324' ry='10.5324' style='fill:#276419;fill-rule:evenodd;stroke:#276419;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='98.7569' cy='91.5135' rx='10.5324' ry='10.5324' style='fill:#D7EEB8;fill-rule:evenodd;stroke:#D7EEB8;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='70.7942' cy='106.226' rx='10.5324' ry='10.5324' style='fill:#F6F6F6;fill-rule:evenodd;stroke:#F6F6F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='76.1461' cy='137.367' rx='10.5324' ry='10.5324' style='fill:#276419;fill-rule:evenodd;stroke:#276419;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='54.0985' cy='160.001' rx='10.5324' ry='10.5324' style='fill:#F6F6F6;fill-rule:evenodd;stroke:#F6F6F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='23.4733' cy='152.224' rx='10.5324' ry='10.5324' style='fill:#F6F6F6;fill-rule:evenodd;stroke:#F6F6F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='62.6762' cy='190.411' rx='10.5324' ry='10.5324' style='fill:#F6F6F6;fill-rule:evenodd;stroke:#F6F6F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='107.416' cy='141.9' rx='10.5324' ry='10.5324' style='fill:#276419;fill-rule:evenodd;stroke:#276419;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='122.129' cy='169.863' rx='10.5324' ry='10.5324' style='fill:#F6F6F6;fill-rule:evenodd;stroke:#F6F6F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='203.877' cy='127.15' rx='10.5324' ry='10.5324' style='fill:#85C049;fill-rule:evenodd;stroke:#85C049;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='214.813' cy='97.5054' rx='10.5324' ry='10.5324' style='fill:#F6F6F6;fill-rule:evenodd;stroke:#F6F6F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='224.082' cy='151.443' rx='10.5324' ry='10.5324' style='fill:#F6F6F6;fill-rule:evenodd;stroke:#F6F6F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='213.147' cy='181.087' rx='10.5324' ry='10.5324' style='fill:#F6F6F6;fill-rule:evenodd;stroke:#F6F6F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='255.223' cy='146.091' rx='10.5324' ry='10.5324' style='fill:#F6F6F6;fill-rule:evenodd;stroke:#F6F6F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='260.575' cy='177.231' rx='10.5324' ry='10.5324' style='fill:#F6F6F6;fill-rule:evenodd;stroke:#F6F6F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='249.871' cy='114.95' rx='10.5324' ry='10.5324' style='fill:#F6F6F6;fill-rule:evenodd;stroke:#F6F6F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='286.364' cy='140.739' rx='10.5324' ry='10.5324' style='fill:#F6F6F6;fill-rule:evenodd;stroke:#F6F6F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 139.93,221.435 153.041,219.182' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 153.041,219.182 166.152,216.929' style='fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 173.013,210.817 177.51,198.628' style='fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 177.51,198.628 182.006,186.439' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 182.006,186.439 161.801,162.146' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 183.834,178.754 169.69,161.749' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 182.006,186.439 213.147,181.087' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 161.801,162.146 172.737,132.502' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 172.737,132.502 152.531,108.209' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 172.737,132.502 203.877,127.15' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 178.478,137.927 200.276,134.181' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 152.531,108.209 157.028,96.02' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 157.028,96.02 161.524,83.8308' style='fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 152.531,108.209 121.391,113.561' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 121.391,113.561 112.356,104.76' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 112.356,104.76 103.321,95.9597' style='fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 114.271,115.448 107.947,109.287' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 107.947,109.287 101.622,103.127' style='fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 121.391,113.561 115.702,125.097' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 115.702,125.097 110.013,136.634' style='fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 94.1924,93.9151 82.4933,100.071' style='fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 82.4933,100.071 70.7942,106.226' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 70.7942,106.226 76.1461,137.367' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 77.8251,109.827 81.5714,131.626' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 76.1461,137.367 67.9437,145.788' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 67.9437,145.788 59.7413,154.208' style='fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 76.1461,137.367 89.499,139.303' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 89.499,139.303 102.852,141.238' style='fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 48.4658,155.31 38.8176,152.86' style='fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 38.8176,152.86 