{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Machine Learning (classification and regression)\n",
    "### myChEMBL team, ChEMBL group, EMBL-EBI."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial is based on the great notebook by Nikolas Fechner and Greg Landrum [1]. In addition, there is a demonstration of a regression model trained on a ChEMBL data set. The notebook also uses code snippets from a notebook by Isidro Cortés Ciriano [2]. The interested reader is referred to these notebooks for further information. \n",
    "\n",
    "\n",
    "1 : http://nbviewer.ipython.org/gist/greglandrum/4316463\n",
    "\n",
    "2 : http://nbviewer.ipython.org/github/BenderGroup/IPythonNotebooks/blob/master/Isidro_RDKit_Introduction/RDkit_Introduction.ipynb?create=1 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Helper methods"
   ]
  },
  {
   "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"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%pylab inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
    "\n",
    "from sklearn.metrics import classification_report, confusion_matrix, roc_curve,auc,accuracy_score\n",
    "\n",
    "def confusion_matrix_summary(acts,preds):\n",
    "    from cStringIO import StringIO\n",
    "    file_str = StringIO()\n",
    "    vTab=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",
    "    fpr, tpr, thresholds = roc_curve(observations, probabilities)\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    print \"Area under the ROC curve : %f\" % roc_auc\n",
    "    pl.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % 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 createPropAccPlot(pl,observations,probabilities,caption):\n",
    "    fpr, tpr, thresholds = roc_curve(observations, probabilities)\n",
    "    accuracies = []\n",
    "    for t in thresholds:\n",
    "        \n",
    "        predictions = [1 if p >= t else 0 for p in probabilities]\n",
    "        acc = accuracy_score(observations,predictions)\n",
    "        accuracies.append(acc)\n",
    "    pl.plot(thresholds, accuracies, label='Prob. vs Accuracy curve')\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('Probability threshold')\n",
    "    pl.set_ylabel('Accuracy')\n",
    "    pl.set_title(caption)\n",
    "\n",
    "def createImportancePlot(splt,desc,importances,caption):\n",
    "    #plt.xkcd()\n",
    "    import numpy as np    \n",
    "    labels = []\n",
    "    weights = []\n",
    "    threshold = sort([abs(w) for w in importances])[-11]\n",
    "    for d in zip(desc,importances):\n",
    "        if abs(d[1]) > threshold:\n",
    "            labels.append(d[0])\n",
    "            weights.append(d[1])\n",
    "    \n",
    "    xlocations = np.array(range(len(labels)))+0.5\n",
    "    width = 0.8\n",
    "    splt.bar(xlocations, weights, width=width)\n",
    "    splt.set_xticks([r+1 for r in range(len(labels))])\n",
    "    splt.set_xticklabels(labels, rotation='vertical')\n",
    "    splt.set_xlim(0, xlocations[-1]+width*2)\n",
    "    splt.set_title(caption)\n",
    "    splt.get_xaxis().tick_bottom()\n",
    "    splt.get_yaxis().tick_left()"
   ]
  },
  {
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data available from http://doc.ml.tu-berlin.de/toxbenchmark/smiles_cas_N6512.smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from rdkit import Chem\n",
    "import csv\n",
    "#obtained from http://doc.ml.tu-berlin.de/toxbenchmark/smiles_cas_N6512.smi\n",
    "with open('/home/chembl/ipynb_workbench/smiles_cas_N6512.smi','rU') as train_file:\n",
    "    r = csv.reader(train_file, delimiter='\\t')\n",
    "    r.next()\n",
    "    molecules = []\n",
    "    observations = []\n",
    "    for row in r:\n",
    "        try:\n",
    "         temp = Chem.MolFromSmiles(row[0])        \n",
    "        except e:\n",
    "            print e,row\n",
    "            continue\n",
    "        molecules.append(temp)\n",
    "        observations.append(int(row[2]))\n",
    "#molecules = [Chem.MolFromSmiles(d[0]) for d in row]\n",
    "#observations = [int(d[2]) for d in raw]\n",
    "data = [(x,y) for x,y in zip(molecules,observations) if x is not None]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fingerprint calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from rdkit.Chem import AllChem\n",
    "import random\n",
    "\n",
    "ind = range(len(data))\n",
    "random.seed(0)\n",
    "random.shuffle(ind)\n",
    "half = int(round(0.5*len(ind)))\n",
    "train = ind[:half]\n",
    "test = ind[half:]\n",
    "\n",
    "train_morgan_fps = []\n",
    "train_rd_fps = []\n",
    "train_ys = []\n",
    "train_info=[]\n",
    "test_info=[]\n",
    "\n",
    "for i in train:\n",
    "    mol = data[i][0]\n",
    "    info = {}\n",
    "    temp = AllChem.GetMorganFingerprintAsBitVect(mol,2,2048,bitInfo=info)\n",
    "    train_morgan_fps.append(temp)\n",
    "    train_info.append(info)\n",
    "    train_rd_fps.append(Chem.RDKFingerprint(mol))\n",
    "    train_ys.append(int(data[i][1]))\n",
    "    \n",
    "test_morgan_fps = []\n",
    "test_rd_fps = []\n",
    "test_ys = []\n",
    "    \n",
    "for i in test:\n",
    "    mol = data[i][0]\n",
    "    info = {}\n",
    "    temp = AllChem.GetMorganFingerprintAsBitVect(mol,2,2048,bitInfo=info)\n",
    "    test_morgan_fps.append(temp)\n",
    "    test_info.append(info)\n",
    "    test_rd_fps.append(Chem.RDKFingerprint(mol))\n",
    "    test_ys.append(int(data[i][1]))\n",
    "    \n",
    "#import cPickle\n",
    "#outf=file('fps.pkl','wb+')\n",
    "#cPickle.dump((train,train_morgan_fps,train_info,train_rd_fps,train_ys),outf)\n",
    "#cPickle.dump((test,test_morgan_fps,test_info,test_rd_fps,test_ys),outf)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Writing out the data can be a bit lengthy (especially on the virtual machine), hence the written file is provided in mychembl and the text is commented out. When uncommenting, please give the script some time before continuing. If an error occurs below 'EOF: ' this means the writing was not completed. Try these two sections again in this case. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import cPickle\n",
    "inf=file('/home/chembl/ipynb_workbench/fps.pkl','rb+')\n",
    "(train,train_morgan_fps,train_info,train_rd_fps,train_ys)=cPickle.load(inf)\n",
    "(test,test_morgan_fps,test_info,test_rd_fps,test_ys)=cPickle.load(inf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Binary Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Morgan Fingerprints\n",
      "\n",
      "\n",
      "\tResults Table (experiment in rows):\n",
      "       1153    332     |  77.64\n",
      "        548   1219     |  68.99\n",
      "    --------------\n",
      "      67.78  78.59\n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.68      0.78      0.72      1485\n",
      "          1       0.79      0.69      0.73      1767\n",
      "\n",
      "avg / total       0.74      0.73      0.73      3252\n",
      "\n",
      "------------------------------------------------------------\n",
      "RDKit Fingerprints\n",
      "\n",
      "\n",
      "\tResults Table (experiment in rows):\n",
      "       1012    473     |  68.15\n",
      "        750   1017     |  57.56\n",
      "    --------------\n",
      "      57.43  68.26\n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.57      0.68      0.62      1485\n",
      "          1       0.68      0.58      0.62      1767\n",
      "\n",
      "avg / total       0.63      0.62      0.62      3252\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import BernoulliNB\n",
    "morgan_bnb = BernoulliNB(fit_prior=False)\n",
    "morgan_bnb.fit(train_morgan_fps,train_ys)\n",
    "morgan_predictions = morgan_bnb.predict(test_morgan_fps)\n",
    "print 'Morgan Fingerprints'\n",
    "print confusion_matrix_summary(test_ys,morgan_predictions)\n",
    "print classification_report(test_ys,morgan_predictions)\n",
    "\n",
    "\n",
    "print '------------------------------------------------------------'\n",
    "print 'RDKit Fingerprints'\n",
    "rd_bnb = BernoulliNB(fit_prior=False)\n",
    "rd_bnb.fit(train_rd_fps,train_ys)\n",
    "rd_predictions = rd_bnb.predict(test_rd_fps)\n",
    "print confusion_matrix_summary(test_ys,rd_predictions)\n",
    "print classification_report(test_ys,rd_predictions)\n",
    "\n",
    "morgan_probabilities = [p[1] for p in morgan_bnb.predict_proba(test_morgan_fps)]\n",
    "rd_probabilities = [p[1] for p in rd_bnb.predict_proba(test_rd_fps)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Area under the ROC curve : 0.791429\n",
      "Area under the ROC curve : 0.646198\n"
     ]
    },
    {
     "data": {
      "image/png": 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8sbuIUSQz1lqd9pUFTQsR8efHH92+6uRkNz62enXficJHtThzmhYi3nTt6hrr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\nHrUeNixajTWoDotEUcTKTPz274fGjcNOISIiYXv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9FnYSEZHEitzIdWGhmmsRkWT3/PPQ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\nqLkWEREREfHJ/wEpkNq/8OC4pwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa8bc87d390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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12WdL339Pcw0ALgWmuc7KsuKTbADx6qKLpC+/lCSaawBwKTAHNNJYA6UXCoWU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\nVpB/ARlFnraBpvh7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa8be4ab390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure,(plt1,plt2) = pyplot.subplots(1,2)\n",
    "figure.set_size_inches(10,5)\n",
    "figure.tight_layout()\n",
    "\n",
