gridSearch.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def warn(*args, **kwargs):\n",
    "    pass\n",
    "import warnings\n",
    "warnings.warn = warn\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import mean_absolute_error as mae\n",
    "from sklearn.metrics import mean_squared_error as mse\n",
    "from sklearn.model_selection import cross_val_predict, ParameterGrid\n",
    "from sklearn.utils import shuffle\n",
    "\n",
    "from paulRegressor import * \n",
    "from seaborn import distplot\n",
    "\n",
    "from numpy import load, save,append, array,percentile,hstack,mean,abs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "params = {\"randomForest\":{'min_samples_split':[2, 5, 10, 15, 20], 'n_estimators':[10, 20, 100, 200], 'max_features':['auto',0.25, 0.33], 'max_depth':[None,10 ,25], 'n_jobs':[-1]},\n",
    "\t\"extraTrees\":{'n_estimators':[2,3,10, 20],'n_jobs':[-1]},\n",
    "\t\"rbfSVM\":{'gamma': [1e-3, 1e-4],\n",
    "\t                     'C': [1, 10, 100, 1000]},\n",
    "\t\"linearSVM\":{'C': [1, 10, 100, 1000]},\n",
    "\t\"ridge\":{'alpha':[0.001],'fit_intercept':[True,False],'normalize':[True],'solver':['svd'],'tol':[0.0001]},\n",
    "\t\"dummy\":{'strategy':['mean','median']},\n",
    "\t\"LinearRegression\":{'fit_intercept':[True,False],'normalize':[True,False]},\t\"NNGarrotteRegression\":{},\"KernelRegression\":{},\n",
    "\t\"KernelRidge\":{'alpha':[1,10]},\n",
    "\t\"AdaBoost\":{'n_estimators':[20,50,100],'loss':['linear','square'],'learning_rate':[0.5,1.0,2.0]},\"Bagging\":{},\n",
    "\t'SGDRegression': {'penalty':['l1','l2','elasticnet',None],'l1_ratio':[0.01,0.10,0.20,0.80]},\n",
    "\t\"KNeighborsRegression\":{'n_neighbors':[2,5,10],'weights':['uniform','distance'],'algorithm':['auto', 'ball_tree', 'kd_tree', 'brute']},\n",
    "\t\"MultiLasso\":{'alpha':[0.01,0.1,1.0,10.0]},\n",
    "\t\"lasso\":{'alpha':[0.01,0.1,1.0,10.0]},\n",
    "\t\"DecisionTree\":{'max_depth':[5,8,11]},\n",
    "\t\"MultiElasticNet\":{'alpha':[0.5,1,2],'l1_ratio':[0,0.5,1.0],'normalize':[True,False],'warm_start':[True,False]}}\n",
    "\n",
    "moreParams = {\"randomForest\":{'min_samples_split':[2, 5, 10, 15, 20], 'n_estimators':[10, 20, 100, 200], 'max_features':['auto',0.25, 0.33], 'max_depth':[None,10 ,25], 'n_jobs':[-1]},\n",
    "\t\"extraTrees\":{'n_estimators':[10,20,30,50,100,200],'max_features':['auto',0.25, 0.33], 'max_depth':[None,10 ,25], 'n_jobs':[-1]}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def loadNumpy(name,path='.'):\n",
    "    if \".npy\" in name:\n",
    "        fullPath = path+'/'+name\n",
    "    else:\n",
    "        fullPath = path+'/'+name+'.npy'\n",
    "    return load(fullPath)\n",
    "\n",
    "\n",
    "def saveNumpy(obj, name, path='.'):\n",
    "    if \".npy\" not in name:\n",
    "        fullPath = path+'/'+name\n",
    "        save(fullPath, obj)\n",
    "        print name,'saved successfully in',path\n",
    "    else:\n",
    "        fullPath = path+'/'+name.split(\".npy\")[0]\n",
    "        save(fullPath, obj)\n",
    "        print name,'saved successfully in',path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "forest = RandomForestRegressor(n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ridge = Ridge()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sgd= SGDRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "extraTrees = ExtraTreesRegressor(n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ada = AdaBoostRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def leaveOneOut(X_full,y_full,estimator=forest):\n",
    "\ty_predict = []\n",
    "\tloo = cross_validation.LeaveOneOut(len(X_full))\n",
    "\tfor train_index, test_index in loo:\n",
    "\t \tX_train, X_test = X_full[train_index], X_full[test_index]\n",
    "\t \ty_train, y_test = y_full[train_index], y_full[test_index]\n",
    "\t \ty_predict += [estimator.fit(X_train, y_train).predict(X_test)]\n",
    "        return r2_score(y_full, y_predict)\n",
    "    \n",
    "def mean_absolute_percentage_error(y_true, y_pred):\n",
    "\ty_true = check_arrays(y_true)\n",
    "\ty_pred = check_arrays(y_pred)\n",
    "\n",
    "\treturn mean(abs((y_true - y_pred) / y_true)) * 100\n",
    "\n",
    "def mape(y_true,y_pred):\n",
    "    return mean_absolute_percentage_error(y_true,y_pred)\n",
    "\n",
    "def getEstimator(regressor):\n",
    "\n",
    "\tif \"lasso\" in regressor or \"Lasso\" in regressor:\n",
    "\t\testimator = Lasso(alpha = 0.1)#RandomForestRegressor(random_state=0, n_estimators=100)\\\n",
    "\telif \"MultiLasso\" in regressor:\n",
    "\t\testimator = MultiLasso()\n",
    "\telif \"ridge\" in regressor or \"Ridge\" in regressor:\n",
    "\t\testimator = Ridge()#(alphas=[0.1, 1.0, 10.0])\n",
    "\telif \"SGDRegression\" in regressor:\n",
    "\t\testimator = SGDRegressor()\n",
    "\telif \"NNGarrotteRegression\" in regressor:\n",
    "\t\testimator = NNGarrotteRegressor()\n",
    "\telif \"KernelRegression\" in regressor:\n",
    "\t\testimator = KernelRegressor()\n",
    "\telif \"LinearRegression\" in regressor:\n",
    "\t\testimator = LinearRegression()\n",
    "\telif \"KNeighborsRegression\" in regressor:\n",
    "\t\testimator = KNeighborsRegressor()\n",
    "\telif \"randomForest\" in regressor or \"RandomForest\" in regressor:\n",
    "\t\testimator = RandomForestRegressor()\n",
    "\telif \"extraTrees\" in regressor or \"ExtraTrees\" in regressor:\n",
    "\t\testimator = ExtraTreesRegressor()\n",
    "\telif \"rbfSVM\" in regressor or \"RBFSVM\" in regressor:\n",
    "\t\testimator = SVR(kernel=\"rbf\")\n",
    "\telif \"linearSVM\" in regressor or \"LinearSVM\" in regressor:\n",
    "\t\testimator = SVR(kernel=\"linear\")\n",
    "\telif \"polySVM\" in regressor or \"PolySVM\" in regressor:\n",
    "\t\testimator = polySVR()\n",
    "\telif \"ElasticNet\" in regressor:\n",
    "\t\testimator = ElasticNet()\n",
    "\telif \"MultiElasticNet\" in regressor:\n",
    "\t\testimator = MultiElasticNet()\n",
    "\telif \"gradientBoost\" in regressor or \"GradientBoost\" in regressor:\n",
    "\t\testimator = gradientBoost()\n",
    "\telif \"AdaBoost\" in regressor:\n",
    "\t\testimator = AdaBoostRegressor()\n",
    "\telif \"Bagging\" in regressor:\n",
    "\t\testimator = BaggingRegressor()\n",
    "\telif \"DecisionTree\" in regressor:\n",
    "\t\testimator = DecisionTreeRegressor()\n",
    "\telif \"dummy\" in regressor:\n",
    "\t\testimator = DummyRegressor()\n",
    "\n",
    "\treturn estimator\n",
    "\n",
    "def crossVal_r2(X,y,estimator,CV=10):\n",
    "    predicted = cross_val_predict(estimator, X, y, cv=CV)\n",
    "    return r2_score(y, predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def runGrid(algorithm,fpType=atom,label = HOMO, maximum=0.7):\n",
