QSAR Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "file = pd.read_csv(\"https://raw.githubusercontent.com/sawsimeon/HDAC-Chembl/master/hdac.txt\", sep = \"\\t\")\n",
    "query = file.query('STANDARD_TYPE == \"IC50\" and RELATION == \"=\" and STANDARD_UNITS == \"nM\"')\n",
    "query.drop_duplicates(subset = \"CANONICAL_SMILES\")\n",
    "\n",
    "IC50 = query['STANDARD_VALUE']\n",
    "SMILES = query['CANONICAL_SMILES']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CMPD_CHEMBLID            707\n",
       "MOLREGNO                 707\n",
       "PARENT_CMPD_CHEMBLID     707\n",
       "PARENT_MOLREGNO          707\n",
       "MOL_PREF_NAME            131\n",
       "COMPOUND_KEY             707\n",
       "MOLWEIGHT                700\n",
       "ALOGP                    697\n",
       "PSA                      697\n",
       "NUM_RO5_VIOLATIONS       697\n",
       "CANONICAL_SMILES         707\n",
       "ACTIVITY_ID              707\n",
       "STANDARD_TYPE            707\n",
       "RELATION                 707\n",
       "STANDARD_VALUE           707\n",
       "STANDARD_UNITS           707\n",
       "PCHEMBL_VALUE            701\n",
       "ACTIVITY_COMMENT           0\n",
       "DATA_VALIDITY_COMMENT      6\n",
       "POTENTIAL_DUPLICATE      707\n",
       "BAO_ENDPOINT             707\n",
       "UO_UNITS                 707\n",
       "QUDT_UNITS               707\n",
       "ASSAY_ID                 707\n",
       "ASSAY_CHEMBLID           707\n",
       "ASSAY_TYPE               707\n",
       "DESCRIPTION              707\n",
       "ASSAY_SRC_ID             707\n",
       "ASSAY_SRC_DESCRIPTION    707\n",
       "ASSAY_ORGANISM           567\n",
       "ASSAY_STRAIN               0\n",
       "ASSAY_TAX_ID             567\n",
       "CURATED_BY               707\n",
       "BAO_FORMAT               707\n",
       "TID                      707\n",
       "TARGET_CHEMBLID          707\n",
       "TARGET_TYPE              707\n",
       "PROTEIN_ACCESSION        707\n",
       "PREF_NAME                707\n",
       "ORGANISM                 707\n",
       "CONFIDENCE_SCORE         707\n",
       "TARGET_MAPPING           707\n",
       "APD_NAME                 562\n",
       "APD_CONFIDENCE           439\n",
       "DOC_ID                   707\n",
       "DOC_CHEMBLID             707\n",
       "PUBMED_ID                680\n",
       "JOURNAL                  707\n",
       "YEAR                     707\n",
       "VOLUME                   707\n",
       "ISSUE                    648\n",
       "FIRST_PAGE               707\n",
       "CELL_ID                   30\n",
       "CELL_CHEMBL_ID            30\n",
       "CELL_NAME                 30\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query.count(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Standardized molecules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from rdkit.Chem.SaltRemover import SaltRemover\n",
    "from rdkit.Chem import MolFromSmiles,MolToSmiles\n",
    "\n",
    "remover = SaltRemover()\n",
    "len(remover.salts)\n",
    "\n",
    "SMILES_desalt = []\n",
    "\n",
    "for i in SMILES:\n",
    "    mol = MolFromSmiles(i) \n",
    "    mol_desalt = remover.StripMol(mol)\n",
    "    mol_SMILES = MolToSmiles(mol_desalt)\n",
    "    SMILES_desalt.append(mol_SMILES)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write SDF file of Standardized molecules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from rdkit.Chem.rdmolfiles import SDWriter\n",
    "w = SDWriter(\"HDAC.sdf\")\n",
    "for i in SMILES_desalt:\n",
    "    mol = MolFromSmiles(i)\n",
    "    w.write(mol)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Binding the Standardized SMILES with Bioactivity Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "IC50_nm = pd.Series.tolist(IC50)\n",
    "\n",
    "df = pd.DataFrame({'smiles' : SMILES_desalt,\n",
    " 'IC50':IC50_nm\n",