    "createROCPlot(plt1,test_ys,morgan_probabilities,'Morgan FP')\n",
    "createROCPlot(plt2,test_ys,rd_probabilities,'RDKit FP')\n",
    "\n",
    "figure,(plt1,plt2) = pyplot.subplots(1,2)\n",
    "figure.set_size_inches(10,5)\n",
    "figure.tight_layout()\n",
    "\n",
    "createPropAccPlot(plt1,test_ys,morgan_probabilities,'Morgan FP')\n",
    "createPropAccPlot(plt2,test_ys,rd_probabilities,'RDKit FP')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Obtain bit probabilities for later analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bit_probabilities = morgan_bnb.feature_log_prob_\n",
    "position = []\n",
    "enrichment = []\n",
    "for d in zip(range(len(bit_probabilities[1])),[(w[0]-w[1]) for w in zip(bit_probabilities[0],bit_probabilities[1])]):\n",
    "    if abs(d[1]) >= 1: #one log-level probablity difference between classes\n",
    "        position.append(d[0])\n",
    "        enrichment.append(d[1])\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Binary classification is obtained by mapping selecting the most likely class "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\tResults Table (experiment in rows):\n",
      "       1153    332     |  77.64\n",
      "        548   1219     |  68.99\n",
      "    --------------\n",
      "      67.78  78.59\n",
      "\n",
      "\n",
      "\n",
      "\tResults Table (experiment in rows):\n",
      "       1153    332     |  77.64\n",
      "        548   1219     |  68.99\n",
      "    --------------\n",
      "      67.78  78.59\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#as implemented in sklearn\n",
    "check_predictions = [1 if t[1] >= t[0] else 0 for t in morgan_bnb.predict_proba(test_morgan_fps)]\n",
    "print confusion_matrix_summary(test_ys,check_predictions)\n",
    "\n",
    "#equivalent for binary classification\n",
    "check_predictions = [1 if p >= 0.5 else 0 for p in [t[1] for t in morgan_bnb.predict_proba(test_morgan_fps)]]\n",
    "print confusion_matrix_summary(test_ys,check_predictions)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note, depending on your hardware this can take up to 30 minutes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 1 candidates, totalling 10 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:  2.0min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Morgan Fingerprints\n",
      "\n",
      "\n",
      "\tResults Table (experiment in rows):\n",
      "       1187    298     |  79.93\n",
      "        456   1311     |  74.19\n",
      "    --------------\n",
      "      72.25  81.48\n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.72      0.80      0.76      1485\n",
      "          1       0.81      0.74      0.78      1767\n",
      "\n",
      "avg / total       0.77      0.77      0.77      3252\n",
      "\n",
      "------------------------------------------------------------\n",
      "Fitting 10 folds for each of 1 candidates, totalling 10 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:  1.9min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RDKit Fingerprints\n",
      "\n",
      "\n",
      "\tResults Table (experiment in rows):\n",
      "       1177    308     |  79.26\n",
      "        372   1395     |  78.95\n",
      "    --------------\n",
      "      75.98  81.91\n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.76      0.79      0.78      1485\n",
      "          1       0.82      0.79      0.80      1767\n",
      "\n",
      "avg / total       0.79      0.79      0.79      3252\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':[2,5,10,20],'min_samples_split':[2,8,32,128],'min_samples_leaf':[1,2,5,10],'compute_importances':[True],'n_estimators':[200]}\n",
    "#grid search is very timeconsuming, ~3hrs\n",
    "params = {'min_samples_split': [8], 'max_depth': [20], 'min_samples_leaf': [1],'n_estimators':[200]}\n",
    "cv = KFold(n=len(train),n_folds=10,shuffle=True)\n",
    "gs = GridSearchCV(RandomForestClassifier(), params, cv=cv,verbose=1,refit=True)\n",
    "gs.fit(train_morgan_fps, train_ys)\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(test_morgan_fps)\n",
    "print 'Morgan Fingerprints'\n",
    "print confusion_matrix_summary(test_ys,best_morgan_tree_prediction)\n",
    "print classification_report(test_ys,best_morgan_tree_prediction)\n",
    "\n",
    "print '------------------------------------------------------------'\n",
    "gs.fit(train_rd_fps, train_ys)\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(test_rd_fps)\n",
    "print 'RDKit Fingerprints'\n",
    "print confusion_matrix_summary(test_ys,best_rd_tree_prediction)\n",
    "print classification_report(test_ys,best_rd_tree_prediction)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Probabilities can be obtained as proportion of the trees that predict the respective class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Area under the ROC curve : 0.847774\n",
      "Area under the ROC curve : 0.865580\n"
     ]
    },
    {
     "data": {
      "image/png": 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yqA5LQeQ5cp3LUOB54FXgGuA2gntwQTHcJc9EoAww2xgz21q78vAX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\nERGRHFFzLSIiIiKSI/8flnuiCBGxEsoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa8bc8a3dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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bfinBoQ2NEpFmznSF9cKFKqwjTY0arpvI3r3utMyPP3a3GS+/PJvdu2/l5JMH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\nm5mZmZmlxMW1mZmZmVlK/h882UEeLPAl0wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa8be5f83d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "morgan_probabilities = [p[1] for p in best_morgan_treemodel.predict_proba(test_morgan_fps)]\n",
    "rd_probabilities = [p[1] for p in best_rd_treemodel.predict_proba(test_rd_fps)]\n",
    "figure,(plt1,plt2) = pyplot.subplots(1,2)\n",
    "figure.set_size_inches(10,5)\n",
    "figure.tight_layout()\n",
    "\n",
    "createROCPlot(plt1,test_ys,morgan_probabilities,'Morgan FP')\n",
    "createROCPlot(plt2,test_ys,rd_probabilities,'RDKit FP')\n",
    "\n",
    "figure,(plt1,plt2) = pyplot.subplots(1,2)\n",
    "figure.set_size_inches(10,5)\n",
    "figure.tight_layout()\n",
    "\n",
    "createPropAccPlot(plt1,test_ys,morgan_probabilities,'Morgan FP')\n",
    "createPropAccPlot(plt2,test_ys,rd_probabilities,'RDKit FP')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Substructure analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Idea: Compute the probability of a bit for each class, identify bits enriched in one class and report substructures associated with these bits. Then identify substructures that occur predominantly in molecules belonging to one class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bit 6: \t6 (6) molecules, in-class log_probabilities -6.637 -5.154 \t SMILES: n(C)(c(n)N)c(c)c\n",
      "bit 11: \t8 (0) molecules, in-class log_probabilities -4.557 -5.848 \t SMILES: S(C)CC\n",
      "bit 16: \t12 (1) molecules, in-class log_probabilities -4.622 -5.665 \t SMILES: C(C)(CC)(C(=C)C)C(C)C\n",
      "bit 34: \t6 (5) molecules, in-class log_probabilities -5.250 -4.161 \t SMILES: C(C)n\n",
      "bit 64: \t10 (10) molecules, in-class log_probabilities -5.720 -4.413 \t SMILES: c(cc)(cs)C(N)=O\n",
      "bit 79: \t24 (1) molecules, in-class log_probabilities -3.803 -5.665 \t SMILES: C(c)C\n",
      "bit 106: \t7 (1) molecules, in-class log_probabilities -4.194 -5.260 \t SMILES: c(c)(n)O\n",
      "bit 131: \t15 (14) molecules, in-class log_probabilities -5.720 -4.624 \t SMILES: c(c)(c)s\n",
      "bit 144: \t14 (14) molecules, in-class log_probabilities -5.250 -4.125 \t SMILES: N(C)=[N+]\n",
      "bit 164: \t17 (16) molecules, in-class log_probabilities -3.774 -2.695 \t SMILES: c(c)([N+])s\n",
      "bit 199: \t29 (26) molecules, in-class log_probabilities -4.765 -2.784 \t SMILES: c(c(c)[N+])c(c)[N+]\n",
      "bit 255: \t36 (36) molecules, in-class log_probabilities -5.027 -3.506 \t SMILES: [N+](=O)([O-])c(c)o\n",
      "bit 256: \t14 (14) molecules, in-class log_probabilities -5.250 -4.199 \t SMILES: c(C=C)(cc)oc\n",
      "bit 307: \t7 (0) molecules, in-class log_probabilities -4.765 -6.071 \t SMILES: N(C)S\n",
      "bit 312: \t11 (11) molecules, in-class log_probabilities -5.720 -4.567 \t SMILES: N(CC)=[N+]=[N-]\n",
      "bit 394: \t15 (1) molecules, in-class log_probabilities -4.439 -6.358 \t SMILES: CS\n",
      "bit 505: \t10 (9) molecules, in-class log_probabilities -5.943 -4.461 \t SMILES: C(CC)(OC)c(c)c\n",
      "bit 513: \t7 (0) molecules, in-class log_probabilities -5.027 -6.071 \t SMILES: P(=O)(Oc)(Oc)Oc\n",
      "bit 587: \t20 (20) molecules, in-class log_probabilities -5.384 -3.628 \t SMILES: c(cc)(cc)C(N)=O\n",
      "bit 598: \t15 (2) molecules, in-class log_probabilities -3.666 -4.818 \t SMILES: C(c)(C)C\n",
      "bit 606: \t7 (1) molecules, in-class log_probabilities -3.480 -4.624 \t SMILES: c(=O)(nc)n(C)c\n",
      "bit 628: \t15 (2) molecules, in-class log_probabilities -4.239 -5.511 \t SMILES: C[N+]\n",
      "bit 647: \t46 (39) molecules, in-class log_probabilities -4.765 -3.673 \t SMILES: c(N)(cc)cc\n",
      "bit 684: \t7 (0) molecules, in-class log_probabilities -4.691 -6.764 \t SMILES: C(=O)(O)CO\n",
      "bit 715: \t508 (423) molecules, in-class log_probabilities -2.518 -1.379 \t SMILES: [O-]\n",
      "bit 716: \t206 (181) molecules, in-class log_probabilities -3.864 -2.194 \t SMILES: c(c)(c)[N+]\n",
      "bit 716: \t8 (7) molecules, in-class log_probabilities -3.864 -2.194 \t SMILES: n(cc)c(c)c\n",
      "bit 724: \t6 (1) molecules, in-class log_probabilities -4.385 -5.665 \t SMILES: C(c)(c)=N\n",