    "    estimator = getEstimator(algorithm)\n",
    "    count = 0\n",
    "    if 'extraTrees' not in algorithm:\n",
    "        parameters = params[algorithm]\n",
    "    else:\n",
    "        parameters = moreParams[algorithm]\n",
    "    for g in ParameterGrid(params[algorithm]):\n",
    "        count += 1\n",
    "        estimator.set_params(**g)\n",
    "        r2 = crossVal_r2(fpType,label,estimator,5)\n",
    "        if r2>maximum:\n",
    "            print estimator\n",
    "            print r2\n",
    "            maximum = r2\n",
    "            best = estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "HOMO = loadNumpy('HOMO')\n",
    "LUMO = loadNumpy('LUMO')\n",
    "GAP = loadNumpy('GAP')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "atom = loadNumpy('AtomPairCount')\n",
    "topo = loadNumpy('TopologicalCount')\n",
    "morgan = loadNumpy('MorganCount')\n",
    "maccs = loadNumpy('maccsCount')\n",
    "maccsBin = loadNumpy('maccsBinary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "atomTrain = loadNumpy('atom90')\n",
    "topoTrain = loadNumpy('topo90')\n",
    "morganTrain = loadNumpy('morgan90')\n",
    "maccsTrain = loadNumpy('maccs90')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "HOMOTrain = loadNumpy('HOMO90')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           n_estimators=10, n_jobs=-1, oob_score=False, random_state=None,\n",
       "           verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "   normalize=False, random_state=None, solver='auto', tol=0.001)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
       "          max_features='auto', max_leaf_nodes=None,\n",
       "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "          n_estimators=10, n_jobs=-1, oob_score=False, random_state=None,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extraTrees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.36299185421948976"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crossVal_r2(atom,HOMO,ridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "maximum = 0.757690826374\n",
    "best = extraTrees"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "estimator = getEstimator('ridge')\n",
    "count = 0\n",
    "for g in ParameterGrid(params['ridge']):\n",
    "    count += 1\n",
    "    estimator.set_params(**g)\n",
    "    r2 = crossVal_r2(atom,HOMO,estimator,43)\n",
    "    if r2>maximum:\n",
    "        print estimator\n",
    "        print r2\n",
    "        maximum = r2\n",
    "        best = estimator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#####  ridge does not reach 0.6 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"purple\"> Extra Trees</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### AtomPair"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=2, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.6476492952\n",
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=3, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.702126327611\n",
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=10, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.728391130108\n"
     ]
    }
   ],
   "source": [
    "estimator = getEstimator('extraTrees')\n",
    "count = 0\n",
    "for g in ParameterGrid(params['extraTrees']):\n",
    "    count += 1\n",
    "    estimator.set_params(**g)\n",
    "    r2 = crossVal_r2(atom,HOMO,estimator,43)\n",
    "    if r2>maximum:\n",
    "        print estimator\n",
    "        print r2\n",
    "        maximum = r2\n",
    "        best = estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=25, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.757690826374\n"
     ]
    }
   ],
   "source": [
    "estimator = getEstimator('extraTrees')\n",
    "count = 0\n",
    "for g in ParameterGrid(moreParams['extraTrees']):\n",
    "    count += 1\n",
    "    estimator.set_params(**g)\n",
    "    r2 = crossVal_r2(atom,HOMO,estimator,43)\n",
    "    if r2>maximum:\n",
    "        print estimator\n",
    "        print r2\n",
    "        maximum = r2\n",
    "        best = estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=30, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.768526102917\n"
     ]
    }
   ],
   "source": [
    "estimator = getEstimator('extraTrees')\n",
    "count = 0\n",
    "for g in ParameterGrid(moreParams['extraTrees']):\n",
    "    count += 1\n",
    "    estimator.set_params(**g)\n",
    "    r2 = crossVal_r2(atom,HOMO,estimator,43)\n",
    "    if r2>maximum:\n",
    "        print estimator\n",
    "        print r2\n",
    "        maximum = r2\n",
    "        best = estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "runGrid('SGDRegression',atom,0.62)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "runGrid(\"linearSVM\",atom,0.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=2, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.656809764772\n",
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=3, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.674923265941\n",
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=10, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.803650832531\n",
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=20, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.824344942603\n"
     ]
    }
   ],
   "source": [
    "runGrid('extraTrees',atom,LUMO, 0.65)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoostRegressor(base_estimator=None, learning_rate=0.5, loss='linear',\n",
      "         n_estimators=50, random_state=None)\n",
      "0.605706281502\n",
      "AdaBoostRegressor(base_estimator=None, learning_rate=0.5, loss='square',\n",
      "         n_estimators=20, random_state=None)\n",
      "0.614484141257\n",
      "AdaBoostRegressor(base_estimator=None, learning_rate=1.0, loss='linear',\n",
      "         n_estimators=20, random_state=None)\n",
      "0.628100358722\n",
      "AdaBoostRegressor(base_estimator=None, learning_rate=1.0, loss='square',\n",
      "         n_estimators=100, random_state=None)\n",
      "0.629841551841\n",
      "AdaBoostRegressor(base_estimator=None, learning_rate=2.0, loss='linear',\n",
      "         n_estimators=100, random_state=None)\n",
      "0.631300344444\n"
     ]
    }
   ],
   "source": [
    "runGrid('AdaBoost',atom,HOMO, 0.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=2, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.694656791006\n",
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=3, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.712491265414\n",