    "  })"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pharsing the SMILES to calculate Morgan Fingerprint Descriptors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from rdkit.Chem import PandasTools, AllChem\n",
    "PandasTools.AddMoleculeColumnToFrame(df, smilesCol = 'smiles')\n",
    "fps = [AllChem.GetMorganFingerprintAsBitVect(m, 1) for m in df['ROMol']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Converting RDkit data structure to numerical descriptors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from rdkit import DataStructs\n",
    "descriptors = []\n",
    "for fp in fps:\n",
    "  arr = np.zeros((1,))\n",
    "  DataStructs.ConvertToNumpyArray(fp, arr)\n",
    "  descriptors.append(arr)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Transformation (Converting IC50 to pIC50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from math import log10\n",
    "import numpy as np\n",
    "np_ic50 = np.array(IC50)\n",
    "\n",
    "pIC50 = []\n",
    "for i in np_ic50:\n",
    "    molar = i*(10**-9) # Converts nM to M\n",
    "    pIC50.append(-log10(molar))\n",
    "\n",
    "\n",
    "y = pIC50\n",
    "X = descriptors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Standardizing\n",
    "Descriptors are standradized before performing PCA analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "X_std = StandardScaler().fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computing Eigenvectors and Eigenvalues\n",
    "The PCA approach is to perform the eigendecomposition on the covariance matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA as sklearnPCA\n",
    "sklearn_pca = sklearnPCA(n_components=2)\n",
    "sklearn_transf = sklearn_pca.fit_transform(X_std.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Obtain Covariance Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cov_mat = sklearn_pca.get_covariance()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Perform an eigendecomposition on the covariance matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "eig_vals, eig_vecs = np.linalg.eig(cov_mat)\n",
    "\n",
    "eig_vals = np.around(eig_vals, decimals=1)\n",
    "eig_vecs = np.around(eig_vecs, decimals = 1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explained Variance Plot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x122e5fcc0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EYPx46NWrnPLy8kLFaGbWKlRUVFBRUZGz8yki6v5AmhIR50l6rI6PIyKOafTk\n0lBgYkSMSLfHpcdOyihzPfBYdS1I0mJgWESsSbf7AfdGxAH1XKPezyVFRLB6NQwZAm+91VjEZmYm\niYjIqruhLvXWWCLivHR1ZER8WOui22d5/rnAwPSX/xvA6cAZtcrMAr4L3J4movXVSaX6ctTqT5HU\nOyKqp+86BXihoSB69YKPPoK1a6F79ywjNzOzZsmm8/7vWe77jIjYBIwFZgOLgNsi4iVJYySdl5a5\nH3hV0svADcB3qo+XdGt6rb0krZA0Ov3oSknPSVoIDAMubCgOCfbeG5YuzSZqMzNriYb6WHqTdKLv\nkL5bUl1r6Ax0yPYC6aPAe9fad0Ot7bH1HHtmPfvPyvb61fbaK0ksQ4c29UgzM2uKhoZ0OQ44G+gD\n/JJPE8u7wCX5DSv3qhOLmZnlV72d9zUFpC9HxJ8KFE9OVXfeA9x3H1x6KfzjH0UOysysxLW08z6b\nPpZDJdW8eS+pW/qIb6sycmRSY3njjWJHYmbWtmWTWEZGxPrqjYhYRzJ+WKvSrh0MGgTLlxc7EjOz\nti2bxNJO0nbVG5J2ALZroHzJGjjQicXMLN+ySSy/B/4i6RxJ5wAPAzPyG1Z+DBkCc+cWOwozs7at\n0c57qBmBeHi6+XBEPJTXqHIks/Mekj6Wo46CefNg992LGJiZWQlraed9VomltaqdWAC+851ktOMf\n/rBIQZmZlbi8PxUmaaikuZLel/SxpE2S3m3uBYttxAh4/PFiR2Fm1nZl08dyDcn4XsuAHYBvAb/N\nZ1D5NHgwLFlS7CjMzNqurGaQjIiXgXYRsSkippNMT9wq9e8Pq1Ylg1KamVnuZZNYPpC0LbBQ0pWS\nLszyuJLUvn3y2PFllxU7EjOztimbBPH1tNxYYAPQF/hyPoPKtyuugKefLnYUZmZtU4NPhUlqB8yM\niP8sXEi5U9dTYQDLlsGxx8KrrxYhKDOzEpfXp8LS+VT6pU1hbcaAAfDee/DKK8WOxMys7clmdOOZ\nwL4kMz1uqN4fEVflN7SWq6/GAsn7LHvsAf/zPwUOysysxBVidOPlwH1p2U4ZS1YkjZC0WNJSSRfX\nU2aypGWSFqaTilXvnyZpjaTnapXvJmm2pCWSHpLUJdt4qh16KDz3XOPlzMysaepNLJJuTlfXR8RP\nai/ZnFxSGcl7MMcB+wFnSNqnVpmRwJ4RMQgYA1yX8fH09NjaxgGPRMTewKNAk9+jP/RQeOIJ2Lix\nqUeamVmIpqyzAAARVUlEQVRDGqqxHCppV+CbaQ2he+aS5fkPA5ZFRGVEbARuA0bVKjMKmAkQEXOA\nLpJ6pdtPAuvqOO8oPh0IcwZwcpbx1DjwwKSv5dZbm3qkmZk1pKHEcj3wF2AfYF6t5Zksz78bsDJj\n+/V0X0NlquooU1vPiFgDEBGrgZ5ZxlNDSvpXrroKPvmkqUebmVl96p3zPiImA5MlXRcR3y5