      "bit 725: \t11 (11) molecules, in-class log_probabilities -2.657 -1.499 \t SMILES: c(c(C)c)c([N+])s\n",
      "bit 725: \t237 (211) molecules, in-class log_probabilities -2.657 -1.499 \t SMILES: c(cc)(cc)c(c)c\n",
      "bit 740: \t28 (27) molecules, in-class log_probabilities -4.845 -3.525 \t SMILES: [N-]=[N+]\n",
      "bit 753: \t486 (411) molecules, in-class log_probabilities -2.852 -1.422 \t SMILES: O=[N+]\n",
      "bit 780: \t20 (19) molecules, in-class log_probabilities -6.231 -4.818 \t SMILES: C(=Cc)c(c)c\n",
      "bit 801: \t12 (1) molecules, in-class log_probabilities -4.691 -5.848 \t SMILES: c(cc)(c(N)s)c(c)n\n",
      "bit 808: \t7 (0) molecules, in-class log_probabilities -5.027 -6.764 \t SMILES: C(C)(C)(Cl)Cl\n",
      "bit 811: \t13 (13) molecules, in-class log_probabilities -5.384 -4.366 \t SMILES: O(C(C)=O)N(C)O\n",
      "bit 815: \t20 (2) molecules, in-class log_probabilities -4.557 -3.180 \t SMILES: S(c)(N)(=O)=O\n",
      "bit 815: \t101 (88) molecules, in-class log_probabilities -4.557 -3.180 \t SMILES: N(N)=O\n",
      "bit 838: \t495 (416) molecules, in-class log_probabilities -2.948 -1.422 \t SMILES: [O-][N+]\n",
      "bit 851: \t7 (0) molecules, in-class log_probabilities -4.497 -5.665 \t SMILES: C(Cc)(NC)C(=O)O\n",
      "bit 860: \t48 (46) molecules, in-class log_probabilities -6.231 -4.322 \t SMILES: c(c(c)[N+])c(c)c\n",
      "bit 870: \t13 (13) molecules, in-class log_probabilities -5.250 -4.056 \t SMILES: c(C)(c(c)c)c(c)c\n",
      "bit 909: \t21 (17) molecules, in-class log_probabilities -4.285 -5.511 \t SMILES: N(C)(C)N\n",
      "bit 922: \t7 (7) molecules, in-class log_probabilities -5.943 -4.238 \t SMILES: s(c(c)c)c(c)c\n",
      "bit 961: \t99 (82) molecules, in-class log_probabilities -3.929 -2.903 \t SMILES: c(cc)c(c)n\n",
      "bit 978: \t7 (0) molecules, in-class log_probabilities -4.285 -5.511 \t SMILES: c(cc)(c(c)C)c(c)[nH]\n",
      "bit 979: \t133 (116) molecules, in-class log_probabilities -4.152 -2.621 \t SMILES: O=N\n",
      "bit 991: \t7 (0) molecules, in-class log_probabilities -4.622 -5.665 \t SMILES: c(cc)(OC)c(c)Cl\n",
      "bit 994: \t11 (11) molecules, in-class log_probabilities -5.943 -4.567 \t SMILES: C(ON)c(c)c\n",
      "bit 1007: \t17 (15) molecules, in-class log_probabilities -5.720 -4.461 \t SMILES: c(c(-c)c)(c(-c)c)c(c)c\n",
      "bit 1015: \t10 (10) molecules, in-class log_probabilities -5.720 -4.567 \t SMILES: c(cc)(cc)N=O\n",
      "bit 1033: \t15 (13) molecules, in-class log_probabilities -5.720 -4.567 \t SMILES: c(nc)(c(n)N)c(n)n\n",
      "bit 1034: \t6 (1) molecules, in-class log_probabilities -3.746 -4.749 \t SMILES: C(C)(C)(O)O\n",
      "bit 1056: \t30 (30) molecules, in-class log_probabilities -5.250 -3.819 \t SMILES: c(cc)c([N+])o\n",
      "bit 1083: \t20 (20) molecules, in-class log_probabilities -4.334 -3.314 \t SMILES: N(=O)N(C)C\n",
      "bit 1098: \t19 (17) molecules, in-class log_probabilities -6.637 -4.413 \t SMILES: N(CC)c(c)c\n",
      "bit 1110: \t16 (3) molecules, in-class log_probabilities -3.641 -5.260 \t SMILES: c(Cl)(cc)c(c)O\n",
      "bit 1129: \t14 (1) molecules, in-class log_probabilities -4.765 -6.071 \t SMILES: C(NC)C(C)O\n",
      "bit 1195: \t460 (395) molecules, in-class log_probabilities -3.026 -1.448 \t SMILES: [N+](c)(=O)[O-]\n",
      "bit 1216: \t31 (31) molecules, in-class log_probabilities -6.637 -3.846 \t SMILES: c(cc)(oc)[N+](=O)[O-]\n",
      "bit 1228: \t77 (62) molecules, in-class log_probabilities -5.720 -6.764 \t SMILES: O(C)C\n",
      "bit 1256: \t23 (3) molecules, in-class log_probabilities -4.497 -6.071 \t SMILES: C(CC)C(=O)O\n",
      "bit 1263: \t7 (6) molecules, in-class log_probabilities -6.231 -4.684 \t SMILES: C(N)(O)=O\n",
      "bit 1276: \t6 (0) molecules, in-class log_probabilities -5.250 -6.358 \t SMILES: O(C(C)=O)C(C)C\n",
      "bit 1276: \t8 (7) molecules, in-class log_probabilities -5.250 -6.358 \t SMILES: C(=CC)c(c)c\n",
      "bit 1300: \t16 (13) molecules, in-class log_probabilities -4.557 -3.283 \t SMILES: C(Cc)C(c)=O\n",
      "bit 1300: \t53 (48) molecules, in-class log_probabilities -4.557 -3.283 \t SMILES: n(c(c)c)c(c)c\n",
      "bit 1326: \t12 (1) molecules, in-class log_probabilities -4.622 -6.764 \t SMILES: C(CC)(C(C)C)C(C)C\n",
      "bit 1354: \t9 (0) molecules, in-class log_probabilities -5.538 -6.764 \t SMILES: c(Cl)(cc)c(c)C\n",
      "bit 1358: \t6 (1) molecules, in-class log_probabilities -5.250 -6.764 \t SMILES: C(C)Nc\n",
      "bit 1365: \t78 (13) molecules, in-class log_probabilities -3.666 -4.892 \t SMILES: c(C)(c)c\n",
      "bit 1365: \t12 (11) molecules, in-class log_probabilities -3.666 -4.892 \t SMILES: c(O)(c(C)c)c(c)C\n",
      "bit 1366: \t59 (9) molecules, in-class log_probabilities -2.841 -4.023 \t SMILES: C(CC)C(C)C\n",
      "bit 1382: \t8 (0) molecules, in-class log_probabilities -4.439 -7.457 \t SMILES: C(=O)(CC)Nc\n",
      "bit 1386: \t66 (12) molecules, in-class log_probabilities -3.322 -4.684 \t SMILES: C(C)(O)=O\n",
      "bit 1386: \t6 (1) molecules, in-class log_probabilities -3.322 -4.684 \t SMILES: C(CC)C(C)=C\n",
      "bit 1393: \t7 (0) molecules, in-class log_probabilities -5.538 -7.457 \t SMILES: C(CC)C(C)N\n",
      "bit 1421: \t15 (2) molecules, in-class log_probabilities -5.538 -6.764 \t SMILES: C(C)(C)=O\n",
      "bit 1421: \t6 (1) molecules, in-class log_probabilities -5.538 -6.764 \t SMILES: O(CC)P(O)(O)=O\n",
      "bit 1433: \t24 (23) molecules, in-class log_probabilities -6.231 -4.567 \t SMILES: c(cc)(c(c)c)[N+](=O)[O-]\n",
      "bit 1434: \t8 (1) molecules, in-class log_probabilities -5.720 -6.764 \t SMILES: c(cc)(cc)C(C)O\n",
      "bit 1444: \t242 (40) molecules, in-class log_probabilities -3.039 -4.056 \t SMILES: C(CC)CC\n",
      "bit 1449: \t28 (27) molecules, in-class log_probabilities -5.133 -4.023 \t SMILES: [N-]\n",
      "bit 1489: \t13 (2) molecules, in-class log_probabilities -5.720 -6.764 \t SMILES: c(cc)(cc)S(N)(=O)=O\n",
      "bit 1490: \t13 (12) molecules, in-class log_probabilities -5.027 -6.764 \t SMILES: C(=O)(NC)c(c)c\n",
      "bit 1500: \t26 (25) molecules, in-class log_probabilities -7.330 -5.848 \t SMILES: c(cc)(c(c)C)c(c)c\n",
      "bit 1532: \t8 (1) molecules, in-class log_probabilities -4.691 -6.358 \t SMILES: C(=O)(O)C=C\n",
      "bit 1532: \t6 (1) molecules, in-class log_probabilities -4.691 -6.358 \t SMILES: c(c(c)S)c(c)N\n",
      "bit 1549: \t8 (7) molecules, in-class log_probabilities -6.637 -5.154 \t SMILES: c(N=N)(c(c)O)c(c)S\n",
      "bit 1553: \t8 (8) molecules, in-class log_probabilities -6.637 -5.154 \t SMILES: c(nc)(c(c)c)c(c)n\n",
      "bit 1554: \t40 (7) molecules, in-class log_probabilities -4.194 -5.511 \t SMILES: c(Cl)(cc)c(c)Cl\n",
      "bit 1562: \t6 (6) molecules, in-class log_probabilities -6.231 -5.059 \t SMILES: c(cc)(c(c)n)c(c)n\n",
      "bit 1566: \t15 (14) molecules, in-class log_probabilities -6.637 -4.892 \t SMILES: c(cc)(c(c)c)c(c)n\n",
      "bit 1574: \t260 (235) molecules, in-class log_probabilities -4.034 -5.665 \t SMILES: c(cc)(c(c)c)c(c)c\n",
      "bit 1574: \t9 (0) molecules, in-class log_probabilities -4.034 -5.665 \t SMILES: N(CC)C(C)C\n",
      "bit 1582: \t23 (2) molecules, in-class log_probabilities -4.152 -5.511 \t SMILES: C(CC)C(C)(C)C\n",
      "bit 1582: \t12 (2) molecules, in-class log_probabilities -4.152 -5.511 \t SMILES: C(C)(C)=CC\n",
      "bit 1592: \t10 (1) molecules, in-class log_probabilities -4.622 -6.071 \t SMILES: O(CC)C(c)=O\n",
      "bit 1611: \t7 (6) molecules, in-class log_probabilities -5.250 -7.457 \t SMILES: n(c(n)N)c(c)c\n",
      "bit 1615: \t9 (9) molecules, in-class log_probabilities -7.330 -4.624 \t SMILES: c(cc)(c(c)c)c(c)[nH]\n",
      "bit 1624: \t25 (2) molecules, in-class log_probabilities -4.034 -5.154 \t SMILES: C(=O)(O)C(C)N\n",
      "bit 1648: \t67 (56) molecules, in-class log_probabilities -4.497 -3.314 \t SMILES: c(c)(c)C\n",
      "bit 1655: \t23 (23) molecules, in-class log_probabilities -5.943 -4.238 \t SMILES: o(c(C)c)c(c)[N+]\n",
      "bit 1660: \t12 (0) molecules, in-class log_probabilities -4.932 -6.358 \t SMILES: C(=O)(NC)C(C)N\n",
      "bit 1660: \t7 (0) molecules, in-class log_probabilities -4.932 -6.358 \t SMILES: c(cc)(CC)c(C)c\n",
      "bit 1674: \t19 (19) molecules, in-class log_probabilities -5.720 -4.684 \t SMILES: C1(c(c)c)OC1c\n",
      "bit 1707: \t44 (39) molecules, in-class log_probabilities -6.637 -5.511 \t SMILES: C1OC1C\n",
      "bit 1718: \t187 (32) molecules, in-class log_probabilities -4.152 -5.848 \t SMILES: C(C)C\n",
      "bit 1718: \t12 (0) molecules, in-class log_probabilities -4.152 -5.848 \t SMILES: C(CC)C(N)=O\n",
      "bit 1718: \t7 (1) molecules, in-class log_probabilities -4.152 -5.848 \t SMILES: C(C=C)C(C)C\n",
      "bit 1733: \t23 (2) molecules, in-class log_probabilities -3.480 -2.110 \t SMILES: C(=O)(CC)OC\n",
      "bit 1734: \t27 (1) molecules, in-class log_probabilities -3.833 -5.059 \t SMILES: C(CC)OC\n",
      "bit 1737: \t116 (22) molecules, in-class log_probabilities -3.053 -4.090 \t SMILES: C(C)(=O)O\n",
      "bit 1746: \t47 (6) molecules, in-class log_probabilities -4.765 -6.071 \t SMILES: C(=O)(O)CC\n",
      "bit 1757: \t27 (3) molecules, in-class log_probabilities -5.384 -6.764 \t SMILES: N(C)(C)C\n",
      "bit 1778: \t16 (1) molecules, in-class log_probabilities -3.864 -5.154 \t SMILES: c(cc)(cc)CC\n",
      "bit 1792: \t6 (6) molecules, in-class log_probabilities -6.637 -5.378 \t SMILES: C(Cl)CN\n",
      "bit 1805: \t21 (3) molecules, in-class log_probabilities -6.637 -5.260 \t SMILES: C(CC)C(C)O\n",
      "bit 1805: \t6 (6) molecules, in-class log_probabilities -6.637 -5.260 \t SMILES: c(c(c)N)c(c)[N+]\n",