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=10, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.785140970431\n",
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=20, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.78883825339\n"
     ]
    }
   ],
   "source": [
    "runGrid('extraTrees',atom,GAP, 0.65)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "90-10 split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=20, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.718177358977\n"
     ]
    }
   ],
   "source": [
    "runGrid('extraTrees',atomTrain,HOMOTrain, 0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Topological"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "maximum = 0.7\n",
    "best = extraTrees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=5, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.634401024146\n",
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=10, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.675931657679\n",
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=30, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.687142075827\n"
     ]
    }
   ],
   "source": [
    "runGrid('extraTrees',topo,0.62)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=10, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.688884270561\n"
     ]
    }
   ],
   "source": [
    "runGrid('extraTrees',topo,LUMO, 0.65)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "runGrid('extraTrees',topo,GAP, 0.65)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "runGrid('SGDRegression',topo,0.62)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "runGrid(\"KNeighborsRegression\",topo,0.62)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "runGrid(\"linearSVM\",topo,0.62)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "runGrid(\"LinearRegression\",topo,HOMO,0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ElasticNet(alpha=0.5, copy_X=True, fit_intercept=True, l1_ratio=0,\n",
      "      max_iter=1000, normalize=False, positive=False, precompute=False,\n",
      "      random_state=None, selection='cyclic', tol=0.0001, warm_start=True)\n",
      "0.607389003436\n"
     ]
    }
   ],
   "source": [
    "runGrid(\"MultiElasticNet\",topo,HOMO,0.4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "90-10 split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "runGrid(\"extraTrees\",topoTrain,HOMOTrain,0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"orange\"> Morgan </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=5, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.702671937053\n",
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=10, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.7504542867\n",
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=20, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.769151217913\n",
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=30, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.773599436674\n"
     ]
    }
   ],
   "source": [
    "runGrid('extraTrees',morgan,0.65)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=2, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.801129922817\n",
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=20, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.817445595432\n"
     ]
    }
   ],
   "source": [
    "runGrid('extraTrees',morgan,LUMO, 0.65)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=3, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.686658376068\n",
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=10, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.736669113492\n",
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=20, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.749691732371\n"
     ]
    }
   ],
   "source": [
    "runGrid('extraTrees',morgan,GAP, 0.65)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ElasticNet(alpha=0.5, copy_X=True, fit_intercept=True, l1_ratio=0,\n",
      "      max_iter=1000, normalize=False, positive=False, precompute=False,\n",
      "      random_state=None, selection='cyclic', tol=0.0001, warm_start=True)\n",
      "0.677670843891\n"
     ]
    }
   ],
   "source": [
    "runGrid(\"MultiElasticNet\",morgan,HOMO,0.4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "90-10 split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=10, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.750938411645\n",
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=20, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.763767389738\n"
     ]
    }
   ],
   "source": [
    "runGrid('extraTrees',morganTrain,morganTrain,0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MACCS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=5, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.71312699438\n",
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=10, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.742446043688\n",
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=20, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.754641279972\n",
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=30, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.772175833982\n"
     ]
    }
   ],
   "source": [
    "runGrid('extraTrees',maccs,0.65)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "runGrid('SGDRegression',maccs,0.62)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "runGrid('SGDRegression',maccs,0.62)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "runGrid(\"linearSVM\",maccs,0.62)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "runGrid('extraTrees',maccsBin,0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### It produces less than 0.5 when running grid on binary values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=2, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.756062068224\n",
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=10, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.767555350392\n",
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=20, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.771871173458\n"
     ]
    }
   ],
   "source": [
    "runGrid('extraTrees',maccs,LUMO, 0.65)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=2, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.694275348288\n",
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=10, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.788401007689\n",
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=20, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.790962333909\n"
     ]
    }
   ],
   "source": [
    "runGrid('extraTrees',maccs,GAP, 0.65)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "90-10 split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=10, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.718738519602\n",