gTM1R\n7xMIEydOrFkvLy+nvLy8ZnvECPjFL+B734Nrr81neGZmpauiooKKioqcnS+voxtLGgpMjIgR6fY4\nICJiUkaZ64HHIuL2dHsxMKy6RiKpH3BvRByQccxLQHlErJHUOz1+3zquX+9TYdXWroW994a//Q32\n2quld2xm1voV4qmwlpgLDJRU/S7M6SSPLWeaBZwFNYlofXVSSSldah9zdrr+DeCe5gbYvTuMHg0z\nZzb3DGZmlinv87FIGgFcTZLEpkXEFZLGkNRcpqRlriEZ2HIDMDoi5qf7bwXKgR7AGmBCRExPHx64\ng2R4mUrgtIhYX8e1G62xADz7bPIm/rx50KdPi2/ZzKxV80RfDcg2sQB8//vJ11/+Mo8BmZm1Ak4s\nDWhKYlm5Eg44AB56CA47LM+BmZmVMCeWBjQlsQBcfz3ceSfMng1lrXZiADOzlin1zvtW5eyzk6fE\n7ryz2JGYmbVeTiwZtt8ezj8/ebfl3XeLHY2ZWevkprBaNm2C885Lpi6+5ZY8BWZmVsLcx9KA5iQW\nSOZqGTQo6Ws54IDGy5uZtSXuY8mDTp1g3DgYP77YkZiZtT6usdTjww9h8GA44gi47jro3DnHwZmZ\nlSjXWPJk++1h/vxkFORLLil2NGZmrYdrLI1YvRr22QeqqmDHHXMUmJlZCXONJc9694bjjkvGEvvw\nw2JHY2ZW+lxjycLmzXDqqclIyFOm+K18M2vbXGMpgLIyuOkmWLwYLrus2NGYmZU211ia4LXX4NBD\n4cknYd/PTCtmZtY2uMZSQP37w69+lfS5PPtssaMxMytN9c55b3U76yzYuBGGD4c//xmOPrrYEZmZ\nlZa811gkjZC0WNJSSRfXU2aypGWSFko6qLFjJU2Q9Lqk+ekyIt/3kemcc+APf4BTTkn6XszM7FN5\n7WORVAYsBYYDq4C5wOkRsTijzEhgbEScIOlw4OqIGNrQsZImAO9FxFWNXD+nfSy1LVoEX/lKMjHY\nDTfAdtvl7VJmZgVT6n0shwHLIqIyIjYCtwGjapUZBcwEiIg5QBdJvbI4ttk3nSv77QdPPw0rVsDl\nlxc7GjOz0pDvxLIbsDJj+/V0XzZlGjt2bNp0NlVSl9yF3DQdO8I118Dvfgc33wxt+CE7M7OslGLn\nfTY1kWuBn0ZESPoZcBVwTl0FJ06cWLNeXl5OeXl5DkLc0uDBcM89cNpp8PDDMG0abLNNzi9jZpYX\nFRUVVFRU5Ox8+e5jGQpMjIgR6fY4ICJiUkaZ64HHIuL2dHsxMAwY0Nix6f5+wL0R8ZmZU/Ldx1Lb\nBx8kfS5lZXDHHbDDDgW7tJlZzpR6H8tcYKCkfpK2BU4HZtUqMws4C2oS0fqIWNPQsZJ6Zxx/CvBC\nfm8jOx06wN13Q9euyXD7y5YVOyIzs8LLa1NYRGySNBaYTZLEpkXES5LGJB/HlIi4X9Lxkl4GNgCj\nGzo2PfWV6WPJm4HXgDH5vI+m2GYbmDEDrrwyeZFyzhzYeediR2VmVjge0iWPvv99eOAB+D//B776\n1WRuFzOzUuc57xtQ7MQSAQ89lExzvM02MH067L9/0cIxM8tKqfexbNUkGDECFiyA886D8nKYMMGP\nJJtZ2+YaSwGtWgX//u/J5GHjx8NRRxU7IjOzz3KNpRXZdVd46qlkAMtTT01mpZw50zNTmlnb4hpL\nkbzzTvIy5dSpSVPZOefAt78NffsWOzIz29q5xtJKdemS1FoefBCeeCJ5ufKgg+DMM+H554sdnZlZ\n8zmxlIC99oJf/xpefTVJLl/6EnzxizBrljv6zaz1cVNYCfrXv+D22+E3v4F165J5X8aOTWawNDPL\nN7/H0oDWmliqRcBzz8Gtt8KUKUnz2bBhcPLJcMIJsO22xY7QzNoiJ5YGtPbEkmnzZli6FB59FG65\nBRYvhpEj4fDDk2azfff1m/1mlhtOLA1oS4mlttdfTzr+58yB2bOTN/uPOgqOPjoZPqZTp2JHaGat\nlRNLA9pyYskUAc8+C/Pnw733JrWaY49N+maGDoUBA4odoZm1Jk4sDdhaEktta9fCXXfB//2/yQuZ\n7drBN74BJ56YNJ25yczMGuLE0oCtNbFkioAXX4SbbkoSzYcfJgnmq1+FI48sdnRmVoqcWBrgxLKl\n6iazBx+E3/42qckcfTQccMCnXzt0KHaUZlZsJZ9YJI0Afs2nk3VNqqPMZGAkyURfZ0fEwoaOldQN\nuB3oRzLR12kR8U4d53ViqUdE8mTZ3/8OCxfCk0/CkiWw++5w4IHJ8P777pu8sDlgQJKEzGzrUNJD\nukgqA64BjgP2A86QtE+tMiOBPSNiEMlMkNdncew44JGI2Bt4FPhhPu8j3yoqKgp+TSlJHOeck7yI\nuWBBMn7ZH/+YjMC8YUMyQOYXvwgdOyaJZtiwCsaPT/bPmQPr1xc87KwU4/vZVK0hRnCcudZa4myp\nfA/pchiwLCIqI2IjcBswqlaZUcBMgIiYA3SR1KuRY0cBM9L1GcDJ+b2N/CqVf2zbbANDhsDXvw5X\nXJEMKfPaa/D228m7MzvvXMF22yVNad/5DvTpk0wBMGxY8nDA+efD1VcnT6YtXJgkqmIole9nQ1pD\njOA4c621xNlSeZ3zHtgNWJmx/TpJwmiszG6NHNsrItYARMRqST1zGbRtaccdkyax/fdPplmuFgFV\nVUkT2ooV8O67yfrs2cn2q68myWrAgCQJ9egB3bsnX3feOfm6447J0qnTp8uOO8J227n5zay1yndi\naY7mtOu5I6UIpCRh9OlT9+cR8M9/Jgmmqip5DPqf/0yW5cuT7Q0b4P33k+W995Llgw+Sp9fat4ft\nt0+W7bZLhrDZZpstl/btkzgyl8pKePxxKCv77Gf1LYW2ZAnMm1f46zaV48yt6ji7doWbby52NHkU\nEXlbgKHAgxnb44CLa5W5HvhqxvZioFdDxwIvkdRaAHoDL9Vz/fDixYsXL01fWvK7P981lrnAQEn9\ngDeA04EzapWZBXwXuF3SUGB9RKyR9HYDx84CzgYmAd8A7qnr4i15qsHMzJonr4klIjZJGgvM5tNH\nhl+SNCb5OKZExP2Sjpf0MsnjxqMbOjY99STgDknfBCqB0/J5H2Zmlr02/YKkmZkVXpucQVLSCEmL\nJS2VdHGRY5kmaY2k5zL2dZM0W9ISSQ9J6pLx2Q8lLZP0kqRjCxhnH0mPSlok6XlJ3yvFWCVtJ2mO\npAVpnBNKMc70umWS5kuaVcIxvibp2fT7+XQJx9lF0h/T6y6SdHipxSlpr/T7OD/9+o6k75VanOl1\nL5T0gqTnJP1e0rY5jTOfnffFWEiS5cskb+VvAywE9iliPJ8HDgKey9g3CfjfdP1i4Ip0fTCwgKSJ\nsn96HypQnL2Bg9L1jsASYJ8SjbVD+rUd8BTJY+ilGOeFwC3ArBL+ub8CdKu1rxTjvAkYna63B7qU\nYpwZ8ZYBq4C+pRYnsGv6c9823b6dpK86Z3EW7BtdwB/oUOCBjO3PPIlWhJj6sWViWcyWT7UtritW\n4AHg8CLFfDfwxVKOFegAPAN8rtTiBPoADwPlfJpYSirG9FqvAj1q7SupOIHOwPI69pdUnLViOxZ4\nohTjJEkslUC3NFnMyvX/9bbYFFbfC5elpGdkvOAJVL/gWTv2KooQu6T+JLWsp6j1MiolEGvaxLQA\nWA08HBFzSzDOXwE/IHl0s1qpxQhJfA9LmivpWyUa5wDgbUnT02amKZI6lGCcmb4K3Jqul1ScEbEK\n+CWwIr3mOxHxSC7jbIuJpTUqmScoJHUE7gQuiIj3+WxsRY81IjZHxMEktYLDJO1HCcUp6QRgTSSD\nqTb0yHvRv5fAURFxCHA88F1JR1NC38tUe+AQ4LdprBtI/ooutTgBkLQNcBLwx3RXScUpqSvJsFj9\nSGovO0r6zzrianacbTGxVAG7Z2z3SfeVkjVKxkNDUm/gzXR/FUmbbLWCxi6pPUlSuTkiqt8NKslY\nASLiXaACGEFpxXkUcJKkV4A/AMdIuhlYXUIxAhARb6Rf3yJp/jyM0vpeQtLqsDIinkm3/0SSaEot\nzmojgXkR8Xa6XWpxfhF4JSLWRsQm4M/AkbmMsy0mlpqXMiVtS/Ji5awixyS2/Mu1+gVP2PIFz1nA\n6ekTGgOAgcDThQoSuBF4MSKuzthXUrFK2qn6aRVJOwBfIhmJoWTijIhLImL3iNiD5N/foxHxdeDe\nUokRQFKHtIaKpB1J+gWep4S+lwBp88xKSXulu4YDi0otzgxnkPxBUa3U4lwBDJW0vSSRfD9fzGmc\nhezQKtRC8hfsEmAZMK7IsdxK8nTIR+kPdDRJp9kjaYyzga4Z5X9I8tTFS8CxBYzzKGATyVN0C4D5\n6fexeynFCgxJY1sIPAeMT/eXVJwZ1x7Gp533JRUjSd9F9c/7+er/K6UWZ3rdA0n+aFwI3EXyVFgp\nxtkBeAvolLGvFOOckF7zOZIR4rfJZZx+QdLMzHKqLTaFmZlZETmxmJlZTjmxmJlZTjmxmJlZTjmx\nmJlZTjmxmJlZTjmxmG2lJF0gaftix2Ftj99jMdtKSXoVODQi1hY7FmtbXGOxNicdzuclSbdIelHS\nHdV/mUv6nKS/SVoo6SlJO6bl/yrpmXQZWs95z9Knk2LNyLjWX9LzPSypT7p/uqRrJf1D0suShimZ\n9O1FSTdmnPM9SVelky49LKlHuv+g9NiFkv6UMYzNY5KuUDLZ2WJJR6X7yyRdme5fKOncdP+w9Jjq\nSbJuTvefTzIA4WNp/GVpzM+l93hBvn4+thUo1BACXrwUaiEZtXUzMDTdngZcRDJsxXLgkHR/R5I/\nrrbn00mPBgJz6zjnYJL5Krql213Tr7OAr6Xro4E/p+vTgVvT9ZOAd4DB6fYzwAHp+mbg9HT9x8Dk\ndP1Z4PPp+k+Aq9L1x4Cfp+sjSaYNADgXuCRd35Zk+JN+JEPKrAN2IRmv7u/AkWm5mkm+SAZ1nJ1x\nv52L/XP00noX11isrVoREU+l67eQzOS5N7AqIuYDRMT7EbGZ5BfxVCXTR/8R2LeO8x0D/DEi1qXH\nrk/3H8GnAw7eTDLmWrV706/PA6sj4sV0exHJTHyQJJY7MuOU1BnoEhFPpvtnAP+Wcd670q/zSJIH\nJANInqVknpo5JOM+DUo/ezoi3oiIIBlrq/ramYOjvgIMkHS1pOOA9+r4HphlpX2xAzArkOrOxLrm\nR7mQ5Bf/AZLaAf9qxnnr8lH6dXPGevV2ff/3Goqz9nk3ZZxHwPkR8XBmQUnDal0785hPLxqxXtKB\nwHHAGOA04JwGYjCrl2ss1lbtLunwdP1M4AmSUVt7SzoUkknN0kTSBXgjLXsW0K6O8z0KfEVS9/TY\nbun+v5MMkw7wtfQ6dakvUZQBp6br/wk8Gck8M2ur+0+ArwOPN3Leh4DvKJlTB0mDlMyy2JB3Sab9\nJe3baRcRfyZpkju4kWPN6uUai7VVS0hmRJxO0vR0fURslPRV4Jp0LpcPSCY9uhb4k6SzgAdJZijc\nQkS8KOlS4HFJn5AMNf9N4HvAdEn/QzJc+ujqQ2qfop71DSSzYP4YWEMypS0k82HckMb5ShbnnUrS\nxDU/nWPjTeDkOr4vmcf/DnhQUhVJrW26pLK0zLg6jjXLih83tjZHUj/gvogYUuxYGiPpvYjoVOw4\nzHLJTWHWVrWWv5haS5xmWXONxczMcso1FjMzyyknFjMzyyknFjMzyyknFjMzyyknFjMzyyknFjMz\ny6n/B/rpwo7Rnq7iAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11af7b6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "pca = sklearnPCA().fit(X_std)\n",
    "plt.plot(pca.explained_variance_ratio_, label='pca explained variance')\n",
    "plt.legend(loc='best')\n",
    "plt.xlabel('pca components')\n",
    "plt.ylabel('fraction of total variance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda/lib/python3.5/site-packages/matplotlib/axes/_axes.py:519: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
      "  warnings.warn(\"No labelled objects found. \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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pycu2bZcRDrtwOifxelN4vbUcO9bH9PQuEokxfL5GPJ7cgaQc579TlU2Djio7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\nBh2llFIFo0FHKaVUwfz/L86/sXUQtrAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113ffbf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.plot(sklearn_transf[0:707,0],sklearn_transf[0:707,1], 'o', markersize=7, color='blue', alpha=0.3)\n",
    "#plt.plot(sklearn_transf[0:2751,0], sklearn_transf[0:2751,1], '^', markersize=7, color='red', alpha=0.5, label='class2')\n",
    "\n",
    "plt.xlabel('PC 1')\n",
    "plt.ylabel('PC 2')\n",
    "plt.xlim([-12,12])\n",
    "plt.ylim([-12,12])\n",
    "plt.legend()\n",
    "plt.title('Principal Component Analysis of Histone Deacetylase Inhibitors')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.cross_validation import train_test_split, cross_val_predict, cross_val_score\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "import numpy as np\n",
    "\n",
    "def build_models_regression(x, y, model):\n",
    "   \n",
    "    \n",
    "    if model == \"training\":\n",
    "        X_train, X_test, y_train, y_test = train_test_split(x, y,\n",
    "        test_size = 0.2)\n",
    "        rf = RandomForestRegressor(n_estimators = 500, max_features = 'sqrt')\n",
    "        rf.fit(X_train, y_train)\n",
    "        rmse = np.sqrt(mean_squared_error(y_train, rf.predict(X_train)))\n",