29.1695,150.41' style='fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 46.9104,161.436 37.2623,158.986' style='fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 37.2623,158.986 27.6141,156.536' style='fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 55.7325,165.794 58.3874,175.206' style='fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 58.3874,175.206 61.0423,184.619' style='fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 110.187,147.166 116.158,158.515' style='fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 116.158,158.515 122.129,169.863' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 203.877,127.15 208.374,114.961' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 208.374,114.961 212.87,102.772' style='fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 203.877,127.15 224.082,151.443' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 224.082,151.443 213.147,181.087' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 216.513,153.702 208.858,174.453' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 224.082,151.443 255.223,146.091' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 255.223,146.091 260.575,177.231' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 255.223,146.091 249.871,114.95' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 255.223,146.091 286.364,140.739' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<text x='166.152' y='221.35' style='font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000' ><tspan>O</tspan></text>\n",
       "<text x='153.984' y='83.8308' style='font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000' ><tspan>OH</tspan></text>\n",
       "<text x='94.1924' y='96.7797' style='font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#0000FF' ><tspan>N</tspan></text>\n",
       "<text x='47.6881' y='165.794' style='font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#0000FF' ><tspan>N</tspan><tspan style='baseline-shift:super;font-size:7.5px;'>+</tspan><tspan></tspan></text>\n",
       "<text x='18.5549' y='157.49' style='font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000' ><tspan>O</tspan></text>\n",
       "<text x='56.7051' y='196.204' style='font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000' ><tspan>O</tspan><tspan style='baseline-shift:super;font-size:7.5px;'>-</tspan><tspan></tspan></text>\n",
       "<text x='102.852' y='147.166' style='font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#0000FF' ><tspan>N</tspan></text>\n",
       "<text x='205.33' y='102.772' style='font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000' ><tspan>OH</tspan></text>\n",
       "<text x='61.9376' y='288' style='font-size:12px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000' ><tspan>Class: 1, Prob(Class 1): 1.0</tspan></text>\n",
       "</svg>\n",
       "</html>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h3>Optimized Random Forest</h3>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><hr><h4>Morgan2 Fingerprint: CAS 149396-88-3 </h4><?xml version='1.0' encoding='iso-8859-1'?>\n",
       "<svg version='1.1' baseProfile='full'\n",
       "              xmlns:svg='http://www.w3.org/2000/svg'\n",
       "                      xmlns:rdkit='http://www.rdkit.org/xml'\n",
       "                      xmlns:xlink='http://www.w3.org/1999/xlink'\n",
       "                  xml:space='preserve'\n",
       "width='300px' height='300px' >\n",
       "<rect style='opacity:1.0;fill:#FFFFFF;stroke:none' width='300' height='300' x='0' y='0'> </rect>\n",
       "<ellipse cx='139.93' cy='221.435' rx='10.5324' ry='10.5324' style='fill:#F7F5F6;fill-rule:evenodd;stroke:#F7F5F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='171.071' cy='216.083' rx='10.5324' ry='10.5324' style='fill:#F8F2F5;fill-rule:evenodd;stroke:#F8F2F5;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='182.006' cy='186.439' rx='10.5324' ry='10.5324' style='fill:#F7F6F6;fill-rule:evenodd;stroke:#F7F6F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='161.801' cy='162.146' rx='10.5324' ry='10.5324' style='fill:#F7F5F6;fill-rule:evenodd;stroke:#F7F5F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='172.737' cy='132.502' rx='10.5324' ry='10.5324' style='fill:#F7F4F6;fill-rule:evenodd;stroke:#F7F4F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='152.531' cy='108.209' rx='10.5324' ry='10.5324' style='fill:#FDE0EF;fill-rule:evenodd;stroke:#FDE0EF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='163.467' cy='78.5646' rx='10.5324' ry='10.5324' style='fill:#F7F4F6;fill-rule:evenodd;stroke:#F7F4F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='121.391' cy='113.561' rx='10.5324' ry='10.5324' style='fill:#F9EFF4;fill-rule:evenodd;stroke:#F9EFF4;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='98.7569' cy='91.5135' rx='10.5324' ry='10.5324' style='fill:#FCE3F0;fill-rule:evenodd;stroke:#FCE3F0;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='70.7942' cy='106.226' rx='10.5324' ry='10.5324' style='fill:#F9EFF4;fill-rule:evenodd;stroke:#F9EFF4;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='76.1461' cy='137.367' rx='10.5324' ry='10.5324' style='fill:#F7F5F6;fill-rule:evenodd;stroke:#F7F5F