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=20, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.746482384834\n"
     ]
    }
   ],
   "source": [
    "runGrid('extraTrees',maccsTrain,HOMOTrain,0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"purple\">Combination </font>\n",
    "#### MACCS Morgan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "maccsMorgan = loadNumpy('maccsMorganCount')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=3, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.70313230947\n",
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=10, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.763543165338\n"
     ]
    }
   ],
   "source": [
    "runGrid('extraTrees',maccsMorgan,HOMO, 0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### MACCS Topo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "maccsTopo = loadNumpy('maccsTopoCount')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=10, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.751550769114\n",
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=20, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.752999199728\n"
     ]
    }
   ],
   "source": [
    "runGrid('extraTrees',maccsTopo,HOMO, 0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Morgan Topo "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "morganTopo = loadNumpy('morganTopoCount')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=20, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.750875536848\n"
     ]
    }
   ],
   "source": [
    "runGrid('extraTrees',morganTopo,HOMO, 0.73)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Atom Morgan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "atomMorgan = loadNumpy('atomMorganCount')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=10, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.732289442461\n",
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=20, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.752642394829\n"
     ]
    }
   ],
   "source": [
    "runGrid('extraTrees',atomMorgan,HOMO, 0.73)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### MACCS Atom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "maccsAtom = loadNumpy('maccsAtomCount')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=10, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.734471538771\n",
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=20, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.747916969908\n"
     ]
    }
   ],
   "source": [
    "runGrid('extraTrees',maccsAtom,HOMO, 0.73)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Atom Topo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "atomTopo = loadNumpy('atomTopoCount')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=20, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.746106879047\n"
     ]
    }
   ],
   "source": [
    "runGrid('extraTrees',atomTopo,HOMO, 0.73)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"red\">Leave One Out</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bestExtra = ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
    "          max_features='auto', max_leaf_nodes=None,\n",
    "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
    "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
    "          n_estimators=30, n_jobs=-1, oob_score=False, random_state=None,\n",
    "          verbose=0, warm_start=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.76221698393413129"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leaveOneOut(morgan,HOMO,bestExtra)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.77114660557546977"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leaveOneOut(morganTrain,HOMOTrain,bestExtra)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.74867194695551209"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leaveOneOut(morganTrain,HOMOTrain,nextBest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.69375279994054195"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leaveOneOut(morganTrain80,HOMOTrain80,bestExtra)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.77518137364465267"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leaveOneOut(maccs,HOMO,bestExtra)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "90-10 split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7744127971355852"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leaveOneOut(maccsTrain,HOMOTrain,bestExtra)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "leaveOneOut(maccsTrain80,HOMOTrain80,bestExtra)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.76774789762387941"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leaveOneOut(maccsTrain,HOMOTrain,nextBest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=10, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.75953356071\n",
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=20, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.767550681497\n"
     ]
    }
   ],
   "source": [
    "runGrid('extraTrees',maccsMorgan,HOMO, 0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7553427872248677"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leaveOneOut(atom,HOMO,bestExtra)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.75089538420205182"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leaveOneOut(atomTrain,HOMOTrain,bestExtra)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.75502508173080884"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leaveOneOut(atomTrain,HOMOTrain,nextBest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.69795208183588331"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leaveOneOut(topo,HOMO,bestExtra)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.66419468108687729"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leaveOneOut(topoTrain,HOMOTrain,bestExtra)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.58945828989395588"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leaveOneOut(topoTrain80,HOMOTrain80,nextBest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.68761118277860211"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leaveOneOut(topoTrain,HOMOTrain,nextBest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.60358299984328367"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leaveOneOut(topoTrain80,HOMOTrain80,nextBest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nextBest = ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
    "          max_features='auto', max_leaf_nodes=None,\n",
    "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