    "        r2 = r2_score(y_train, rf.predict(X_train))\n",
    "        print(\"The internal set's R-Squared is %.2f  and Root Mean Squared Error is %.2f\" % (r2, rmse))\n",
    "    #rmse = np.sqrt(mean_squared_error(y_train, rf.predict(X_train)))\n",
    "    #r2 = r2_score(y_train, rf.predict(X_train))\n",
    "    #print \"The internal set's R-Squared is %.2f  and Root Mean Squared Error is %.2f\" % (r2, rmse)\n",
    "    elif model == \"CV\":\n",
    "        X_train, X_test, y_train, y_test = train_test_split(x, y,\n",
    "        test_size = 0.2)\n",
    "        rf = RandomForestRegressor(n_estimators = 500, max_features = 'sqrt')\n",
    "        rf.fit(X_train, y_train)\n",
    "        #rmse = np.sqrt(mean_squared_error(y_train, cross_val_predict(rf, X_train, y_train, cv = 10)))\n",
    "        rmse = np.sqrt(mean_squared_error(y_train, cross_val_predict(rf, X_train, y_train, cv = 10)))\n",
    "        r2 = r2_score(y_train, cross_val_predict(rf, X_train, y_train, cv = 10))\n",
    "        print(\"The 10 fold set's R-Squared is %.2f  and Root Mean Squared Error is %.2f\" % (r2, rmse))\n",
    "    elif model == \"testing\":\n",
    "        X_train, X_test, y_train, y_test = train_test_split(x, y,\n",
    "        test_size = 0.2)\n",
    "        rf = RandomForestRegressor(n_estimators = 500, max_features = 'sqrt')\n",
    "        rf.fit(X_train, y_train)\n",
    "        rmse = np.sqrt(mean_squared_error(y_test, rf.predict(X_test)))\n",
    "        r2 = r2_score(y_test, rf.predict(X_test))\n",
    "        print(\"The external set's R-Squared is %.2f  and Root Mean Squared Error is %.2f\" % (r2, rmse))\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The internal set's R-Squared is 0.88  and Root Mean Squared Error is 0.39\n"
     ]
    }
   ],
   "source": [
    "build_models_regression(x = descriptors, y = pIC50, model = \"training\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10-Fold Cross Validation's Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The 10 fold set's R-Squared is 0.53  and Root Mean Squared Error is 0.75\n"
     ]
    }
   ],
   "source": [
    "build_models_regression(x = descriptors, y = pIC50, model = \"CV\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The external set's R-Squared is 0.51  and Root Mean Squared Error is 0.78\n"
     ]
    }
   ],
   "source": [
    "build_models_regression(x = descriptors, y = pIC50, model = \"testing\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scrambling for evaluating the predictive models (function)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Scrambling pIC50\n",
    "import numpy as np\n",
    "def scrambling(x, y, model):\n",
    "   \n",
    "    \n",
    "    if model == \"R2\":\n",
    "        for i in range(100):\n",
    "            \n",
    "            np.random.shuffle(y)\n",
    "            X_train, X_test, y_train, y_test = train_test_split(x, y,\n",
    "            test_size = 0.2)\n",
    "            rf = RandomForestRegressor(n_estimators = 500, max_features = 'sqrt', n_jobs = -1)\n",
    "            rf.fit(X_train, y_train)\n",
    "            rmse = np.sqrt(mean_squared_error(y_train, rf.predict(X_train)))\n",
    "            r2 = abs(r2_score(y_train, rf.predict(X_train)))\n",
    "            print(\"The Scrambled  R-Squared is %.2f\" % r2)\n",
    "    #rmse = np.sqrt(mean_squared_error(y_train, rf.predict(X_train)))\n",
    "    #r2 = r2_score(y_train, rf.predict(X_train))\n",
    "    #print \"The internal set's R-Squared is %.2f  and Root Mean Squared Error is %.2f\" % (r2, rmse)\n",
    "    elif model == \"Q2\":\n",
    "        for i in range(100):\n",
    "            np.random.shuffle(y)\n",
    "            X_train, X_test, y_train, y_test = train_test_split(x, y,\n",
    "            test_size = 0.2)\n",
    "            rf = RandomForestRegressor(n_estimators = 500, max_features = 'sqrt', n_jobs = -1)\n",