      "bit 1809: \t416 (357) molecules, in-class log_probabilities -3.719 -2.270 \t SMILES: [N+](=O)([O-])c(c)c\n",
      "bit 1814: \t286 (252) molecules, in-class log_probabilities -4.239 -2.453 \t SMILES: c(cc)c(c)[N+]\n",
      "bit 1838: \t27 (26) molecules, in-class log_probabilities -5.133 -4.090 \t SMILES: [N+](=N)=[N-]\n",
      "bit 1845: \t34 (3) molecules, in-class log_probabilities -6.231 -5.154 \t SMILES: C(=O)(OC)c(c)c\n",
      "bit 1847: \t22 (1) molecules, in-class log_probabilities -3.962 -5.260 \t SMILES: C(C)(C)N\n",
      "bit 1853: \t26 (5) molecules, in-class log_probabilities -3.962 -5.059 \t SMILES: c(cc)c(C)c\n",
      "bit 1856: \t30 (25) molecules, in-class log_probabilities -5.384 -4.125 \t SMILES: c(cc)c(c)N\n",
      "bit 1875: \t23 (19) molecules, in-class log_probabilities -5.538 -6.764 \t SMILES: c(O)(cc)c(C)c\n",
      "bit 1880: \t36 (36) molecules, in-class log_probabilities -5.720 -6.764 \t SMILES: c(cc)(c(c)[N+])c(c)c\n",
      "bit 1884: \t6 (5) molecules, in-class log_probabilities -4.239 -5.665 \t SMILES: c(cc)(c(c)c)C(C)O\n",
      "bit 1884: \t25 (3) molecules, in-class log_probabilities -4.239 -5.665 \t SMILES: C(c)(C)(C)C\n",
      "bit 1884: \t61 (12) molecules, in-class log_probabilities -4.239 -5.665 \t SMILES: C(C)(C)(C)C\n",
      "bit 1894: \t26 (2) molecules, in-class log_probabilities -5.720 -6.764 \t SMILES: C(CC)=C(C)C\n",
      "bit 1912: \t122 (112) molecules, in-class log_probabilities -4.765 -3.223 \t SMILES: c(c(c)c)(c(c)c)c(c)c\n",
      "bit 1914: \t7 (6) molecules, in-class log_probabilities -4.932 -3.545 \t SMILES: C(CCl)N(C)C\n",
      "bit 1914: \t100 (90) molecules, in-class log_probabilities -4.932 -3.545 \t SMILES: c(c(c)c)c(c)c\n",
      "bit 1923: \t8 (1) molecules, in-class log_probabilities -4.239 -3.238 \t SMILES: C(C)(C)OC\n",
      "bit 1925: \t63 (52) molecules, in-class log_probabilities -5.027 -3.874 \t SMILES: C(=O)(c(c)c)c(c)c\n",
      "bit 1925: \t7 (7) molecules, in-class log_probabilities -5.027 -3.874 \t SMILES: C(C)(N)O\n",
      "bit 1936: \t7 (7) molecules, in-class log_probabilities -5.943 -4.413 \t SMILES: c(c(c)C)c(c)c\n",
      "bit 1940: \t186 (154) molecules, in-class log_probabilities -4.152 -2.882 \t SMILES: c(cc)(cc)[N+](=O)[O-]\n",
      "bit 1940: \t32 (6) molecules, in-class log_probabilities -4.152 -2.882 \t SMILES: C(CC)(C(C)C)C(C)(C)C\n",
      "bit 1950: \t161 (25) molecules, in-class log_probabilities -3.864 -5.378 \t SMILES: C(C)(C)C\n",
      "bit 1963: \t531 (448) molecules, in-class log_probabilities -2.887 -1.403 \t SMILES: [N+]\n",
      "bit 1963: \t6 (0) molecules, in-class log_probabilities -2.887 -1.403 \t SMILES: O(C(C)C)C(C)O\n",
      "bit 1984: \t19 (18) molecules, in-class log_probabilities -3.235 -1.805 \t SMILES: O(C)N\n",
      "bit 1984: \t316 (263) molecules, in-class log_probabilities -3.235 -1.805 \t SMILES: c(c)(c)c\n",
      "bit 2006: \t12 (1) molecules, in-class log_probabilities -5.250 -6.358 \t SMILES: C(c(c)c)C(C)N\n",
      "bit 2016: \t10 (0) molecules, in-class log_probabilities -4.845 -6.764 \t SMILES: N(C(C)=O)C(C)C\n",
      "bit 2022: \t30 (3) molecules, in-class log_probabilities -5.720 -6.764 \t SMILES: C(OC)(OC)C(C)O\n"
     ]
    }
   ],
   "source": [
    "images = []\n",
    "smiles = {}\n",
    "#find all substructures associated with enriched bits\n",
    "for bit in zip(position,enrichment):\n",
    "    i = 0\n",
    "    class0_prob = bit_probabilities[0][bit[0]]\n",
    "    class1_prob = bit_probabilities[1][bit[0]]\n",
    "    for info in train_info:\n",
    "        if info.has_key(bit[0]):\n",
    "            try:\n",
    "                m = data[train[i]][0]\n",
    "                atomId,radius = info[bit[0]][0]\n",
    "                env=Chem.FindAtomEnvironmentOfRadiusN(m,radius,atomId)\n",
    "                amap={}            \n",
    "                #submol = Chem.PathToSubmol(m,env,atomMap=amap)\n",
    "                ats = set([atomId])\n",
    "                for bidx in env:\n",
    "                    bond = m.GetBondWithIdx(bidx)\n",
    "                    ats.add(bond.GetBeginAtomIdx())\n",
    "                    ats.add(bond.GetEndAtomIdx())\n",
    "                smi = Chem.MolFragmentToSmiles(m,atomsToUse=list(ats),bondsToUse=env,rootedAtAtom=atomId)\n",
    "                #smi = Chem.FastFindRings(smi)\n",
    "                \n",
    "                if smiles.has_key(smi):\n",
    "                    num_molecules = smiles[smi][0]\n",
    "                    num_positives = smiles[smi][1]\n",
    "                    if data[train[i]][1] == 1: num_positives+=1\n",
    "                    smiles[smi] = (num_molecules+1,num_positives,bit[0],class0_prob,class1_prob)\n",
    "                else:\n",
    "                    num_positives = 0\n",
    "                    if data[train[i]][1] == 1: num_positives=1\n",
    "                    smiles[smi] = (1,num_positives,bit[0],class0_prob,class1_prob)\n",
    "                \n",
    "            except:\n",
    "                continue\n",
    "        i+=1\n",
    "\n",
    "        results=[]\n",
    "for smi in smiles.keys():\n",
    "    ratio = float(smiles[smi][1])/smiles[smi][0]\n",
    "    if smiles[smi][0] > 5 and (ratio < 0.2 or ratio > 0.8): #show only substructures occuring in at least 5 molecules, with either less than 20% or more than 80% active proportion\n",
    "        results.append((smiles[smi][2],smiles[smi][0],smiles[smi][1],(smiles[smi][3]),(smiles[smi][4]),smi))\n",
    "results.sort(key=lambda x: x[0])\n",
    "for line in results:        \n",
    "    print 'bit %i: \\t%i (%i) molecules, in-class log_probabilities %0.3f %0.3f \\t SMILES: %s'%line"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following section will demonstrate the construction of regression models using Random Forests from scikit-learn. The dataset consists of a csv file with precaclulated descriptors. The data is on the Adenosine A2A receptor (accession P29274) and consists of ~1500 compound obtained from ChEMBL_18. The tutorial uses pandas data frames. \n",
    "\n",
    "The data can be obtained from here : http://www.gjpvanwesten.nl/miscellaneous/A2A_Adenosine_set_mychembl.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os,sys\n",
    "import gzip\n",
    "import rdkit\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import AllChem\n",
    "import rdkit.rdBase\n",
    "from rdkit.Chem.MACCSkeys import GenMACCSKeys\n",
    "from rdkit import DataStructs\n",
    "from rdkit.DataStructs import BitVectToText\n",
    "from rdkit.Chem.Draw import IPythonConsole\n",
    "from rdkit.Chem import Draw\n",
    "from rdkit import DataStructs\n",
    "from rdkit.Chem import MCS as MCS\n",
    "from rdkit.Chem import Descriptors as Descriptors\n",
    "from rdkit.ML.Descriptors import MoleculeDescriptors\n",
    "import pandas as pd \n",
    "from rdkit.Chem import PandasTools as PandasTools\n",
    "from rdkit.Chem import Descriptors as Descriptors\n",
    "import pylab as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Definition of a function to remove near zero variance descriptors\n",
    "\n",
    "from collections import defaultdict\n",
    "\n",
    "def rmNearZeroVarDescs(descs,ratio):\n",
    "    colNb = descs.shape[1]\n",
    "    to_keep=[]\n",
    "    for j in range(0,colNb):\n",
    "        descs_now = descs[:,j]\n",
    "        d = defaultdict(int)\n",
    "        for i in descs_now:\n",
    "            d[i] += 1\n",
    "        freqs=sorted(d.iteritems(), key=lambda x: x[1], reverse=True)\n",
    "        if len(freqs) > 1: # there is more than one value..\n",
    "            most_frequent1 = freqs[0][1]\n",
    "            most_frequent2 = freqs[1][1]\n",
    "            if (np.array(most_frequent1)/np.array(most_frequent2)) < ratio:\n",
    "              to_keep.append(j)\n",
    "    return np.array(to_keep)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To demonstrate the use, the data is loaded and manipulated with pandas dataframes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "frame = pd.read_csv('/home/chembl/ipynb_workbench/A2A_Adenosine_set_mychembl.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data should be 1567 rows and 497 columns. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1567, 497)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>ACCESSION</th>\n",
       "      <th>MOLREGNO</th>\n",
       "      <th>PCHEMBL_VALUE</th>\n",
       "      <th>HBA</th>\n",
       "      <th>HBD</th>\n",
       "      <th>PSA</th>\n",
       "      <th>RTB</th>\n",
       "      <th>MED_CHEM_FRIENDLY</th>\n",
       "      <th>NUM_ALERTS</th>\n",
       "      <th>HEAVY_ATOMS</th>\n",
       "      <th>...</th>\n",
       "      <th>CM#FCFP_6#-835254725</th>\n",
       "      <th>CM#FCFP_6#238890401</th>\n",
       "      <th>CM#FCFP_6#522623800</th>\n",
       "      <th>CM#FCFP_6#426949827</th>\n",
       "      <th>CM#FCFP_6#930691991</th>\n",
       "      <th>CM#FCFP_6#-692413328</th>\n",
       "      <th>CM#FCFP_6#-703392038</th>\n",
       "      <th>CM#FCFP_6#238434898</th>\n",
       "      <th>adenosine_temp</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P29274</td>\n",
       "      <td>591791</td>\n",
       "      <td>7.24</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>103.02</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.239994</td>\n",
       "      <td>0.000006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P29274</td>\n",
       "      <td>439974</td>\n",