    "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
    "          n_estimators=20, n_jobs=-1, oob_score=False, random_state=None,\n",
    "          verbose=0, warm_start=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.75704717110249709"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leaveOneOut(morgan,HOMO,nextBest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.75531714588288135"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leaveOneOut(maccs,HOMO,nextBest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.72397282903503535"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leaveOneOut(atom,HOMO,nextBest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.69045778100316191"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leaveOneOut(topo,HOMO,nextBest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5-fold Cross-validation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f916bcdda10>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW8AAAD7CAYAAAClvBX1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl0W9d94PEv9oXgApIgRZGUqPVKsmzLlpU6sV0rie0m\nqd20VVN3knHdOj1NO22nM2067TnNSRq3ncy00+Wc1l2T1q4z7STtSRo7TlzHTrzbsS1b1mZdiRIp\ncRMJkuACYiGWN38AUCiZC0QCeHjg73OSIywPwI/XwA8Xv3cXm2EYCCGEsBa72QEIIYS4epK8hRDC\ngiR5CyGEBUnyFkIIC5LkLYQQFiTJWwghLMi50gFKqU8C9y246SZgN/Ao4ABGgPu01smyRCiEEOJd\nbFczzlspdTvw04Af+JbW+l+VUv8TGNBa/3WZYhRCCHGFFXveV/gs8AngFeCX8rc9DnwaWDJ5h8Oz\nNTMTKBj0E4nEzA7DVNIGOdIO0gZQ3jYIheptS91XdM1bKXWAXA/7IlC3oEwyBnSsLUTrcDodZodg\nOmmDHGkHaQMwrw2upuf9C8DDi9y+5DdDQTDor6n/yKFQvdkhmE7aIEfaQdoAzGmDq0neB4Ffy1+O\nKqV8Wus40AkML/fAWvpZFQrVEw7Pmh2GqaQNcqQdpA2gvG2w3JdCUWUTpdRGIKq1ns/f9DRwKH/5\nEPDkWgIUQghxdYqteXeQq20XfA64Xyn1AtAMPFLqwIQQQiytqLKJ1vow8OEF10eAO8sVlBBCiOXJ\nDEshhLAgSd5CCGFBkryFEMKCJHkLIYQFXe30eCFEFXr2yNCKxxzc11mBSESlSM9bCCEsSJK3EEJY\nkCRvIYSwIEneQghhQZK8hRDCgiR5CyGEBUnyFkIIC5LkLYQQFiTJWwghLEiStxBCWJAkbyGEsCBJ\n3kIIYUGSvIUQwoIkeQshhAVJ8hZCCAuS5C2EEBYkyVsIISyoqJ10lFKfAP4HkAY+CxwFHgUcwAhw\nn9Y6Wa4ghRBCXG7FnrdSqgX4HHArcDfwUeBB4CGt9W1AL/BAOYMUQghxuWLKJncAT2utZ7XWI1rr\nXwQOAo/l7388f4wQQogKKaZs0gP4lVKPAUHg94C6BWWSMaCjLNEJIYRYVDHJ2wa0AD8BbAa+l79t\n4f3LCgb9OJ2OVQVYjUKherNDMJ20QU61tEN9wLviMeWKtVrawExmtEExyXsUeFlrnQbOKqVmgbRS\nyqe1jgOdwPByTxCJxNYeaZUIheoJh2fNDsNU0gY51dQOs9HEiseUI9ZqagOzlLMNlvtSKKbm/RTw\nAaWUPX/yMgA8DRzK338IeHKtQQohhCjeislbaz0E/BvwKvBt4NfIjT65Xyn1AtAMPFLOIIUQQlyu\nqHHeWuu/Bf72ipvvLH04QgghiiEzLIUQwoIkeQshhAVJ8hZCCAuS5C2EEBYkyVsIISxIkrcQQliQ\nJG8hhLAgSd5CCGFBkryFEMKCJHkLIYQFSfIWQggLkuQthBAWVNTCVEII65iZm+elYyOkMwbNDR66\nQgE2b5ANE2qN9LyFqCEzc/M89doA4akEM3PznB2a4bkjw5wbnjY7NFFikryFqBGzsXn+47ULxJJp\nblIh/tMdO/jwzZtwOe28cnyUC6Pre8ebWiPJW4gacViHiScz3LQrxJ4tzdjtNkJNPm69roNM1uAv\nv3aMaDxldpiiRCR5C1EDpqJJLoxGaW30sntz8LL7utsCXLuthfHpBN98ud+cAEXJSfIWogac6JsE\nYO/WZmw227vuv25bC00BN8+9PcxcQnrftUCStxAWNzGd4NzwDI11brrbAose47DbuPNAN8n5DM++\nNVThCEU5SPIWwuL+4/ULGMbSve6C26/vxOdx8J03BkmlMxWMUJSDJG8hLCyVzvLi0RH8Xic9HQ3L\nHuv3Ojm4r5OZuXlePn6xQhGKcpHkLYSFneifJDGfoWdDPQ770r3ugjtu6sbpsPGdNwYxDKMCEYpy\nWXGGpVLqIPCvwIn8TceAPwIeBRzACHCf1jpZphiFEEs4fGoMoOgZlMF6D9dvb+WwDjMwFmVTu8y8\ntKpie97Paa0P5v//a8CDwENa69uAXuCBskUohFhUOpPlSO84wXoPrY3eoh938552AL5/crRcoYkK\nWG3Z5CDwWP7y48AdJYlGCFG0UxcizCXS7N8ZWvZE5ZWu29aCz+Pg+++MkpXSiWUVm7z3KKUeU0q9\nqJS6E6hbUCYZAzrKE54QYilvnAoDsF+FrupxLqeDG3eGmJxJ0jsoa55YVTGrCp4BPg98FdgKfO+K\nx634lR8M+nE6HasKsBqFQlInlDbIMasdMlmDt8+O0xTw8N4buvnO98+v+JiFsd713i28dOwiR/sm\nueXG7jXFIu8Fc9pgxeSttR4CvpK/elYpdRE4oJTyaa3jQCcwvNxzRCKxNQdaLUKhesLh9b3Aj7RB\njpntoC9EmI7Oc3DfRiYnosxGEys+ZmGsG5s8NPhdPP/WED9+Sw9Ox+oqqPJeKG8bLPelsOJ/MaXU\nJ5RSn85f3gC0A/8IHMofcgh4cu1hCiGKdaI/Nx3+uu2tq3q8w27nwK52ovEU+sJUKUMTFVJM2eQx\n4J+VUh8F3MAvA28B/6SU+hRwHnikfCEKIa50oi+Cw25DdTcV/Zhnj1w+Ld6er2Q+8Wo/4ek4AAf3\ndZYsRlFexZRNZoF7FrnrztKHI4RYyVwiRf/FGbZ3NuLzrH4zrLagH5fTzuDYHAd2GVc1YkWYT2ZY\nCmExp85PYRiwp6d5Tc/jsNvY2FpHNJ5iem6+RNGJSpHkLYTFnDyfq3fv6QmucOTKukJ1AAyORdf8\nXKKyJHkLYTEn+yN43A62rLAQVTE6C8k7PLfm5xKVJclbCAuZmE4wOhljV3fTqof3LeR1Owk1eQlH\n4iTmZZlYK5HkLYSFXCqZbFlbvXuhrrYABjA8LqUTK5HkLYSFvNMfAWDP5rXXuwu6QrnddwbHpHRi\nJZK8hbAIwzDQA1M0+F1sbK0r2fM2Bdz4vU6GJ+bIZmWhKquQ5C2ERUxMJ4jMJtnR1VTSMdk2W27I\n4HwqS//F9T3V3UokeQthEXogN41951XMqixWoSd/vG+i5M8tykOStxAWcWawfMm7o8WPDTh+brLk\nzy3KQ5K3EBZxemAar9tBd1ug5M/tcTlobfJybniGWCJV8ucXpSfJWwgLmJmb5+JkjO2djdiL2Gh4\nNTa21pE1DE7mR7SI6ibJWwgLOF3GeneB1L2tRZK3EBZwuoz17oKWRi91XifH+yYxZG/LqifJWwgL\nODMwjdNhY0tH+bbbstts7OlpZnImychE7ex+VaskeQtR5eLJNBfGZtnS0YCrzHvB7s1Puz/eJ6NO\nqp0kbyGq3LmRGQwDdnSVr2RSsHdrCwDHz0ndu9pJ8haiyp0dnAZgW+fal4BdSbDeQ2eoDj0wxXxK\nVhmsZpK8hahyvcOF5N1Ykdfbu6WZVDp76SSpqE6SvIWoYlnD4NzQDG1BHw1+d0Ve8welE6l7VzNJ\n3kJUsZGJGLFkmm0bK9PrBtjZ1YjbaZeTllVOkrcQVezsUK5ksr2rcsnb5XSgNgUZHp9jciZRsdcV\nV8dZzEFKKR9wHPh94BngUcABjAD3aa2TZYtQiHWsN5+8t20s/8nKhfZubebYuQmO903yw9dvrOhr\ni+IU2/P+DFD4DfUg8JDW+jagF3igHIEJIXI9b4/bcWm3m0q5NN5bhgxWrRWTt1JqF7AHeCJ/00Hg\nsfzlx4E7yhKZEOvcXCLFyESMrR0NZVuMaikbmv20NHg52R8hk81W9LVFcYopm/wJ8KvA/fnrdQvK\nJGNAx0pPEAz6cZZ5ZlglhULlm6JsFdIGOeVsh/PvjAJw3Y7Qiq9TH/CW5DUXvs6Bazbw5Cv9TMUz\n7OpZuuYu7wVz2mDZ5K2U+lngFa11n1JqsUOK6g5EIrWzTkIoVE84vL63ipI2yCl3O7x58iIAHUHf\niq8zGy3NicWFr7NtQy4hvfDmAC11rkWPl/dCedtguS+FlXrePwpsVUrdDXQBSSCqlPJpreNAJzBc\nqkCFWG+ePTK05H1v6DEARibnmJyt/KiP3ZuD2G02jvdN8uO3ba3464vlLZu8tdb3Fi4rpX4P6Afe\nBxwCvpz/98nyhSfE+pQ1DMJTcRrr3Hhc5pQc/V4n2zsbODM0TTSeIuBbvPctzLGacd6fA+5XSr0A\nNAOPlDYkIcR0NEk6YxBq8pkaxzVbWzAMONkvE3aqTVHjvAG01r+34OqdpQ9FCFEwFsmVSUJNpTkR\nuVp7tzTz9efPcfzcJO/Z3W5qLOJyMsNSiCoUnooDmN7z3ryhnnq/i2PnJsjK7jpVRZK3EFUoPBXH\n5bTTGKjMYlRLsdtsXLethem5efpH1veokmojyVuIKpOYTzMbSxFq8mKzVXZyzmJu2BEC4K0zYZMj\nEQtJ8haiyoSnCvVuc0smBdf0NONy2jnSO252KGIBSd5CVJlwpDrq3QUet4M9m4MMhecYy9fihfkk\neQtRZQonK1tNHmmy0L4drQAcOSO972pR9FBBIUT5ZbMG49MJmgJu3CasB7TUjM94Mp27/60h7jrQ\nXcmQxBKk5y1EFZmcTZLJGrQFq6NkUuDzOGlt9DIaiRGNp8wORyDJW4iqUm317oW62wMYBrwtJy6r\ngiRvIapI4YRgtfW8ATa351a4O6xlyGA1kOQtRJUwDINwJI7X7ajKRaAa6tw0Bdwc75skMZ82O5x1\nT5K3EFViLpEmlkzTFvRVxeScxWxqryedyXL0rGyPZjZJ3kJUiWpZz2Q5mzfk9tKU0on5JHkLUSXG\n8icr26o4eTcFPLQFfRw9O0EqnTE7nHVNkrcQVSI8Fcdus9Hc6DE7lCXZbDb27wyRTGU43idrfJtJ\nkrcQVSCVzhKZTdLS6MFhr+6P5X7VBkjpxGwyw1KIKjA+HccwqnOI4JXOj87g9zp549QYT7x0jlhs\n/rL7D+7rNCmy9aW6v+KFWCeqbSXB5dhsNja1B5hPZxkamzM7nHVLkrcQVWCsimdWLqYwYefs0JTJ\nkaxfkryFMJmR3ym+3u/C57FGJTMU9OF1O+gbnpHt0UwiyVsIk01F50mls5bpdUNue7RN7QHiyfSl\n9VhEZUnyFsJk4Spez2Q5m/Klk/OjsrelGVb8jaaU8gMPA+2AF/h94G3gUcABjAD3aa2T5QtTiNpl\ntXp3wYZmPx6XgwujUQ7saqvaKf21qpie9z3AG1rr24GfBv4UeBB4SGt9G9ALPFC+EIWobYWd4ptM\n3in+atntNno6Gogl0kzOSN+t0lZM3lrrr2it/yh/tRsYBA4Cj+Vvexy4oyzRCVHj4snCTvHVuxjV\ncno2NgAwGI6aHMn6U/SpbaXUy0AXcDfw9IIyyRjQsdxjg0E/ThO2dCqXUKje7BBMJ22Qs9Z2iCZy\n64N0tdVTH6iePSuL5fG4sNtsDE/EuHVfLv71+N4w428uOnlrrd+nlNoHfBlY2EVYsbsQicRWEVp1\nCoXqCYfX9wkaaYOcUrTDhYvTADTWOZmNJkoRVkXVB7y0N/sYmYgxOh7F73Wuu/dGOT8Py30prFg2\nUUrtV0p1A2itj5BL+LNKqcLZlU5guARxCrHujE7GsdmgtdFaJysX6grllomV0