    "            rf.fit(X_train, y_train)\n",
    "            #rmse = np.sqrt(mean_squared_error(y_train, cross_val_predict(rf, X_train, y_train, cv = 10)))\n",
    "            r2 = cross_val_score(rf, X_train, y_train, cv = 10)\n",
    "            r2 = abs(r2.mean())\n",
    "            #r2 = r2_score(y_train, cross_val_predict(rf, X_train, y_train, cv = 10))\n",
    "            print(\"The Scrambled Q2 is %.2f\" % r2)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scrambled R2 for 100 times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Scrambled  R-Squared is 0.62\n",
      "The Scrambled  R-Squared is 0.60\n",
      "The Scrambled  R-Squared is 0.66\n",
      "The Scrambled  R-Squared is 0.64\n",
      "The Scrambled  R-Squared is 0.64\n",
      "The Scrambled  R-Squared is 0.59\n",
      "The Scrambled  R-Squared is 0.61\n",
      "The Scrambled  R-Squared is 0.59\n",
      "The Scrambled  R-Squared is 0.58\n",
      "The Scrambled  R-Squared is 0.64\n",
      "The Scrambled  R-Squared is 0.65\n",
      "The Scrambled  R-Squared is 0.63\n",
      "The Scrambled  R-Squared is 0.63\n",
      "The Scrambled  R-Squared is 0.62\n",
      "The Scrambled  R-Squared is 0.64\n",
      "The Scrambled  R-Squared is 0.68\n",
      "The Scrambled  R-Squared is 0.62\n",
      "The Scrambled  R-Squared is 0.66\n",
      "The Scrambled  R-Squared is 0.61\n",
      "The Scrambled  R-Squared is 0.61\n",
      "The Scrambled  R-Squared is 0.60\n",
      "The Scrambled  R-Squared is 0.64\n",
      "The Scrambled  R-Squared is 0.59\n",
      "The Scrambled  R-Squared is 0.62\n",
      "The Scrambled  R-Squared is 0.65\n",
      "The Scrambled  R-Squared is 0.63\n",
      "The Scrambled  R-Squared is 0.65\n",
      "The Scrambled  R-Squared is 0.62\n",
      "The Scrambled  R-Squared is 0.62\n",
      "The Scrambled  R-Squared is 0.61\n",
      "The Scrambled  R-Squared is 0.62\n",
      "The Scrambled  R-Squared is 0.57\n",
      "The Scrambled  R-Squared is 0.64\n",
      "The Scrambled  R-Squared is 0.64\n",
      "The Scrambled  R-Squared is 0.60\n",
      "The Scrambled  R-Squared is 0.59\n",
      "The Scrambled  R-Squared is 0.58\n",
      "The Scrambled  R-Squared is 0.61\n",
      "The Scrambled  R-Squared is 0.63\n",
      "The Scrambled  R-Squared is 0.58\n",
      "The Scrambled  R-Squared is 0.62\n",
      "The Scrambled  R-Squared is 0.68\n",
      "The Scrambled  R-Squared is 0.60\n",
      "The Scrambled  R-Squared is 0.59\n",
      "The Scrambled  R-Squared is 0.62\n",
      "The Scrambled  R-Squared is 0.60\n",
      "The Scrambled  R-Squared is 0.64\n",
      "The Scrambled  R-Squared is 0.63\n",
      "The Scrambled  R-Squared is 0.61\n",
      "The Scrambled  R-Squared is 0.62\n",
      "The Scrambled  R-Squared is 0.59\n",
      "The Scrambled  R-Squared is 0.63\n",
      "The Scrambled  R-Squared is 0.63\n",
      "The Scrambled  R-Squared is 0.64\n",
      "The Scrambled  R-Squared is 0.60\n",
      "The Scrambled  R-Squared is 0.66\n",
      "The Scrambled  R-Squared is 0.61\n",
      "The Scrambled  R-Squared is 0.61\n",
      "The Scrambled  R-Squared is 0.63\n",
      "The Scrambled  R-Squared is 0.65\n",
      "The Scrambled  R-Squared is 0.63\n",
      "The Scrambled  R-Squared is 0.62\n",
      "The Scrambled  R-Squared is 0.58\n",
      "The Scrambled  R-Squared is 0.60\n",
      "The Scrambled  R-Squared is 0.61\n",
      "The Scrambled  R-Squared is 0.64\n",
      "The Scrambled  R-Squared is 0.61\n",
      "The Scrambled  R-Squared is 0.59\n",
      "The Scrambled  R-Squared is 0.69\n",
      "The Scrambled  R-Squared is 0.60\n",
      "The Scrambled  R-Squared is 0.60\n",
      "The Scrambled  R-Squared is 0.59\n",
      "The Scrambled  R-Squared is 0.62\n",
      "The Scrambled  R-Squared is 0.63\n",
      "The Scrambled  R-Squared is 0.61\n",
      "The Scrambled  R-Squared is 0.60\n",
      "The Scrambled  R-Squared is 0.60\n",