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='54.0985' cy='160.001' rx='10.5324' ry='10.5324' style='fill:#F6F6F4;fill-rule:evenodd;stroke:#F6F6F4;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='23.4733' cy='152.224' rx='10.5324' ry='10.5324' style='fill:#F6F6F4;fill-rule:evenodd;stroke:#F6F6F4;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='62.6762' cy='190.411' rx='10.5324' ry='10.5324' style='fill:#F6F6F4;fill-rule:evenodd;stroke:#F6F6F4;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='107.416' cy='141.9' rx='10.5324' ry='10.5324' style='fill:#FBE7F1;fill-rule:evenodd;stroke:#FBE7F1;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='122.129' cy='169.863' rx='10.5324' ry='10.5324' style='fill:#F8F0F4;fill-rule:evenodd;stroke:#F8F0F4;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='203.877' cy='127.15' rx='10.5324' ry='10.5324' style='fill:#FBE7F1;fill-rule:evenodd;stroke:#FBE7F1;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='214.813' cy='97.5054' rx='10.5324' ry='10.5324' style='fill:#F7F5F6;fill-rule:evenodd;stroke:#F7F5F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='224.082' cy='151.443' rx='10.5324' ry='10.5324' style='fill:#F9ECF3;fill-rule:evenodd;stroke:#F9ECF3;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='213.147' cy='181.087' rx='10.5324' ry='10.5324' style='fill:#F9EDF3;fill-rule:evenodd;stroke:#F9EDF3;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='255.223' cy='146.091' rx='10.5324' ry='10.5324' style='fill:#E48DBD;fill-rule:evenodd;stroke:#E48DBD;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='260.575' cy='177.231' rx='10.5324' ry='10.5324' style='fill:#FAEAF2;fill-rule:evenodd;stroke:#FAEAF2;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='249.871' cy='114.95' rx='10.5324' ry='10.5324' style='fill:#FAEAF2;fill-rule:evenodd;stroke:#FAEAF2;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='286.364' cy='140.739' rx='10.5324' ry='10.5324' style='fill:#FAEAF2;fill-rule:evenodd;stroke:#FAEAF2;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 139.93,221.435 153.041,219.182' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 153.041,219.182 166.152,216.929' style='fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 173.013,210.817 177.51,198.628' style='fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 177.51,198.628 182.006,186.439' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 182.006,186.439 161.801,162.146' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 183.834,178.754 169.69,161.749' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 182.006,186.439 213.147,181.087' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 161.801,162.146 172.737,132.502' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 172.737,132.502 152.531,108.209' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 172.737,132.502 203.877,127.15' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 178.478,137.927 200.276,134.181' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 152.531,108.209 157.028,96.02' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 157.028,96.02 161.524,83.8308' style='fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 152.531,108.209 121.391,113.561' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 121.391,113.561 112.356,104.76' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 112.356,104.76 103.321,95.9597' style='fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 114.271,115.448 107.947,109.287' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 107.947,109.287 101.622,103.127' style='fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 121.391,113.561 115.702,125.097' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 115.702,125.097 110.013,136.634' style='fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 94.1924,93.9151 82.4933,100.071' style='fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 82.4933,100.071 70.7942,106.226' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 70.7942,106.226 76.1461,137.367' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 77.8251,109.827 81.5714,131.626' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 76.1461,137.367 67.9437,145.788' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 67.9437,145.788 59.7413,154.208' style='fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 76.1461,137.367 89.499,139.303' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 89.499,139.303 102.852,141.238' style='fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 48.4658,155.31 38.8176,152.86' style='fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 38.8176,152.86 29.1695,150.41' style='fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 46.9104,161.436 37.2623,158.986' style='fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 37.2623,158.986 27.6141,156.536' style='fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 55.7325,165.794 58.3874,175.206' style='fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 58.3874,175.206 61.0423,184.619' style='fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 110.187,147.166 116.158,158.515' style='fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 116.158,158.515 