kllFXPC8oeB3wRQ\nSrUDAeBp4FD+/kPAk2WJTogaFk+mmZhJ0NLgxeW07qjdrrY6AAbHJHlXUjFlk78BvqSUegHwAb8C\nvAH8k1LqU8B54JHyhShEbTozOI1hwIYWv9mhrEm9301jwM3IRIx0Jmt2OOvGislbax0HPr7IXXeW\nPhwh1o9TFyJAbry01XWFApzom+TiRO2c36p21v2tJoTF6QsR7DbrTc5ZTFcoVzoZGpdVBitFkrcQ\nJogl0vRfnKWl0WfpendBqMmH02FjRHreFWP9d40QFnR6cKom6t0FdruNDc1+ZubmmZi23pBHK5Lk\nLYQJ9KV6t/VLJgUdLbnSyYl+2duyEiR5C2GCU+encDpsNVHvLtjYmvsVcVKSd0VI8haiwqLxFBdG\nZ9na0YDTUTsfwYY6N36vk5P9EdmgoQJq550jhEWc7J/EAK7Z0mx2KCVls9noaPFf+nIS5SXJW4gK\nO3Z2AoBrt7WYHEnpbSzUvfukdFJukryFqKCsYXCsb5IGv+vSTjS1pDB65mR/xORIap8kbyEqaGA0\nyszcPHu3tmC34PrdK/F5nHSFAvQOTctU+TKT5C1EBR09ly+ZbK29kkmB6m4ilc7SPyJ173KS5C1E\nBR07N4HNVnsnKxfauakJAD0gpZNykuQtRIXMJVKcHZpm68YGAj6X2eGUzc6uRgBOD0ybHEltk+Qt\nRIWc6JvEMGq7ZALQGPDQ3uznzOAU2ayM9y4XSd5CVMhbZ8YBuH5bq8mRlJ/qbiQxn2FANmgoG0ne\nQlTAfCrDkd5xQk1eNrUHzA6n7HZ2F+reUyZHUrskeQtRASf6JknOZ7hJtWGrwSGCVyok79OSvMtG\nkrcQFfC6HgPgpl1tJkdSGa2NPloaPJwemMKQdU7Kopg9LIUQa5BKZzlyZpzWRi89G2pvVuWVnj0y\nBOROXJ4bnuEbL/XRFPBcdszBfZ1mhFZTpOctRJmd6JsksY5KJgVtwdxyt6OTcZMjqU2SvIUos9dP\n5Uom+3eFTI6kstqDuXVORiOyNVo5FFU2UUr9EXBb/vgvAK8DjwIOYAS4T2udLFeQQlhVcj7DW2fC\ntDR42NrRYHY4FdVQ58LrdjA2GccwjHX1q6MSVux5K6XeD+zVWr8X+BDw58CDwENa69uAXuCBskYp\nhEW9ocdIzGd4396OdZe8bDYb7UEfsWSaaDxldjg1p5iyyfPAx/KXp4A64CDwWP62x4E7Sh6ZEDXg\nxaMjANxyXYfJkZijrTlfOpG6d8mtmLy11hmt9Vz+6ieBbwF1C8okY8D6fGcKsYzRSAw9MMWuTU20\n1dBelVejvXDSUureJVf0UEGl1EfJJe+7gDML7lrxt2Aw6MfpdFx9dFUqFKr94V4rkTbIWa4dvv36\nAAAfuXXrksfVB7xliauSlvsbAnUePC4H4anEZcfV2vvHjL+n2BOWPwL8LvAhrfW0UiqqlPJpreNA\nJzC83OMjNfStGwrVEw6v73WKpQ1ylmuHbNbgO98/j8/jYEfH0sfNRhPlDLHs6gPeFf+GUJOXwfAc\no+Oz+L251RRr6f1Tzs/Dcl8KxZywbAT+GLhba13YmO5p4FD+8iHgyTXGKERNOXpugqnoPO/Z3Y7H\nVTu/OlejvVD3jkjdu5SK6XnfC7QCX1VKFW67H/iiUupTwHngkfKEJ4Q1PXN4EID33yAzCS/VvSdj\nbFlnwyXLacXkrbX+O+DvFrnrztKHI4T1DY/PcaJvkp3dTTW5yfDVam7w4nTYGJOed0nJDEshSuyZ\nN3O97jv2d5kcSXWw222EmnxMRedJzKfNDqdmyMJUQqzCs0eGFj1ZN5/K8MLbw/i9Tm7YWfubLhSr\nvdnPyESMsUhcfo2UiPS8hSih3qFp0hkDtakJh10+XgXtskhVycm7S4gSyWYN3umP4LDb2JHfhFfk\ntDZ6sdtsjNXQsGGzSfIWokTOX5xlLpFme1cjXrdUJBdyOOy0NnmZnEkyn86YHU5NkOQtRAkYhsGJ\n/klswJ6eoNnhVKX2oA8DCEesPTGpWkjyFqIERiZiTM4k2bShnnq/2+xwqtIPJutI6aQU5LedECVw\noi83+fiaLc2XbitsByZyQk0+bMhJy1KRnrcQazQxk2BkIsaGZj+tjdZfaKpcXE47zQ1eJqbjJFNS\n914rSd5CrNHJRXrdYnEbWvxkDTgzOGV2KJYnyVuINYjGUvRfnCVY72Fjq9/scKpeR0uujU72R0yO\nxPokeQuxBifPT2IYuV73etvmbDXagj7sNhvvSPJeM0neQqxSIpmmd3Aav9dJzwaZ8l0Mp8NOqMnL\nhdFZ2ddyjSR5C7FKx86Ok84Y7OkJYrdLr7tYHa11GMCp89L7XgtJ3kKsQiqd5WjvOG6XnR1dTWaH\nYykd+fHe70jyXhNJ3kKswpnBKRLzGXZvDuJyysfoarQ0evG6HZyU5L0m8q4T4iqlM1lO9kVwOuzs\n2iRT4a+W3W5DdTcxOhljckamyq+WJG8hrtLLxy8SS6a5ZmsLHvf63p9ytXb35MbEF2amiqsnyVuI\nq5DNGnz71fPYbbBvZ8jscCzr2q255H307ITJkViXJG8hrsLh02FGI3G2djYS8LnMDseyOlrqaA/6\nON4/SSqdNTscS5LkLUSRDMPgiVf6sdlgr0yFX7Prt7eSnM+gL8iJy9WQ5C1EkU70TXJhNMqBXW00\n1Mmyr2t1/fbcHp9v90rpZDWKWhJWKbUX+AbwZ1rrv1RKdQOPAg5gBLhPa50sX5hCmO+br5wH4CM3\nb+bcyIzJ0Vjfjq5GfB4nR3rH+fidO2R5gau0Ys9bKVUH/AXwzIKbHwQe0lrfBvQCD5QnPCGqw+mB\nKU4PTHHt1hbZ/bxEnA47125tZmImwVB4zuxwLKeYskkS+AgwvOC2g8Bj+cuPA3eUNiwhqodhGHzt\n+XMA3HNLj7nB1JhC6eRI77jJkVjPislba53WWl+59UXdgjLJGNBR8siEqBIn+ic5PTDFddta2N4p\nu8KX0rVbW7DbbLx1Jmx2KJZTim3QVixUBYN+nM7amcwQCsnP5vXSBoZh8Nj/fROAT3702kt/d33A\ne9m/69lq2qDQjiHg+h2tvHU6TAobG0OBEkdXGWZ8HlabvKNKKV++R97J5SWVd4nU0IajoVA94fCs\n2WGYqtbbYOHekxdGZ+kdmGLzhnoOnxzh8MmRS/fVB7zMRtf39O7VtsHC98/+nbnk/a0Xz/HRW7eU\nMryKKOfnYbkvhdUOFXwaOJS/fAh4cpXPI0TVymSzHNZhbMC+7S1mh1OzbtwZwu2y88qJixiGYXY4\nlrFiz1sptR/4E6AHSCmlfgr4BPCwUupTwHngkXIGKYQZTvRFmI2l2L05SGPAY3Y4NcvrdnLDjhDf\nPznKuZEZtm2U8wrFWDF5a60PkxtdcqU7Sx6NEFUiGk9x7OwEXreD66XXXXbvvaad758c5dUTo5K8\niyQzLIW4gmEYvP7OGJmswU27QrhdtXOyvVrt6Wkm4HPx2jujpDOy1kkxJHkLcYXeoRkGxqK0B31s\n6WgwO5x1wemw80N72pmNpThyRsZ8F0OStxALDIxFee3kKG6nnVuu7ZAp2xX0gRs7AXjqjQGTI7EG\nSd5C5MWTaf7q34+TyRrccl0HAb8s+VpJHS11XLu1hd7Bafpk7ZgVSfIWglzi/vN/fZvRyRh7eoJ0\nt1lzsojV3XWgG4CnXpfe90okeYt1L5ZI8SdfOcKZwWnes7uNG2WHHNPs6QnSGarjjVNjsr/lCkox\nPV4Iy+obmeGL3zzJyESM9+3dwAMf2c3zR5edMCxKYOEs1ittag8wFJ7jO28McO8HdlQwKmuRnrdY\nl2KJNP/27Fn+4J/eYGQixl0HunngR3djt8sJSrNt7WjA73XyzOEhxqevXBNPFEjPW6wrU9Ekzxwe\n5LtvDhJPZmht9PLzH9nN7s1Bs0MTeQ6HnRt3tvLi0Yt87flz/OI915gdUlWS5C1qxlI/xbNZg+Hx\nOU4PTjMcniNrGDT4XXzk9s18cH8XXrd8DKrNlo4GBkbnePXEKHfe1C3j7Rch71pRs2KJFHpgmrOD\n08SSaQA2t9dz+76NvG/vBpk5WcVsNhs//f5t/PH/O8JXvtvLb3/8BhlzfwVJ3qLmTEWTnDg3Sd/I\nDFkDXE47O7ub2NHVyKHbt5kdnijS7p5m9m1v5UjvON99c4gP7u8yO6SqIslb1Ix4Ms3bveOcGZjG\nABrq3OzpCbKlowGXU87NW9HPfkjR+6Vpvvq9XnZtDtLZWmd2SFVDkrewvGQqw1OvXeDxl/tJZwwa\n6tzcuLOV7rbAu35qLzdETVSfpoCH+z+0i4e+foy/f+wEn7n/JpwO+SIGSd7CwrKGwSvHcyMSIrNJ\nvG4H+1ULO7qaZMhfDdmvQtx2XQcvHB3hH791ik/evRu71L8leQtrOtk/yVe/28uFsSgup50ffe9m\n6utcuGtor1TxAx+/YyfD43O8cuIi9X4X935g+7o/gSnJW1jGs0eGmIomOazDDIXnANi6sYF9O1oJ\n+GQRqVrmcTv49Y9dzxe+fJinXh/A53HyY7f0rOsELslbWMJoJMZLx0Y4NzyDYUB7s4+bVBstjbJ7\n+3oR8Ln4zXv38YUvH+YbL/YxOhnj5z68a90O+ZTkLaraaCTGN1/u55Xjo2QNg6aAmxt3hugM1a3r\nXtd61dzg5TM/exN/+bVjvHpylJHJGD//4V1sal96l/VaJcm7RFYaxXBwX+ey98cSaf7j9fNE42mi\nsRTReIrEfJpkKks6k8Vus2G32/B5HNT73TTWuWkL+vB53v2fcKXXsoL+izM8/cYgr57IJe3O1jq2\ndTWyuf3dI0hEbVruM3Xz3nawwdmhGT7/8Ovcvq+Te97XQ7D+3RtFFzPCyIqfGUneqxBPpum/OEv/\nyAx9IzMMjc8RmU0yn8piYGCz2XDYbbiddtwuB26nnWNnJ/C4HDjsNmw2G8lUhsR8hqlokvHpBPH8\nDMCr1eB30RkK0N0eoK3JZ+lRFtPRJIdPh3np2Ah9I7MAdLbW8WO3bmG/CvH827Lan8hx2HM7HX30\n1i38y9NnePatIZ47MsT121q5+Zp21KYgjXVus8MsK0neK5hPZbgwFqVvZIb+kRkujM0xFI5edkyd\n14nb5SDgc2GzgWFAOpMllc4SjaVIZbKMRhZfHc3jctDS6GVHVyOJ+QwBn5OAz0XA58LnceJx5xK+\nQW6NjlgizWxsnsmZJGOROGOROO+cj/DO+Qgel4Outjoa/W72bGnGU+W1wOR8hv6LM5y6MMU7/ZOc\nGZrGMMBmg33bWzl4Qyd7tzbLsDCxpL1bWvj8A0FeOjbCc0eGOdI7zpHe3B6YbUEf7UE/yVSaOq+L\nOp+LOp8Tv8eJ3+vEYbf2ePFVJ2+l1J8BNwMG8Ota69dLFlUFpTNZIrNJJmcSTM4kmZhJ5C7PJpmY\nTnBxMkY+WqzJAAAHnElEQVQma1w63u91sntzkJ4N9WzpaKCno56WBi/PLdMrzGYN3rO7nflUhkzW\nwDAMPC4HHrcDj8txqQyw3M87G2B32Gioc9NQ56YzlNvpJZM1GJ2McWE0ysDYLGeHZviLrx3D7bRz\nzZZm9vQ0s6Orka5QwJReeaF9x6bihCNxwlNxxqbiDI/PcXEiRqFlbcC2zkYO7Grjpl1ti/78FWIx\nToed2/d1cvu+Ti6MznLs3AR6YIq+4RmORSaWfJzX7cDvzXWWIjNJOkN1dIYCtAd9lpgIZDMMY+Wj\nrqCUuh34La313Uqp3cA/aK3fu9Tx4fDs1b9ICRiGwWw8RSSflAuJeWImSSR/fSo6v+TjXU47TQE3\nLY1eWhu9tDT46GyvJzqXrOBfUTzDMJiYTmCz2XjzdJiRidil+5wOO6EmL6EmH3VeF5OziUs9WgOD\n/P8WPBdsbK0Dw8DIX3fYbTgddhobvKTm0zgdNjJZg1Q6SzKVufTvdHSembl5pqJJ5hKLl4NcDjvN\nDR6aG7y0N/tob/ZX/S+FK9UHvMxG1/duL2a3QXHnki4wF08xl0gxF08TS6aJJdLMJVLEEunLOmeQ\ne593tPjpbgvQ1Raguy1Ad1s9DX7XoudbQqF6wuHZkv5dC557yR7XanveHwT+HUBr/Y5SKqiUatBa\nl3zX0HgyzVw8RSZrkM5kSWeMS5czmSyxZIa5RIrZ2Dxz8TSz8Xkis0kmZnK96VQ6u+jzOuw2gvUe\n2oO+3M8przP/0yr3r9/nXHTCRzWfLLPZbLQ2+Ti4r5NDt29jdDLG6YEpzgxNMzAWZSwSvyyhr+T0\nwNSqY6nzOmmoc7OpvZ5kKkPA56Le7yLgd1Hvc+PzOKq6LUVt8HudBOs9S/6SMwyDeDLD5vZ6hsJR\nhsbncv8PzzEYnoMTo5eO9bodtDZ6aW300dLopbnBQ53XRXsoQCqRwutx4nU5sNtz57wK/zbUucvS\nk19t8t4AHF5wPZy/raTJe3punt/+65eZXyIBL6fe72JjSx3NDR5aGrw0N3gvNXhzvZfGOjd2u62m\n17pob/bT3uzntus3AoU3aq7X8cKxEbJZA2y5kgXkas2FazbgwO52bPnbbbZcLzudzlIX8BKeiJJK\nZ3E4bHicDlxOO26