      "The Scrambled  R-Squared is 0.66\n",
      "The Scrambled  R-Squared is 0.65\n",
      "The Scrambled  R-Squared is 0.64\n",
      "The Scrambled  R-Squared is 0.63\n",
      "The Scrambled  R-Squared is 0.61\n",
      "The Scrambled  R-Squared is 0.60\n",
      "The Scrambled  R-Squared is 0.66\n",
      "The Scrambled  R-Squared is 0.62\n",
      "The Scrambled  R-Squared is 0.63\n",
      "The Scrambled  R-Squared is 0.61\n",
      "The Scrambled  R-Squared is 0.62\n",
      "The Scrambled  R-Squared is 0.59\n",
      "The Scrambled  R-Squared is 0.63\n",
      "The Scrambled  R-Squared is 0.61\n",
      "The Scrambled  R-Squared is 0.61\n",
      "The Scrambled  R-Squared is 0.61\n",
      "The Scrambled  R-Squared is 0.62\n",
      "The Scrambled  R-Squared is 0.61\n",
      "The Scrambled  R-Squared is 0.65\n",
      "The Scrambled  R-Squared is 0.60\n",
      "The Scrambled  R-Squared is 0.66\n",
      "The Scrambled  R-Squared is 0.62\n",
      "The Scrambled  R-Squared is 0.58\n"
     ]
    }
   ],
   "source": [
    "scrambling(x = descriptors, y = pIC50, model = \"R2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scrambed Q2 for 100 times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Scrambled Q2 is 0.24\n",
      "The Scrambled Q2 is 0.14\n",
      "The Scrambled Q2 is 0.33\n",
      "The Scrambled Q2 is 0.19\n",
      "The Scrambled Q2 is 0.33\n",
      "The Scrambled Q2 is 0.25\n",
      "The Scrambled Q2 is 0.22\n",
      "The Scrambled Q2 is 0.22\n",
      "The Scrambled Q2 is 0.32\n",
      "The Scrambled Q2 is 0.29\n",
      "The Scrambled Q2 is 0.21\n",
      "The Scrambled Q2 is 0.28\n",
      "The Scrambled Q2 is 0.20\n",
      "The Scrambled Q2 is 0.28\n",
      "The Scrambled Q2 is 0.23\n",
      "The Scrambled Q2 is 0.23\n",
      "The Scrambled Q2 is 0.30\n",
      "The Scrambled Q2 is 0.28\n",
      "The Scrambled Q2 is 0.28\n",
      "The Scrambled Q2 is 0.21\n",
      "The Scrambled Q2 is 0.22\n",
      "The Scrambled Q2 is 0.13\n",
      "The Scrambled Q2 is 0.36\n",
      "The Scrambled Q2 is 0.26\n",
      "The Scrambled Q2 is 0.28\n",
      "The Scrambled Q2 is 0.25\n",
      "The Scrambled Q2 is 0.29\n",
      "The Scrambled Q2 is 0.21\n",
      "The Scrambled Q2 is 0.26\n",
      "The Scrambled Q2 is 0.31\n",
      "The Scrambled Q2 is 0.21\n",
      "The Scrambled Q2 is 0.38\n",
      "The Scrambled Q2 is 0.20\n",
      "The Scrambled Q2 is 0.24\n",
      "The Scrambled Q2 is 0.33\n",
      "The Scrambled Q2 is 0.26\n",
      "The Scrambled Q2 is 0.27\n",
      "The Scrambled Q2 is 0.15\n",
      "The Scrambled Q2 is 0.22\n",
      "The Scrambled Q2 is 0.26\n",
      "The Scrambled Q2 is 0.27\n",
      "The Scrambled Q2 is 0.16\n",
      "The Scrambled Q2 is 0.31\n",
      "The Scrambled Q2 is 0.30\n",
      "The Scrambled Q2 is 0.31\n",
      "The Scrambled Q2 is 0.24\n",
      "The Scrambled Q2 is 0.31\n",
      "The Scrambled Q2 is 0.32\n",
      "The Scrambled Q2 is 0.31\n",
      "The Scrambled Q2 is 0.26\n",
      "The Scrambled Q2 is 0.26\n",
      "The Scrambled Q2 is 0.24\n",
      "The Scrambled Q2 is 0.29\n",
      "The Scrambled Q2 is 0.26\n",
      "The Scrambled Q2 is 0.23\n",
      "The Scrambled Q2 is 0.30\n",
      "The Scrambled Q2 is 0.33\n",
      "The Scrambled Q2 is 0.21\n",
      "The Scrambled Q2 is 0.21\n",
      "The Scrambled Q2 is 0.30\n",
      "The Scrambled Q2 is 0.24\n",
      "The Scrambled Q2 is 0.23\n",
      "The Scrambled Q2 is 0.12\n",
      "The Scrambled Q2 is 0.27\n",
      "The Scrambled Q2 is 0.30\n",
      "The Scrambled Q2 is 0.26\n",
      "The Scrambled Q2 is 0.30\n",
      "The Scrambled Q2 is 0.29\n",
      "The Scrambled Q2 is 0.24\n",
      "The Scrambled Q2 is 0.27\n",
      "The Scrambled Q2 is 0.28\n",
      "The Scrambled Q2 is 0.30\n",
      "The Scrambled Q2 is 0.20\n",
      "The Scrambled Q2 is 0.25\n",
      "The Scrambled Q2 is 0.23\n",
      "The Scrambled Q2 is 0.32\n",