122.129,169.863' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 203.877,127.15 208.374,114.961' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 208.374,114.961 212.87,102.772' style='fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 203.877,127.15 224.082,151.443' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 224.082,151.443 213.147,181.087' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 216.513,153.702 208.858,174.453' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 224.082,151.443 255.223,146.091' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 255.223,146.091 260.575,177.231' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 255.223,146.091 249.871,114.95' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 255.223,146.091 286.364,140.739' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<text x='166.152' y='221.35' style='font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000' ><tspan>O</tspan></text>\n",
       "<text x='153.984' y='83.8308' style='font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000' ><tspan>OH</tspan></text>\n",
       "<text x='94.1924' y='96.7797' style='font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#0000FF' ><tspan>N</tspan></text>\n",
       "<text x='47.6881' y='165.794' style='font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#0000FF' ><tspan>N</tspan><tspan style='baseline-shift:super;font-size:7.5px;'>+</tspan><tspan></tspan></text>\n",
       "<text x='18.5549' y='157.49' style='font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000' ><tspan>O</tspan></text>\n",
       "<text x='56.7051' y='196.204' style='font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000' ><tspan>O</tspan><tspan style='baseline-shift:super;font-size:7.5px;'>-</tspan><tspan></tspan></text>\n",
       "<text x='102.852' y='147.166' style='font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#0000FF' ><tspan>N</tspan></text>\n",
       "<text x='205.33' y='102.772' style='font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000' ><tspan>OH</tspan></text>\n",
       "<text x='1.86555' y='288' style='font-size:12px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000' ><tspan>Class: 1, Prob(Class 1): 0.2722061388113392</tspan></text>\n",
       "</svg>\n",
       "</html>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><hr><h4>RDKit Fingerprint: CAS 149396-88-3 </h4><?xml version='1.0' encoding='iso-8859-1'?>\n",
       "<svg version='1.1' baseProfile='full'\n",
       "              xmlns:svg='http://www.w3.org/2000/svg'\n",
       "                      xmlns:rdkit='http://www.rdkit.org/xml'\n",
       "                      xmlns:xlink='http://www.w3.org/1999/xlink'\n",
       "                  xml:space='preserve'\n",
       "width='300px' height='300px' >\n",
       "<rect style='opacity:1.0;fill:#FFFFFF;stroke:none' width='300' height='300' x='0' y='0'> </rect>\n",
       "<ellipse cx='139.93' cy='221.435' rx='10.5324' ry='10.5324' style='fill:#F6F6F6;fill-rule:evenodd;stroke:#F6F6F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='171.071' cy='216.083' rx='10.5324' ry='10.5324' style='fill:#F6F6F6;fill-rule:evenodd;stroke:#F6F6F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='182.006' cy='186.439' rx='10.5324' ry='10.5324' style='fill:#F6F6F6;fill-rule:evenodd;stroke:#F6F6F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='161.801' cy='162.146' rx='10.5324' ry='10.5324' style='fill:#F6F6F6;fill-rule:evenodd;stroke:#F6F6F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='172.737' cy='132.502' rx='10.5324' ry='10.5324' style='fill:#276419;fill-rule:evenodd;stroke:#276419;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='152.531' cy='108.209' rx='10.5324' ry='10.5324' style='fill:#276419;fill-rule:evenodd;stroke:#276419;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='163.467' cy='78.5646' rx='10.5324' ry='10.5324' style='fill:#F6F6F6;fill-rule:evenodd;stroke:#F6F6F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='121.391' cy='113.561' rx='10.5324' ry='10.5324' style='fill:#276419;fill-rule:evenodd;stroke:#276419;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='98.7569' cy='91.5135' rx='10.5324' ry='10.5324' style='fill:#D7EEB8;fill-rule:evenodd;stroke:#D7EEB8;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='70.7942' cy='106.226' rx='10.5324' ry='10.5324' style='fill:#F6F6F6;fill-rule:evenodd;stroke:#F6F6F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='76.1461' cy='137.367' rx='10.5324' ry='10.5324' style='fill:#276419;fill-rule:evenodd;stroke:#276419;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='54.0985' cy='160.001' rx='10.5324' ry='10.5324' style='fill:#F6F6F6;fill-rule:evenodd;stroke:#F6F6F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='23.4733' cy='152.224' rx='10.5324' ry='10.5324' style='fill:#F6F6F6;fill-rule:evenodd;stroke:#F6F6F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='62.6762' cy='190.411' rx='10.5324' ry='10.5324' style='fill:#F6F6F6;fill-rule:evenodd;stroke:#F6F6F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='107.416' cy='141.9' rx='10.5324' ry='10.5324' style='fill:#276419;fill-rule:evenodd;stroke:#276419;