XHbfTQUOdC9eCL75abmNhbTabDb/XyTVbmrlmS/Ol27OGQTgSZ2AsysBYlMFw\nlPBUgvHpeC6pXwXV3cRvf+LGUoe+6rLJ3wFPaK2/kb/+IvCA1vp0ieMTQgixiNX25YfJ9bQLNgIj\naw9HCCFEMVabvJ8CfgpAKXUjMKy1Lk/FXgghxLusqmwCoJT6X8APA1ngV7TWb5cyMCGEEEtbdfIW\nQghhnuofiS6EEOJdJHkLIYQFydomi1BKuYCHgc1ABvh5rfW5K465F/hNcjX/Z7TWv7vgvnbgFPAT\nWutnKxR2Sa22DZRSTuBLwDZy769Pa61frGTspbKGNljxcVZSZDsEgX8BolrrwmCGjcA/AB7AAfx3\nrfXC+SGWsdo2yN/+aeA/Ayngv5RqKRHpeS/u48CU1vpW4A+BLyy8UynlB/43uZmm7wXuUErtWXDI\nHwOW/bDmrbYN7gPm8o/7JPCnFY26tFbbBss+zoKK+Xv+BrjyS/o3gK9rrd8P/E7+sVa1qjZQSl0D\n/AxwE/Ap4O5SBSTJe3EfBL6ev/w0cMvCO7XWMeBarfWs1toAJoAWAKXUB4BZ4Fjlwi2L1bbBl8l9\naCE387alMuGWxWrbYNnHWVAxf88v8O7kPc4P/vsH89etarVtcDfwVa11Wmv9ptb6c6UKSJL34jaQ\nSzxorbOAoZS6bHHgwrh2pdS1QA/wav6YzwG/i/Wtqg201imtdWGlov8G/HPFIi69VbVBMY+zmKLb\n4Qp/BtyrlDoF/D3w2XIHWkarbYMeYJNS6kml1DNKqetLFdC6r3krpX6B3DfmQj90xfVFV1BSSu0g\nl5w+rrVOKaU+C/y91npKKVX6YMuklG2w4PZfAW4E7ilhqGVT4vfBlYdYZgWutbTDIn6LXK/zD5VS\ndwP/B/jJNYZYdiVuAxu5ev+HyfXWvwgcWFOAees+eWutv0iuQS9RSj1M7pv27fyJCpvWev6KY7rI\nrax4n9b6SP7mHwEcSqlfJXfC7j1KqY9prU+U+c9YkxK3AUqpT5JL2j++MKFXsxK3wfBKj6tWq22H\nJdwCfCZ/+TvAX5Uw1LIpcRuMAqfyZbUXlVI9pYpTyiaLewr4WP7yPcD3FjnmS8Ava63fLNygtb5F\na32z1vpm4AlyZ5arOnEvY1VtoJTaCvwS8JMLyidWtao2KPJxVrLav6eXH/RYDwBnShxXJa22Db5N\nrlOHUmoXMFCqgGSG5SKUUg5y37w7gCTwc1rrAaXU7wDPkTsxdQR4bcHD/lRr/diC53gYeNjCQwVX\n1Qbkdlf6GeDCgtvvskrPc6E1tMETiz2ukrGXUhHt8BrwDNAEdAIngAeBd8h9ufnzT/VftdZHKxx+\nSay2DbTW31VKfR64K/9Uv6G1fqUUMUnyFkIIC5KyiRBCWJAkbyGEsCBJ3kIIYUGSvIUQwoIkeQsh\nhAVJ8hZCCAuS5C2EEBYkyVsIISzo/wPtXM0Cb+6VRQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f916bb90590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distplot(HOMO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f916bcc7290>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW8AAAD4CAYAAAAjKGdbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nH8WrGefcexJZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f916be36e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distplot(HOMOTrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=10, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.75211047587\n"
     ]
    }
   ],
   "source": [
    "runGrid('extraTrees',maccs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=20, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.613144772035\n"
     ]
    }
   ],
   "source": [
    "runGrid('extraTrees',maccsTrain, HOMOTrain,0.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=10, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.741860482387\n"
     ]
    }
   ],
   "source": [
    "runGrid('extraTrees',atom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=10, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.545062316119\n",
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=20, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.556998768442\n"
     ]
    }
   ],
   "source": [
    "runGrid('extraTrees',atomTrain, HOMOTrain,0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=20, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.668618481362\n"
     ]
    }
   ],
   "source": [
    "runGrid('extraTrees',topo,HOMO,0.65)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=10, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.579223129964\n"
     ]
    }
   ],
   "source": [
    "runGrid('extraTrees',topoTrain, HOMOTrain,0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=10, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.734379461321\n"
     ]
    }
   ],
   "source": [
    "runGrid('extraTrees',morgan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=10, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.606062900449\n"
     ]
    }
   ],
   "source": [
    "runGrid('extraTrees',morganTrain, HOMOTrain,0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combined Fingerprints "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "HOMO = loadNumpy('HOMO')\n",
    "\n",
    "\n",
    "maccs = loadNumpy('maccsCount')\n",
    "ap = loadNumpy('AtomPairCount')\n",
    "maccsAtom = hstack((maccs,atom))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "maccsInvariant = generateInVariantMACCS(maccs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "maccsInvariant saved successfully in .\n"
     ]
    }
   ],
   "source": [
    "saveNumpy(maccsInvariant,'maccsInvariant')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "maccsAtomUniq = hstack((maccsInvariant,atom))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "maccsAtomUniq saved successfully in .\n"
     ]
    }
   ],
   "source": [
    "saveNumpy(maccsAtomUniq,'maccsAtomUniq')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate a MACCS fingerprint removing any features that are invariant across the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def generateInVariantMACCS(MACCS):\n",
    "    nonZeroList = []\n",
    "    for fp in MACCS:\n",
    "        nonZero = nonzero(fp)[0]\n",
    "        nonZeroList += nonZero.tolist()\n",
    "    \n",
    "    inVariantIndices = list(set(nonZeroList))\n",
    "    \n",
    "    maccsInvariant = []\n",
    "    for fp in MACCS:\n",
    "        reducedMolecule = []\n",
    "        for i in inVariantIndices:\n",
    "            reducedMolecule += [fp[i]]\n",
    "            \n",
    "        maccsInvariant += [array(reducedMolecule)]\n",
    "    \n",
    "    return array(maccsInvariant)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import nonzero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "maccsInvariant = generateInVariantMACCS(maccs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "maccsAtomUniq = hstack((maccsInvariant,atom))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=10, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.710252101831\n",
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=20, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.713999866268\n"
     ]
    }
   ],
   "source": [
    "runGrid('extraTrees',maccsAtomUniq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
      "           max_features=0.25, max_leaf_nodes=None,\n",
      "           min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "           n_estimators=100, n_jobs=-1, oob_score=False, random_state=None,\n",
      "           verbose=0, warm_start=False)\n",