       "      <td>8.27</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>89.08</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.269871</td>\n",
       "      <td>0.000129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P29274</td>\n",
       "      <td>500067</td>\n",
       "      <td>6.48</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>106.24</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.480154</td>\n",
       "      <td>0.000154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P29274</td>\n",
       "      <td>1441734</td>\n",
       "      <td>6.91</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>106.30</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.909724</td>\n",
       "      <td>0.000276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P29274</td>\n",
       "      <td>1421790</td>\n",
       "      <td>5.96</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>70.67</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.959641</td>\n",
       "      <td>0.000359</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 497 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  ACCESSION  MOLREGNO  PCHEMBL_VALUE  HBA  HBD     PSA  RTB  MED_CHEM_FRIENDLY  NUM_ALERTS  HEAVY_ATOMS    ...     CM#FCFP_6#-835254725  CM#FCFP_6#238890401  CM#FCFP_6#522623800  CM#FCFP_6#426949827  CM#FCFP_6#930691991  CM#FCFP_6#-692413328  CM#FCFP_6#-703392038  CM#FCFP_6#238434898  adenosine_temp      diff\n",
       "0    P29274    591791           7.24    6    2  103.02    5                  1           0           25    ...                        0                    0                    0                    0                    0                     0                     0                    0        7.239994  0.000006\n",
       "1    P29274    439974           8.27    5    1   89.08    5                  1           0           30    ...                        0                    0                    0                    0                    0                     0                     0                    0        8.269871  0.000129\n",
       "2    P29274    500067           6.48    6    3  106.24   10                  1           1           39    ...                        0                    0                    0                    0                    0                     0                     0                    0        6.480154  0.000154\n",
       "3    P29274   1441734           6.91    6    2  106.30    7                  1           1           25    ...                        0                    0                    0                    0                    0                     0                     0                    0        6.909724  0.000276\n",
       "4    P29274   1421790           5.96    3    2   70.67    4                  1           0           17    ...                        0                    0                    0                    0                    0                     0                     0                    0        5.959641  0.000359\n",
       "\n",
       "[5 rows x 497 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The full frame is copied to form the x variables. From this copy the y variable and columns not required for learning are dropped. Subsequently the y variable is copied to a seperate dataframe. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['HBA',\n",
       " 'HBD',\n",
       " 'PSA',\n",
       " 'RTB',\n",
       " 'MED_CHEM_FRIENDLY',\n",
       " 'NUM_ALERTS',\n",
       " 'HEAVY_ATOMS',\n",
       " 'FULL_MWT',\n",
       " 'AROMATIC_RINGS',\n",
       " 'NUM_RO5_VIOLATIONS',\n",
       " 'RO3_PASS',\n",
       " 'Acid',\n",
       " 'Base',\n",
       " 'Unknown',\n",
       " 'Ki',\n",
       " 'Kd',\n",
       " 'IC50',\n",
       " 'EC50',\n",
       " 'AC50',\n",
       " 'Other_activity_type',\n",
       " 'LogP',\n",
       " 'Num_Atoms',\n",
       " 'Num_Hydrogens',\n",
       " 'Num_ExplicitBonds',\n",
       " 'Num_PositiveAtoms',\n",
       " 'Num_NegativeAtoms',\n",
       " 'Num_RingBonds',\n",
       " 'Num_RotatableBonds',\n",
       " 'Num_AromaticBonds',\n",
       " 'Num_BridgeBonds',\n",
       " 'Num_Rings',\n",
       " 'Num_RingAssemblies',\n",
       " 'Num_Chains',\n",
       " 'Num_ChainAssemblies',\n",
       " 'Num_MetalAtoms',\n",
       " 'Num_PiBonds',\n",
       " 'Num_StereoAtoms',\n",
       " 'Num_StereoBonds',\n",
       " 'Num_SingleBonds',\n",
       " 'Num_DoubleBonds',\n",
       " 'Num_TripleBonds',\n",
       " 'Num_AliphaticSingleBonds',\n",
       " 'Num_HydrogenBonds',\n",
       " 'Num_TerminalRotomers',\n",
       " 'Num_H_Acceptors',\n",
       " 'Num_H_Donors',\n",
       " 'H_Count',\n",
       " 'C_Count',\n",
       " 'N_Count',\n",
       " 'O_Count',\n",
       " 'F_Count',\n",
       " 'P_Count',\n",
       " 'S_Count',\n",
       " 'Cl_Count',\n",
       " 'Br_Count',\n",
       " 'I_Count',\n",
       " 'Molecular_Solubility',\n",
       " 'Molecular_Volume',\n",
       " 'Molecular_SASA',\n",
       " 'Molecular_PolarSASA_Fraction',\n",
       " 'Solubility',\n",
       " 'Total_Atoms',\n",
       " 'Num_AliphaticRings',\n",
       " 'Heavy_Atom_Bonds',\n",
       " 'Hydrogen_Atom_Bonds',\n",
       " 'SingleBonds_Frac',\n",
       " 'DoubleBonds_Frac',\n",
       " 'AromaticBonds_Frac',\n",
       " 'BridgeBonds_Frac',\n",
       " 'StereoBonds_Frac',\n",
       " 'RingBonds_Frac',\n",
       " 'Aliphatic_Ringbonds_Frac',\n",
       " 'RotatableBonds_Frac',\n",
       " 'Num_Halogens',\n",
       " 'Carbon_fraction',\n",
       " 'Hydrogen_fraction',\n",
       " 'Nitrogen_fraction',\n",
       " 'Heteroatom_fraction',\n",
       " 'Sulphur_fraction',\n",
       " 'Phosphorus_fraction',\n",
       " 'Halogen_fraction',\n",
       " 'OtherAtom_fraction',\n",
       " 'StereoAtom_Fraction',\n",
       " 'PositiveAtom_Fraction',\n",
       " 'NegativeAtom_Fraction',\n",
       " 'H_Acceptors_Fraction',\n",
       " 'H_Donors_fraction',\n",
       " 'Molecular_Weight',\n",
       " 'Lipinski_Pass',\n",
       " 'Molecular_PolarSurfaceArea',\n",
       " 'Molecular_PolarSurfaceArea_Fraction',\n",
       " 'CM#FCFP_6#-65982584',\n",
       " 'CM#FCFP_6#1220222990',\n",
       " 'CM#FCFP_6#-1982171447',\n",
       " 'CM#FCFP_6#-2001551693',\n",
       " 'CM#FCFP_6#-1771696584',\n",
       " 'CM#FCFP_6#1979799016',\n",
       " 'CM#FCFP_6#-1115205388',\n",
       " 'CM#FCFP_6#-65868451',\n",
       " 'CM#FCFP_6#-492065043',\n",
       " 'CM#FCFP_6#-1642107661',\n",
       " 'CM#FCFP_6#-189641276',\n",
       " 'CM#FCFP_6#1462500218',\n",
       " 'CM#FCFP_6#1531826645',\n",
       " 'CM#FCFP_6#-293671387',\n",
       " 'CM#FCFP_6#-1071560772',\n",
       " 'CM#FCFP_6#27378098',\n",
       " 'CM#FCFP_6#-1001873921',\n",
       " 'CM#FCFP_6#193706886',\n",
       " 'CM#FCFP_6#1164792596',\n",
       " 'CM#FCFP_6#-1146141487',\n",
       " 'CM#FCFP_6#285374308',\n",
       " 'CM#FCFP_6#-498663958',\n",
       " 'CM#FCFP_6#-1479282148',\n",
       " 'CM#FCFP_6#-971683173',\n",
       " 'CM#FCFP_6#-699707120',\n",
       " 'CM#FCFP_6#1253847022',\n",
       " 'CM#FCFP_6#391068706',\n",
       " 'CM#FCFP_6#1982718309',\n",
       " 'CM#FCFP_6#1703564034',\n",
       " 'CM#FCFP_6#1140530667',\n",
       " 'CM#FCFP_6#187601626',\n",
       " 'CM#FCFP_6#-1459071555',\n",
       " 'CM#FCFP_6#-434116010',\n",
       " 'CM#FCFP_6#230514996',\n",
       " 'CM#FCFP_6#-936120204',\n",
       " 'CM#FCFP_6#372254866',\n",
       " 'CM#FCFP_6#-917072037',\n",
       " 'CM#FCFP_6#1692787152',\n",
       " 'CM#FCFP_6#73264552',\n",
       " 'CM#FCFP_6#1827402357',\n",
       " 'CM#FCFP_6#-327407377',\n",
       " 'CM#FCFP_6#-67620761',\n",
       " 'CM#FCFP_6#-1941818284',\n",
       " 'CM#FCFP_6#-1286158246',\n",
       " 'CM#FCFP_6#-54973313',\n",
       " 'CM#FCFP_6#1779217362',\n",
       " 'CM#FCFP_6#-1526113663',\n",
       " 'CM#FCFP_6#-2137417524',\n",
       " 'CM#FCFP_6#-2074416831',\n",
       " 'CM#FCFP_6#-1073796186',\n",
       " 'CM#FCFP_6#-413791989',\n",
       " 'CM#FCFP_6#-1532598714',\n",
       " 'CM#FCFP_6#-378110045',\n",
       " 'CM#FCFP_6#365650923',\n",
       " 'CM#FCFP_6#-1059904848',\n",
       " 'CM#FCFP_6#-1005932975',\n",
       " 'CM#FCFP_6#-972801681',\n",
       " 'CM#FCFP_6#-969679530',\n",
       " 'CM#FCFP_6#1685259762',\n",
       " 'CM#FCFP_6#-2090379603',\n",
       " 'CM#FCFP_6#835252212',\n",
       " 'CM#FCFP_6#1618940161',\n",
       " 'CM#FCFP_6#-1968861350',\n",
       " 'CM#FCFP_6#461422072',\n",
       " 'CM#FCFP_6#1234403192',\n",
       " 'CM#FCFP_6#1430777963',\n",
       " 'CM#FCFP_6#1976692152',\n",
       " 'CM#FCFP_6#69791780',\n",
       " 'CM#FCFP_6#1791505287',\n",
       " 'CM#FCFP_6#1635208540',\n",
       " 'CM#FCFP_6#-1401467450',\n",
       " 'CM#FCFP_6#1972424121',\n",
       " 'CM#FCFP_6#-1670510168',\n",
       " 'CM#FCFP_6#254350405',\n",
       " 'CM#FCFP_6#-645813023',\n",
       " 'CM#FCFP_6#603150292',\n",
       " 'CM#FCFP_6#684199633',\n",
       " 'CM#FCFP_6#480222659',\n",
       " 'CM#FCFP_6#1603387139',\n",
       " 'CM#FCFP_6#-1637545813',\n",
       " 'CM#FCFP_6#-2129824562',\n",
       " 'CM#FCFP_6#-330170411',\n",
       " 'CM#FCFP_6#1213951251',\n",
       " 'CM#FCFP_6#1284402681',\n",
       " 'CM#FCFP_6#623390154',\n",
       " 'CM#FCFP_6#1148911516',\n",
       " 'CM#FCFP_6#1135800885',\n",
       " 'CM#FCFP_6#362731743',\n",
       " 'CM#FCFP_6#-1577786275',\n",
       " 'CM#FCFP_6#1868119574',\n",
       " 'CM#FCFP_6#1068095685',\n",
       " 'CM#FCFP_6#94632228',\n",
       " 'CM#FCFP_6#929973182',\n",
       " 'CM#FCFP_6#-1420307139',\n",
       " 'CM#FCFP_6#444284352',\n",
       " 'CM#FCFP_6#-2080611022',\n",