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='122.129' cy='169.863' rx='10.5324' ry='10.5324' style='fill:#F6F6F6;fill-rule:evenodd;stroke:#F6F6F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='203.877' cy='127.15' rx='10.5324' ry='10.5324' style='fill:#85C049;fill-rule:evenodd;stroke:#85C049;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='214.813' cy='97.5054' rx='10.5324' ry='10.5324' style='fill:#F6F6F6;fill-rule:evenodd;stroke:#F6F6F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='224.082' cy='151.443' rx='10.5324' ry='10.5324' style='fill:#F6F6F6;fill-rule:evenodd;stroke:#F6F6F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='213.147' cy='181.087' rx='10.5324' ry='10.5324' style='fill:#F6F6F6;fill-rule:evenodd;stroke:#F6F6F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='255.223' cy='146.091' rx='10.5324' ry='10.5324' style='fill:#F6F6F6;fill-rule:evenodd;stroke:#F6F6F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='260.575' cy='177.231' rx='10.5324' ry='10.5324' style='fill:#F6F6F6;fill-rule:evenodd;stroke:#F6F6F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='249.871' cy='114.95' rx='10.5324' ry='10.5324' style='fill:#F6F6F6;fill-rule:evenodd;stroke:#F6F6F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<ellipse cx='286.364' cy='140.739' rx='10.5324' ry='10.5324' style='fill:#F6F6F6;fill-rule:evenodd;stroke:#F6F6F6;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 139.93,221.435 153.041,219.182' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 153.041,219.182 166.152,216.929' style='fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 173.013,210.817 177.51,198.628' style='fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 177.51,198.628 182.006,186.439' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 182.006,186.439 161.801,162.146' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 183.834,178.754 169.69,161.749' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 182.006,186.439 213.147,181.087' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 161.801,162.146 172.737,132.502' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 172.737,132.502 152.531,108.209' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 172.737,132.502 203.877,127.15' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 178.478,137.927 200.276,134.181' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 152.531,108.209 157.028,96.02' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 157.028,96.02 161.524,83.8308' style='fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 152.531,108.209 121.391,113.561' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 121.391,113.561 112.356,104.76' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 112.356,104.76 103.321,95.9597' style='fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 114.271,115.448 107.947,109.287' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 107.947,109.287 101.622,103.127' style='fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 121.391,113.561 115.702,125.097' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 115.702,125.097 110.013,136.634' style='fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 94.1924,93.9151 82.4933,100.071' style='fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 82.4933,100.071 70.7942,106.226' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 70.7942,106.226 76.1461,137.367' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 77.8251,109.827 81.5714,131.626' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 76.1461,137.367 67.9437,145.788' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 67.9437,145.788 59.7413,154.208' style='fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 76.1461,137.367 89.499,139.303' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 89.499,139.303 102.852,141.238' style='fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 48.4658,155.31 38.8176,152.86' style='fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 38.8176,152.86 29.1695,150.41' style='fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 46.9104,161.436 37.2623,158.986' style='fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 37.2623,158.986 27.6141,156.536' style='fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 55.7325,165.794 58.3874,175.206' style='fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 58.3874,175.206 61.0423,184.619' style='fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 110.187,147.166 116.158,158.515' style='fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 116.158,158.515 122.129,169.863' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 203.877,127.15 208.374,114.961' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 208.374,114.961 212.87,102.772' style='fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 203.877,127.15 224.082,151.443' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 224.082,151.443 213.147,181.087' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 216.513,153.702 208.858,174.453' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 224.082,151.443 255.223,146.091' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 255.223,146.091 260.575,177.231' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 255.223,146.091 249.871,114.95' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<path d='M 255.223,146.091 286.364,140.739' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