      "0.708463229639\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-52-7cc5f19f159c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrunGrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'randomForest'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmaccsAtomUniq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-16-b7c43acd1ac2>\u001b[0m in \u001b[0;36mrunGrid\u001b[0;34m(algorithm, fpType, label, maximum)\u001b[0m\n\u001b[1;32m      9\u001b[0m         \u001b[0mcount\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m         \u001b[0mr2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcrossVal_r2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpType\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mr2\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0mmaximum\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m             \u001b[0;32mprint\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-12-6cf0f8f64887>\u001b[0m in \u001b[0;36mcrossVal_r2\u001b[0;34m(X, y, estimator, CV)\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcrossVal_r2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mCV\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m     \u001b[0mpredicted\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcross_val_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mCV\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mr2_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredicted\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda/lib/python2.7/site-packages/sklearn/model_selection/_validation.pyc\u001b[0m in \u001b[0;36mcross_val_predict\u001b[0;34m(estimator, X, y, groups, cv, n_jobs, verbose, fit_params, pre_dispatch, method)\u001b[0m\n\u001b[1;32m    399\u001b[0m     prediction_blocks = parallel(delayed(_fit_and_predict)(\n\u001b[1;32m    400\u001b[0m         clone(estimator), X, y, train, test, verbose, fit_params, method)\n\u001b[0;32m--> 401\u001b[0;31m         for train, test in cv_iter)\n\u001b[0m\u001b[1;32m    402\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    403\u001b[0m     \u001b[0;31m# Concatenate the predictions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m    756\u001b[0m             \u001b[0;31m# was dispatched. In particular this covers the edge\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    757\u001b[0m             \u001b[0;31m# case of Parallel used with an exhausted iterator.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 758\u001b[0;31m             \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    759\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    760\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    606\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    607\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 608\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    609\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    610\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    569\u001b[0m         \u001b[0mdispatch_timestamp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    570\u001b[0m         \u001b[0mcb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBatchCompletionCallBack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdispatch_timestamp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 571\u001b[0;31m         \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    572\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    573\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/_parallel_backends.pyc\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    107\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 109\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    110\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    111\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/_parallel_backends.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    324\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    325\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 326\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    327\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    328\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda/lib/python2.7/site-packages/sklearn/model_selection/_validation.pyc\u001b[0m in \u001b[0;36m_fit_and_predict\u001b[0;34m(estimator, X, y, train, test, verbose, fit_params, method)\u001b[0m\n\u001b[1;32m    472\u001b[0m         \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    473\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 474\u001b[0;31m         \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    475\u001b[0m     \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    476\u001b[0m     \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda/lib/python2.7/site-packages/sklearn/ensemble/forest.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    324\u001b[0m                     \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrees\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    325\u001b[0m                     verbose=self.verbose, class_weight=self.class_weight)\n\u001b[0;32m--> 326\u001b[0;31m                 for i, t in enumerate(trees))\n\u001b[0m\u001b[1;32m    327\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    328\u001b[0m             \u001b[0;31m# Collect newly grown trees\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m    766\u001b[0m                 \u001b[0;31m# consumption.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    767\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 768\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    769\u001b[0m             \u001b[0;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    770\u001b[0m             \u001b[0melapsed_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    717\u001b[0m                     \u001b[0mensure_ready\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_managed_backend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    718\u001b[0m                     \u001b[0mbackend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabort_everything\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mensure_ready\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mensure_ready\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 719\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mexception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    720\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    721\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "runGrid('randomForest',maccsAtomUniq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=10, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.711763933073\n",
      "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "          max_features='auto', max_leaf_nodes=None,\n",
      "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "          n_estimators=20, n_jobs=-1, oob_score=False, random_state=None,\n",
      "          verbose=0, warm_start=False)\n",
      "0.720554117096\n"
     ]
    }
   ],
   "source": [
    "runGrid('extraTrees',maccsAtom)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}