       " 'CM#FCFP_6#592529482',\n",
       " 'CM#FCFP_6#-222683903',\n",
       " 'CM#FCFP_6#426057100',\n",
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       " 'CM#FCFP_6#-692413328',\n",
       " 'CM#FCFP_6#-703392038',\n",
       " 'CM#FCFP_6#238434898']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = frame\n",
    "PandasTools.AddMoleculeColumnToFrame(x,smilesCol='Smiles',molCol='mol',includeFingerprints=True)\n",
    "\n",
    "#remove properties not required for learning \n",
    "x = x.drop('Smiles',1)\n",
    "x = x.drop('mol',1)\n",
    "x = x.drop('ACCESSION',1)\n",
    "x = x.drop('MOLREGNO',1)\n",
    "x = x.drop('PCHEMBL_VALUE',1)\n",
    "x = x.drop('adenosine_temp' ,1)\n",
    "x = x.drop('diff' ,1)\n",
    "\n",
    "#output variables are transferred to a frame called y\n",
    "y = frame.PCHEMBL_VALUE\n",
    "\n",
    "#use the column names as labels for later\n",
    "Desc_values = list(x.columns)\n",
    "Desc_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#set to floating point type\n",
    "x = x.astype('float64')\n",
    "# optional : visualize x.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Optional : check for missing values\n",
    "#pd.isnull(x).any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn import tree, grid_search,cross_validation\n",
    "from sklearn.cross_validation import StratifiedKFold\n",
    "from sklearn.cross_validation import KFold\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import explained_variance_score\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn import preprocessing\n",
    "from sklearn.grid_search import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Divide the original dataset into training (80%) and test (20%) set\n",
    "X_train2, X_test2, y_train2, y_test2 = cross_validation.train_test_split(x, y, test_size=0.2, random_state=23)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:   39.7s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80% - 20% completed\n",
      "Best score: 0.83\n",
      "Training set performance using best parameters ({'n_estimators': 250, 'oob_score': True})\n",
      "Explained variance (Internal): 0.98161\n",
      "MSE (Internal): 0.01817\n",
      "Explained variance (External): 0.85877\n",
      "MSE (External): 0.13041\n"
     ]
    }
   ],
   "source": [
    "#import and Learn the model\n",
    "from sklearn import tree\n",
    "from sklearn import ensemble\n",
    "from sklearn.cross_validation import KFold\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "\n",
    "\n",
    "custom_forest = ensemble.RandomForestRegressor()\n",
    "params = {'oob_score':[True],'n_estimators':[250]}\n",
    "\n",
    "\n",
    "cv = KFold(n=len(X_train2),n_folds=10,shuffle=True)\n",
    "cv_stratified = StratifiedKFold(y_train2, n_folds=5)\n",
    "gs = GridSearchCV(custom_forest, params, cv=cv_stratified,verbose=1,refit=True)\n",
    "gs.fit(X_train2,y_train2)\n",
    "ztest2 = gs.predict(X_test2)\n",
    "ztest_train2 = gs.predict(X_train2)\n",
    "best_forest2 = gs.best_estimator_\n",
    "\n",
    "print '80% - 20% completed' \n",
    "print 'Best score: %0.2f'%gs.best_score_\n",
    "print 'Training set performance using best parameters (%s)'%gs.best_params_\n",
    "\n",
    "print 'Explained variance (Internal): %0.5f'%(best_forest2.score(X_train2,y_train2))\n",
    "print 'MSE (Internal): %0.5f'%(mean_squared_error(y_train2,ztest_train2))\n",
    "print 'Explained variance (External): %0.5f'%(best_forest2.score(X_test2,y_test2))\n",
    "print 'MSE (External): %0.5f'%(mean_squared_error(y_test2,ztest2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4.7300000000000004, 9.5199999999999996)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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O7nq3mZ2ahHEcPEpkENWNsYBfAVwNlECJKuE/r/9PXvnLK5SWlMJ9wFqMzfzn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hFEgCNRFMMFRs2IZtDQTFpMrMqsmbFYLXi+4Ik1RXY8V6RiUEOhCOedQlEoZLKR2fbpyl8fkhxu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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa8bd1664d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure,(plt5,plt6) = pylab.subplots(1,2)\n",
    "figure.set_size_inches(15,5)\n",
    "plt5.plot(x-1,x,c='red', linewidth=1,zorder=1)\n",
    "plt5.plot(x+1,x,c='red', linewidth=1,zorder=1)\n",
    "plt5.scatter(y_test2,ztest2,c='green',label='test',zorder=2)\n",
    "minn = np.min(np.concatenate([y_test2,ztest2],axis=0))\n",
    "maxx = np.max(np.concatenate([y_test2,ztest2],axis=0))\n",
    "plt5.set_ylim([minn,maxx])\n",
    "plt5.set_xlim([minn,maxx])\n",
    "plt5.set_title(\"Prediction test set 20%\")\n",
    "\n",
    "plt6.plot(x-1,x,c='red', linewidth=1,zorder=1)\n",
    "plt6.plot(x+1,x,c='red', linewidth=1,zorder=1)\n",
    "plt6.scatter(y_train2,ztest_train2,c='green',label='test',zorder=2)\n",
    "plt6.set_title(\"Prediction training set 80%\")\n",
    "minn = np.min(np.concatenate([y_train2,ztest_train2],axis=0))\n",
    "maxx = np.max(np.concatenate([y_train2,ztest_train2],axis=0))\n",
    "plt6.set_ylim([minn,maxx])\n",
    "plt6.set_xlim([minn,maxx])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model interpretation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will use the best predictor to try and understand the data. We do so by directly interpreting the random forest (obviously these values can also be output to a text file for later interpretation.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "491\n"
     ]
    },
    {
     "data": {
      "image/png": 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D969kLRKvJDsDuwM/WNPHSpIkSdJctVEKkbRDIz8NHNH2uEmSJEmSRjB/hH2uAnYauL9D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\nERERERHRYv8DlyvEYrsNtHMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa8be3a3a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_imp = best_forest2.feature_importances_\n",
    "print len(feat_imp)\n",
    "fig,a = pyplot.subplots(1,1)\n",
    "fig.set_size_inches(15,5)\n",
    "createImportancePlot(a,list(x.columns),feat_imp,\"Most important descriptors in the optimized tree\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross validated parameters\n",
      "Cross_val_MSE:  0.0181688251179\n",
      "Cross_val_Explained_variance:  0.981619146069\n",
      "Cross_val_R2_score:  0.981607064373\n",
      "\n",
      "Externally validated parameters\n",
      "Ext_val_MSE:  0.130414625752\n",
      "Ext_val_Explained_variance:  0.858772996164\n",
      "Ext_val_R2_score:  0.858765598653\n"
     ]
    }
   ],
   "source": [
    "print \"Cross validated parameters\" \n",
    "Cross_val_MSE_80 = mean_squared_error(y_train2, ztest_train2)\n",
    "print \"Cross_val_MSE: \", Cross_val_MSE_80\n",
    "Cross_val_Exp_var_80 = explained_variance_score(y_train2, ztest_train2)\n",
    "print \"Cross_val_Explained_variance: \" , Cross_val_Exp_var_80\n",
    "Cross_val_R2_80 = r2_score(y_train2, ztest_train2)\n",
    "print \"Cross_val_R2_score: \" , Cross_val_R2_80\n",
    "print \"\"\n",
    "print 'Externally validated parameters'\n",
    "Ext_val_MSE_80 = extvmetrics2 = mean_squared_error(y_test2 , ztest2)\n",
    "print \"Ext_val_MSE: \", Ext_val_MSE_80 \n",
    "Ext_val_Exp_var_80 =explained_variance_score(y_test2 , ztest2)\n",
    "print \"Ext_val_Explained_variance: \" , Ext_val_Exp_var_80\n",
    "Ext_val_R2_80 = r2_score(y_test2 , ztest2)\n",
    "print \"Ext_val_R2_score: \" , Ext_val_R2_80 "
   ]
  },
  {
   "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>Parameter</th>\n",
       "      <th>Cross Validation</th>\n",
       "      <th>External Validation</th>\n",
       "      <th>Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mean Squared Error</td>\n",
       "      <td>0.018169</td>\n",
       "      <td>0.130415</td>\n",
       "      <td>0.112246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Explained variance</td>\n",
       "      <td>0.981619</td>\n",
       "      <td>0.858773</td>\n",
       "      <td>-0.122846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>R2 Score</td>\n",
       "      <td>0.981607</td>\n",
       "      <td>0.858766</td>\n",
       "      <td>-0.122841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "            Parameter  Cross Validation  External Validation  Difference\n",
       "0  Mean Squared Error          0.018169             0.130415    0.112246\n",
       "1  Explained variance          0.981619             0.858773   -0.122846\n",
       "2            R2 Score          0.981607             0.858766   -0.122841"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Validation2 = pd.DataFrame.from_items([ ('Parameter', ['Mean Squared Error' , 'Explained variance' , 'R2 Score']), ('Cross Validation', [Cross_val_MSE_80, Cross_val_Exp_var_80, Cross_val_R2_80 ]), ('External Validation', [Ext_val_MSE_80, Ext_val_Exp_var_80, Ext_val_R2_80 ]), ('Difference', [Ext_val_MSE_80 - Cross_val_MSE_80, Ext_val_Exp_var_80 - Cross_val_Exp_var_80, Ext_val_R2_80 - Cross_val_R2_80])])\n",
    "Validation2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Learning Curves"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be beneficial for a better understanding of the data and the performance gained by the machine learning algorithm to create learning curves. The concept it to create several splits of your data between training and tests sets. Subsequently multiple models are trained and model performance is plotted against training set size. As this can be computationally expensive it is here included as the last section. Again, depending on the hardware this can take some time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Divide the original dataset into training 20 - testing 80, and a 50-50 set\n",