       "<text x='166.152' y='221.35' style='font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000' ><tspan>O</tspan></text>\n",
       "<text x='153.984' y='83.8308' style='font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000' ><tspan>OH</tspan></text>\n",
       "<text x='94.1924' y='96.7797' style='font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#0000FF' ><tspan>N</tspan></text>\n",
       "<text x='47.6881' y='165.794' style='font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#0000FF' ><tspan>N</tspan><tspan style='baseline-shift:super;font-size:7.5px;'>+</tspan><tspan></tspan></text>\n",
       "<text x='18.5549' y='157.49' style='font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000' ><tspan>O</tspan></text>\n",
       "<text x='56.7051' y='196.204' style='font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000' ><tspan>O</tspan><tspan style='baseline-shift:super;font-size:7.5px;'>-</tspan><tspan></tspan></text>\n",
       "<text x='102.852' y='147.166' style='font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#0000FF' ><tspan>N</tspan></text>\n",
       "<text x='205.33' y='102.772' style='font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000' ><tspan>OH</tspan></text>\n",
       "<text x='1.86555' y='288' style='font-size:12px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000' ><tspan>Class: 1, Prob(Class 1): 0.3176612276612276</tspan></text>\n",
       "</svg>\n",
       "</html>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def predFunc(fp,model):\n",
    "  a = np.zeros(len(fp))\n",
    "  try:\n",
    "    DataStructs.ConvertToNumpyArray(fp,a)\n",
    "  except:\n",
    "    return None\n",
    "  #print(model.predict_proba(a.reshape(1, -1)))\n",
    "  return model.predict_proba(a.reshape(1, -1))[0][-1]\n",
    "\n",
    "def colorMap(c):\n",
    "    _ = (np.sign(c)*(np.sqrt(np.abs(c)))+1.0)/2.0\n",
    "    return cm.PiYG(_)\n",
    "\n",
    "def showMorgan2SimilarityMap(dataRow, model):\n",
    "    mol = dataRow['Molecule']\n",
    "    weights = SimilarityMaps.GetAtomicWeightsForModel(mol,lambda mol,atomId: SimilarityMaps.GetMorganFingerprint(mol,atomId,radius=2, fpType='bv', nBits=2048),lambda x: predFunc(x, morgan_bnb) )\n",
    "    mc = rdMolDraw2D.PrepareMolForDrawing(mol, kekulize=True)\n",
    "    drawer = rdMolDraw2D.MolDraw2DSVG(300,300)\n",
    "    drawer.DrawMolecule(mc,highlightAtoms=[i for i in range(len(weights))],\n",
    "                        highlightAtomColors=dict([(i,colorMap(weights[i])) for i in range(len(weights))]),\n",
    "                        highlightAtomRadii=dict([(i,0.5) for i in range(len(weights))]),\n",
    "                        highlightBonds=[],\n",
    "                        legend='Class: {0}, Prob(Class 1): {1}'.format(dataRow['Class'],\n",
    "                                                                       model.predict_proba(dataRow['Morgan2FP'].reshape(1, -1))[0][-1]))\n",
    "    drawer.FinishDrawing()\n",
    "    text = drawer.GetDrawingText().replace('svg:','')\n",
    "    return HTML('<html><hr><h4>Morgan2 Fingerprint: CAS {0}</h4>{1}</html>'.format(dataRow['CAS'],text))\n",
    "\n",
    "def showRDKit7SimilarityMap(dataRow, model):\n",
    "    mol = dataRow['Molecule']\n",
    "    weights = SimilarityMaps.GetAtomicWeightsForModel(mol,lambda mol,atomId: SimilarityMaps.GetRDKFingerprint(mol,atomId,maxPath=7, fpType='bv', nBits=2048),lambda x: predFunc(x, rd_bnb) )\n",
    "    mc = rdMolDraw2D.PrepareMolForDrawing(mol, kekulize=True)\n",
    "    drawer = rdMolDraw2D.MolDraw2DSVG(300,300)\n",
    "    drawer.DrawMolecule(mc,highlightAtoms=[i for i in range(len(weights))],\n",
    "                        highlightAtomColors=dict([(i,colorMap(weights[i])) for i in range(len(weights))]),\n",
    "                        highlightAtomRadii=dict([(i,0.5) for i in range(len(weights))]),\n",
    "                        highlightBonds=[],\n",
    "                        legend='Class: {0}, Prob(Class 1): {1}'.format(dataRow['Class'],\n",
    "                                                                       model.predict_proba(dataRow['RDKit7FP'].reshape(1, -1))[0][-1]))\n",
    "    drawer.FinishDrawing()\n",
    "    text = drawer.GetDrawingText().replace('svg:','')\n",
    "    return HTML('<html><hr><h4>RDKit Fingerprint: CAS {0}</h4>{1}</html>'.format(dataRow['CAS'],text))\n",
    "\n",
    "ID = 1779 #79\n",
    "display(HTML(testSet.ix[[ID]].to_html()))\n",
    "display(HTML('<h3>Bernoulli Naive Bayes</h3>'))\n",
    "display(showMorgan2SimilarityMap(testSet.ix[ID], morgan_bnb))\n",
    "display(showRDKit7SimilarityMap(testSet.ix[ID], rd_bnb))\n",
    "\n",
    "display(HTML('<h3>Optimized Random Forest</h3>'))\n",
    "display(showMorgan2SimilarityMap(testSet.ix[ID], best_morgan_treemodel))\n",
    "display(showRDKit7SimilarityMap(testSet.ix[ID], best_rd_treemodel))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}