    "X_traina, X_testa, y_traina, y_testa = cross_validation.train_test_split(x, y, test_size=0.95, random_state=23)\n",
    "X_train, X_test, y_train, y_test = cross_validation.train_test_split(x, y, test_size=0.8, random_state=23)\n",
    "X_train1, X_test1, y_train1, y_test1 = cross_validation.train_test_split(x, y, test_size=0.5, random_state=23)\n",
    "X_trainb, X_testb, y_trainb, y_testb = cross_validation.train_test_split(x, y, test_size=0.05, random_state=23)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 1 candidates, totalling 3 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    1.1s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 1 candidates, totalling 3 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    1.1s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5% - 95% completed\n",
      "Best score: -0.02\n",
      "Training set performance using best parameters ({'min_samples_split': 8, 'n_estimators': 200, 'max_depth': 20, 'min_samples_leaf': 1})\n",
      "Explained variance (Internal): 0.88566\n",
      "MSE (Internal): 0.11882\n",
      "Explained variance (External): 0.45760\n",
      "MSE (External): 0.52709\n",
      "Fitting 3 folds for each of 1 candidates, totalling 3 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    2.9s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20% - 80% completed\n",
      "Best score: 0.66\n",
      "Training set performance using best parameters ({'min_samples_split': 8, 'n_estimators': 200, 'max_depth': 20, 'min_samples_leaf': 1})\n",
      "Explained variance (Internal): 0.94140\n",
      "MSE (Internal): 0.06155\n",
      "Explained variance (External): 0.68555\n",
      "MSE (External): 0.30023\n",
      "Fitting 3 folds for each of 1 candidates, totalling 3 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    7.1s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50% - 50% completed\n",
      "Best score: 0.76\n",
      "Training set performance using best parameters ({'min_samples_split': 8, 'n_estimators': 200, 'max_depth': 20, 'min_samples_leaf': 1})\n",
      "Explained variance (Internal): 0.95444\n",
      "MSE (Internal): 0.04562\n",
      "Explained variance (External): 0.79528\n",
      "MSE (External): 0.19427\n",
      "Fitting 3 folds for each of 1 candidates, totalling 3 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:   13.9s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95% - 5% completed\n",
      "Best score: 0.81\n",
      "Training set performance using best parameters ({'min_samples_split': 8, 'n_estimators': 200, 'max_depth': 20, 'min_samples_leaf': 1})\n",
      "Explained variance (Internal): 0.96426\n",
      "MSE (Internal): 0.03544\n",
      "Explained variance (External): 0.75189\n",
      "MSE (External): 0.16341\n"
     ]
    }
   ],
   "source": [
    "params = {'min_samples_split': [8], 'max_depth': [20], 'min_samples_leaf': [1],'n_estimators':[200]}\n",
    "cv = KFold(n=len(X_traina),n_folds=10,shuffle=True)\n",
    "cv_stratified = StratifiedKFold(y_traina)\n",
    "gs = GridSearchCV(custom_forest, params, cv=cv_stratified,verbose=1,refit=True)\n",
    "gs.fit(X_traina,y_traina)\n",
    "gs = GridSearchCV(custom_forest, params, cv=cv_stratified,verbose=1,refit=True)\n",
    "gs.fit(X_traina,y_traina)\n",
    "ztesta = gs.predict(X_testa)\n",
    "ztest_traina = gs.predict(X_traina)\n",
    "best_foresta = gs.best_estimator_\n",
    "\n",
    "#print intermediate results to keep an eye on progress\n",
    "print '5% - 95% completed' \n",
    "print 'Best score: %0.2f'%gs.best_score_\n",
    "print 'Training set performance using best parameters (%s)'%gs.best_params_\n",
    "best_treemodel = gs.best_estimator_\n",
    "print 'Explained variance (Internal): %0.5f'%(best_foresta.score(X_traina,y_traina))\n",
    "print 'MSE (Internal): %0.5f'%(mean_squared_error(y_traina,ztest_traina))\n",
    "print 'Explained variance (External): %0.5f'%(best_foresta.score(X_testa,y_testa))\n",
    "print 'MSE (External): %0.5f'%(mean_squared_error(y_testa,ztesta))\n",
    "\n",
    "\n",
    "cv = KFold(n=len(X_train),n_folds=10,shuffle=True)\n",
    "cv_stratified = StratifiedKFold(y_train)\n",
    "gs = GridSearchCV(custom_forest, params, cv=cv_stratified,verbose=1,refit=True)\n",
    "gs.fit(X_train,y_train)\n",
    "ztest = gs.predict(X_test)\n",
    "ztest_train = gs.predict(X_train)\n",
    "best_forest = gs.best_estimator_\n",
    "\n",
    "#print intermediate results to keep an eye on progress\n",
    "print '20% - 80% completed' \n",
    "print 'Best score: %0.2f'%gs.best_score_\n",
    "print 'Training set performance using best parameters (%s)'%gs.best_params_\n",
    "best_treemodel = gs.best_estimator_\n",
    "print 'Explained variance (Internal): %0.5f'%(best_forest.score(X_train,y_train))\n",
    "print 'MSE (Internal): %0.5f'%(mean_squared_error(y_train,ztest_train))\n",
    "print 'Explained variance (External): %0.5f'%(best_forest.score(X_test,y_test))\n",
    "print 'MSE (External): %0.5f'%(mean_squared_error(y_test,ztest))\n",
    "\n",
    "cv = KFold(n=len(X_train1),n_folds=10,shuffle=True)\n",
    "cv_stratified = StratifiedKFold(y_train1)\n",
    "gs = GridSearchCV(custom_forest, params, cv=cv_stratified,verbose=1,refit=True)\n",
    "gs.fit(X_train1,y_train1)\n",
    "ztest1 = gs.predict(X_test1)\n",
    "ztest_train1 = gs.predict(X_train1)\n",
    "best_forest1 = gs.best_estimator_\n",
    "\n",
    "#print intermediate results to keep an eye on progress\n",
    "print '50% - 50% completed' \n",
    "print 'Best score: %0.2f'%gs.best_score_\n",
    "print 'Training set performance using best parameters (%s)'%gs.best_params_\n",
    "\n",
    "print 'Explained variance (Internal): %0.5f'%(best_forest1.score(X_train1,y_train1))\n",
    "print 'MSE (Internal): %0.5f'%(mean_squared_error(y_train1,ztest_train1))\n",
    "print 'Explained variance (External): %0.5f'%(best_forest1.score(X_test1,y_test1))\n",
    "print 'MSE (External): %0.5f'%(mean_squared_error(y_test1,ztest1))\n",
    "\n",
    "cv = KFold(n=len(X_trainb),n_folds=10,shuffle=True)\n",
    "cv_stratified = StratifiedKFold(y_trainb)\n",
    "gs = GridSearchCV(custom_forest, params, cv=cv_stratified,verbose=1,refit=True)\n",
    "gs.fit(X_trainb,y_trainb)\n",
    "ztestb = gs.predict(X_testb)\n",
    "ztest_trainb = gs.predict(X_trainb)\n",
    "best_forestb = gs.best_estimator_\n",
    "\n",
    "#print intermediate results to keep an eye on progress\n",
    "print '95% - 5% completed' \n",
    "print 'Best score: %0.2f'%gs.best_score_\n",
    "print 'Training set performance using best parameters (%s)'%gs.best_params_\n",
    "\n",
    "print 'Explained variance (Internal): %0.5f'%(best_forestb.score(X_trainb,y_trainb))\n",
    "print 'MSE (Internal): %0.5f'%(mean_squared_error(y_trainb,ztest_trainb))\n",
    "print 'Explained variance (External): %0.5f'%(best_forestb.score(X_testb,y_testb))\n",
    "print 'MSE (External): %0.5f'%(mean_squared_error(y_testb,ztestb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4.7300000000000004, 9.5199999999999996)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Z6UueCIfDLD1rKZGrI7AYeBMCbQEOvXSIUCg0092bMkoptNYq/ZYCiD4KgpABYmzlhZgu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\nMz2J95CaztiMfh9/p7/2zPWozs7ONPMPq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uvrwYnJyQln9PsW573ooouInIjk5OKUi8Nd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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa8b2cade10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YGMA3z5cQNPrm+XC73ZYzV6WUTiEIglA0VHCwBXFruC4JE3aHYRiC7UGe/+Xz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\nMVEAwY+DYAG+BvweqE5uV1hfSPPzzUyePDnnGAlyW7tqLUu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3ELFFMuquIgcjRPOj4AeH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\nfa2mPbmmy67WKX4GbDJqmG6+5Wa++u2vw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PBdCwldGbJYxPvm+fiv/f/F0qVLUx7n+eef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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa8ae052fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9q53yTeXYt9rF4l0QBCFRiEYCvZOL4cFNS0tLIBhrv7wd1xSXyUqNwzgOHiVE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\nzPmEjgpx+KPDGGOo+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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa8a7aedb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PI0mXECJ/lGAwEUIIIfqlBL+QjDey+IKfjjkd+L/gx9PoGbQRr/b2dspqy5Jqtspqy0wt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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa8b4c80b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create measured vs predicted plots\n",
    "#plt.xkcd()\n",
    "figure,(plt1,plt2) = pylab.subplots(1,2)\n",
    "figure.set_size_inches(15,5)\n",
    "plt1.plot(x-1,x,c='red',linewidth=1,zorder=1)\n",
    "plt1.plot(x+1,x,c='red',linewidth=1,zorder=1)\n",
    "plt1.scatter(y_test,ztest,c='green',label='test',zorder=2)\n",
    "minn = np.min(np.concatenate([y_test,ztest],axis=0))\n",
    "maxx = np.max(np.concatenate([y_test,ztest],axis=0))\n",
    "plt1.set_ylim([minn,maxx])\n",
    "plt1.set_xlim([minn,maxx])\n",
    "plt1.set_title(\"Prediction test set 80%\")\n",
    "\n",
    "plt2.plot(x-1,x,c='red',linewidth=1,zorder=1)\n",
    "plt2.plot(x+1,x,c='red',linewidth=1,zorder=1)\n",
    "plt2.scatter(y_train,ztest_train,c='green',label='test',zorder=2)\n",
    "plt2.set_title(\"Prediction training set 20%\")\n",
    "minn = np.min(np.concatenate([y_train,ztest_train],axis=0))\n",
    "maxx = np.max(np.concatenate([y_train,ztest_train],axis=0))\n",
    "plt2.set_ylim([minn,maxx])\n",
    "plt2.set_xlim([minn,maxx])\n",
    "\n",
    "figure,(plt3,plt4) = pylab.subplots(1,2)\n",
    "figure.set_size_inches(15,5)\n",
    "plt3.plot(x-1,x,c='red', linewidth=1,zorder=1)\n",
    "plt3.plot(x+1,x,c='red', linewidth=1,zorder=1)\n",
    "plt3.scatter(y_test1,ztest1,c='green',label='test',zorder=2)\n",
    "minn = np.min(np.concatenate([y_test,ztest],axis=0))\n",
    "maxx = np.max(np.concatenate([y_test,ztest],axis=0))\n",
    "plt3.set_ylim([minn,maxx])\n",
    "plt3.set_xlim([minn,maxx])\n",
    "plt3.set_title(\"Prediction test set 50%\")\n",
    "\n",
    "plt4.plot(x-1,x,c='red', linewidth=1,zorder=1)\n",
    "plt4.plot(x+1,x,c='red', linewidth=1,zorder=1)\n",
    "plt4.scatter(y_train1,ztest_train1,c='green',label='test',zorder=2)\n",
    "plt4.set_title(\"Prediction training set 50%\")\n",
    "minn = np.min(np.concatenate([y_train,ztest_train],axis=0))\n",
    "maxx = np.max(np.concatenate([y_train,ztest_train],axis=0))\n",
    "plt4.set_ylim([minn,maxx])\n",
    "plt4.set_xlim([minn,maxx])\n",
    "\n",
    "\n",
    "figure,(plt5,plt6) = pylab.subplots(1,2)\n",
    "figure.set_size_inches(15,5)\n",
    "plt5.plot(x-1,x,c='red', linewidth=1,zorder=1)\n",
    "plt5.plot(x+1,x,c='red', linewidth=1,zorder=1)\n",
    "plt5.scatter(y_test2,ztest2,c='green',label='test',zorder=2)\n",
    "minn = np.min(np.concatenate([y_test,ztest],axis=0))\n",
    "maxx = np.max(np.concatenate([y_test,ztest],axis=0))\n",
    "plt5.set_ylim([minn,maxx])\n",
    "plt5.set_xlim([minn,maxx])\n",
    "plt5.set_title(\"Prediction test set 20%\")\n",
    "\n",
    "plt6.plot(x-1,x,c='red', linewidth=1,zorder=1)\n",
    "plt6.plot(x+1,x,c='red', linewidth=1,zorder=1)\n",
    "plt6.scatter(y_train2,ztest_train2,c='green',label='test',zorder=2)\n",
    "plt6.set_title(\"Prediction training set 80%\")\n",
    "minn = np.min(np.concatenate([y_train,ztest_train],axis=0))\n",
    "maxx = np.max(np.concatenate([y_train,ztest_train],axis=0))\n",
    "plt6.set_ylim([minn,maxx])\n",
    "plt6.set_xlim([minn,maxx])\n",
    "\n",
    "figure,(plt7,plt8) = pylab.subplots(1,2)\n",
    "figure.set_size_inches(15,5)\n",
    "plt7.plot(x-1,x,c='red', linewidth=1,zorder=1)\n",
    "plt7.plot(x+1,x,c='red', linewidth=1,zorder=1)\n",
    "plt7.scatter(y_testb,ztestb,c='green',label='test',zorder=2)\n",
    "minn = np.min(np.concatenate([y_test,ztest],axis=0))\n",
    "maxx = np.max(np.concatenate([y_test,ztest],axis=0))\n",
    "plt7.set_ylim([minn,maxx])\n",
    "plt7.set_xlim([minn,maxx])\n",
    "plt7.set_title(\"Prediction test set 5%\")\n",
    "\n",
    "plt8.plot(x-1,x,c='red', linewidth=1,zorder=1)\n",
    "plt8.plot(x+1,x,c='red', linewidth=1,zorder=1)\n",
    "plt8.scatter(y_trainb,ztest_trainb,c='green',label='test',zorder=2)\n",
    "plt8.set_title(\"Prediction training set 80%\")\n",
    "minn = np.min(np.concatenate([y_train,ztest_train],axis=0))\n",
    "maxx = np.max(np.concatenate([y_train,ztest_train],axis=0))\n",
    "plt8.set_ylim([minn,maxx])\n",
    "plt8.set_xlim([minn,maxx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5%-95%\n",
      "Cross validated parameters\n",
      "Cross_val_MSE:  0.118817397541\n",
      "Cross_val_R2_score:  0.885664691266\n",
      "\n",
      "Externally validated parameters\n",
      "Ext_val_MSE:  0.527086143766\n",
      "Ext_val_R2_score:  0.457598777352\n",
      "\n",
      "\n",
      "20%-80%\n",
      "Cross_val_MSE:  0.0615506846579\n",
      "Cross_val_Explained_variance:  0.9413985508\n",
      "Cross_val_R2_score:  0.941398548178\n",
      "\n",
      "Ext_val_MSE:  0.300225679856\n",
      "Ext_val_Explained_variance:  0.690357044108\n",
      "Ext_val_R2_score:  0.685551290939\n",
      "\n",
      "\n",
      "50%-50%\n",
      "Cross_val_MSE:  0.045621674341\n",
      "Cross_val_Explained_variance:  0.954451647962\n",
      "Cross_val_R2_score:  0.954443212232\n",
      "\n",
      "Ext_val_MSE:  0.300225679856\n",
      "Ext_val_Explained_variance:  0.798075529688\n",
      "Ext_val_R2_score:  0.795279712506\n",
      "\n",
      "\n",
      "95%-5%\n",
      "Cross validated parameters\n",
      "Cross_val_MSE:  0.0354393897648\n",
      "Cross_val_R2_score:  0.964263720486\n",
      "\n",
      "Externally validated parameters\n",
      "Ext_val_MSE:  0.16341375919\n",
      "Ext_val_R2_score:  0.751889460253\n"
     ]
    }
   ],
   "source": [
    "print '5%-95%'\n",
    "print \"Cross validated parameters\" \n",
    "Cross_val_MSE_05 = mean_squared_error(y_traina, ztest_traina)\n",
    "print \"Cross_val_MSE: \", Cross_val_MSE_05\n",
    "Cross_val_R2_05 = r2_score(y_traina, ztest_traina)\n",
    "print \"Cross_val_R2_score: \" , Cross_val_R2_05\n",
    "print \"\"\n",
    "print 'Externally validated parameters'\n",
    "Ext_val_MSE_05 = extvmetrics2 = mean_squared_error(y_testa , ztesta)\n",
    "print \"Ext_val_MSE: \", Ext_val_MSE_05 \n",
    "Ext_val_R2_05 = r2_score(y_testa , ztesta)\n",
    "print \"Ext_val_R2_score: \" , Ext_val_R2_05 \n",
    "print ''\n",
    "print ''\n",
    "print '20%-80%'\n",
    "Cross_val_MSE_20 = mean_squared_error(y_train, ztest_train)\n",
    "print \"Cross_val_MSE: \", Cross_val_MSE_20\n",
    "Cross_val_Exp_var_20 = explained_variance_score(y_train, ztest_train)\n",
    "print \"Cross_val_Explained_variance: \" , Cross_val_Exp_var_20\n",
    "Cross_val_R2_20 = r2_score(y_train, ztest_train)\n",
    "print \"Cross_val_R2_score: \" , Cross_val_R2_20\n",
    "print ''\n",
    "Ext_val_MSE_20 = mean_squared_error(y_test , ztest)\n",
    "print \"Ext_val_MSE: \", Ext_val_MSE_20\n",
    "Ext_val_Exp_var_20 = explained_variance_score(y_test , ztest)\n",
    "print \"Ext_val_Explained_variance: \" , Ext_val_Exp_var_20\n",
    "Ext_val_R2_20 = r2_score(y_test , ztest)\n",
    "print \"Ext_val_R2_score: \" , Ext_val_R2_20 \n",
    "print ''\n",
    "print ''\n",
    "print '50%-50%'\n",
    "Cross_val_MSE_50 = mean_squared_error(y_train1, ztest_train1)\n",
    "print \"Cross_val_MSE: \", Cross_val_MSE_50 \n",
    "Cross_val_Exp_var_50 = explained_variance_score(y_train1, ztest_train1)\n",
    "print \"Cross_val_Explained_variance: \" , Cross_val_Exp_var_50\n",
    "Cross_val_R2_50 = r2_score(y_train1, ztest_train1)\n",
    "print \"Cross_val_R2_score: \" , Cross_val_R2_50 \n",
    "print''\n",
    "Ext_val_MSE_50 = mean_squared_error(y_test1 , ztest1)\n",
    "print \"Ext_val_MSE: \", Ext_val_MSE_20\n",
    "Ext_val_Exp_var_50 = explained_variance_score(y_test1 , ztest1)\n",
    "print \"Ext_val_Explained_variance: \" ,Ext_val_Exp_var_50\n",
    "Ext_val_R2_50 = r2_score(y_test1 , ztest1)\n",
    "print \"Ext_val_R2_score: \" , Ext_val_R2_50\n",
    "print ''\n",
    "print ''\n",
    "print '95%-5%'\n",
    "print \"Cross validated parameters\" \n",
    "Cross_val_MSE_95 = mean_squared_error(y_trainb, ztest_trainb)\n",
    "print \"Cross_val_MSE: \", Cross_val_MSE_95\n",
    "Cross_val_R2_95 = r2_score(y_trainb, ztest_trainb)\n",
    "print \"Cross_val_R2_score: \" , Cross_val_R2_95\n",
    "print \"\"\n",
    "print 'Externally validated parameters'\n",
    "Ext_val_MSE_95 = extvmetrics2 = mean_squared_error(y_testb , ztestb)\n",
    "print \"Ext_val_MSE: \", Ext_val_MSE_95 \n",
    "Ext_val_R2_95 = r2_score(y_testb , ztestb)\n",
    "print \"Ext_val_R2_score: \" , Ext_val_R2_95 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#generation of the arrays\n",
    "learning_x = np.array([20, 50, 80, 95])\n",
    "plot_ext_rmse = np.array([Ext_val_MSE_20 , Ext_val_MSE_50 , Ext_val_MSE_80 , Ext_val_MSE_95])\n",
    "plot_cross_rmse = np.array([Cross_val_MSE_20 , Cross_val_MSE_50 , Cross_val_MSE_80 , Cross_val_MSE_95])\n",
    "\n",
    "plot_ext_r2 = np.array([Ext_val_R2_20 , Ext_val_R2_50 , Ext_val_R2_80 , Ext_val_R2_95])\n",
    "plot_cross_r2 = np.array([Cross_val_R2_20 , Cross_val_R2_50 , Cross_val_R2_80 , Cross_val_R2_95])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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ajR07dmjhwoXxor28vFxnnnmmpk2bpqlTp6p///5Rh4hWasGCBZo1a5b279+v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\nGPgf3THlpigNzvEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa8b2c01f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#the actual plot\n",
    "figure,(plt1,plt2) = pylab.subplots(1,2)\n",
    "figure.set_size_inches(15,5)\n",
    "plt1.set_ylim([0, 1])\n",
    "plt1.set_xlim([0, 100])\n",
    "plt1.set_title('Mean Squared Error')\n",
    "plt1.plot(learning_x,plot_ext_rmse,c='black',linewidth=1,zorder=1)\n",
    "plt1.plot(learning_x,plot_cross_rmse,c='red',linewidth=1,zorder=1)\n",
    "plt1.set_xlabel('Training Set Size (%)')\n",
    "plt1.set_ylabel('MSE')\n",
    "\n",
    "plt2.set_ylim([0, 1])\n",
    "plt2.set_xlim([0, 100])\n",
    "plt2.set_title('R2 Score')\n",
    "plt2.plot(learning_x,plot_ext_r2,c='black',linewidth=1,zorder=1, label='validation')\n",
    "plt2.plot(learning_x,plot_cross_r2,c='red',linewidth=1,zorder=1, label='training')\n",
    "plt2.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt2.set_xlabel('Training Set Size (%)')\n",
    "plt2.set_ylabel('R2 Score')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interestingly the performance appears to go down for the 95% training - 5% validaiton split. This is however caused by the much smaller sample. The influence of the outliers on the scores is relatively rather large. This effect will not be so pronounced when using a larger data set than the here used 1500 datapoints. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
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