      "The Scrambled Q2 is 0.32\n",
      "The Scrambled Q2 is 0.28\n",
      "The Scrambled Q2 is 0.21\n",
      "The Scrambled Q2 is 0.16\n",
      "The Scrambled Q2 is 0.20\n",
      "The Scrambled Q2 is 0.25\n",
      "The Scrambled Q2 is 0.25\n",
      "The Scrambled Q2 is 0.32\n",
      "The Scrambled Q2 is 0.23\n",
      "The Scrambled Q2 is 0.32\n",
      "The Scrambled Q2 is 0.25\n",
      "The Scrambled Q2 is 0.22\n",
      "The Scrambled Q2 is 0.20\n",
      "The Scrambled Q2 is 0.24\n",
      "The Scrambled Q2 is 0.19\n",
      "The Scrambled Q2 is 0.23\n",
      "The Scrambled Q2 is 0.24\n",
      "The Scrambled Q2 is 0.19\n",
      "The Scrambled Q2 is 0.20\n",
      "The Scrambled Q2 is 0.28\n",
      "The Scrambled Q2 is 0.24\n",
      "The Scrambled Q2 is 0.27\n",
      "The Scrambled Q2 is 0.22\n",
      "The Scrambled Q2 is 0.27\n"
     ]
    }
   ],
   "source": [
    "scrambling(x = descriptors, y = pIC50, model = \"Q2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Importance plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='sqrt', max_leaf_nodes=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           n_estimators=500, n_jobs=1, oob_score=False, random_state=None,\n",
       "           verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.cross_validation import train_test_split, cross_val_predict, cross_val_score\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(descriptors, pIC50,\n",
    "        test_size = 0.2)\n",
    "rf = RandomForestRegressor(n_estimators = 500, max_features = 'sqrt')\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "importances = rf.feature_importances_\n",
    "indices = np.argsort(importances)\n",
    "features = np.array(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.title('Feature Importances')\n",
    "plt.barh(range(len(indices)), importances[indices], color='b', align='center')\n",
    "#plt.yticks(range(len(indices)), features.shape[1][indices])\n",
    "plt.xlabel('Relative Importance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "important_features = []\n",
    "for x,i in enumerate(rf.feature_importances_):\n",
    "    if i>np.average(rf.feature_importances_):\n",
    "        important_features.append((x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 8, 11, 13, 14, 18, 29, 31, 35, 41, 45, 62, 67, 80, 91, 93, 114, 115, 116, 117, 119, 121, 140, 144, 147, 155, 157, 162, 170, 173, 191, 192, 193, 197, 202, 216, 222, 229, 231, 233, 237, 245, 248, 251, 253, 255, 256, 281, 283, 294, 301, 305, 314, 317, 318, 319, 322, 323, 325, 335, 341, 343, 345, 350, 352, 362, 372, 378, 384, 386, 387, 389, 392, 394, 401, 407, 427, 441, 458, 464, 473, 478, 486, 487, 488, 504, 507, 523, 527, 550, 552, 561, 573, 576, 585, 586, 590, 612, 622, 623, 636, 652, 656, 667, 674, 675, 684, 689, 692, 694, 695, 699, 713, 715, 724, 725, 736, 739, 740, 744, 745, 746, 747, 750, 753, 782, 787, 788, 789, 794, 798, 800, 802, 806, 807, 809, 832, 835, 838, 841, 844, 855, 864, 875, 878, 879, 881, 885, 890, 898, 910, 915, 917, 926, 927, 935, 940, 941, 950, 953, 956, 961, 974, 980, 995, 1004, 1017, 1019, 1024, 1027, 1028, 1034, 1056, 1057, 1060, 1083, 1086, 1088, 1089, 1093, 1096, 1097, 1101, 1114, 1122, 1139, 1145, 1147, 1152, 1160, 1162, 1163, 1164, 1168, 1171, 1181, 1182, 1195, 1210, 1224, 1228, 1233, 1236, 1238, 1241, 1243, 1252, 1256, 1257, 1274, 1279, 1280, 1292, 1294, 1303, 1308, 1309, 1312, 1324, 1325, 1333, 1349, 1351, 1356, 1357, 1380, 1385, 1386, 1391, 1402, 1409, 1416, 1417, 1430, 1432, 1449, 1452, 1453, 1455, 1457, 1459, 1476, 1477, 1480, 1495, 1502, 1506, 1535, 1536, 1542, 1544, 1545, 1564, 1567, 1569, 1575, 1582, 1602, 1603, 1604, 1607, 1614, 1623, 1631, 1634, 1640, 1657, 1670, 1683, 1693, 1700, 1722, 1726, 1731, 1737, 1738, 1742, 1745, 1747, 1750, 1753, 1754, 1758, 1765, 1781, 1783, 1791, 1808, 1810, 1816, 1820, 1821, 1823, 1825, 1828, 1838, 1840, 1854, 1855, 1859, 1861, 1866, 1867, 1871, 1873, 1877, 1878, 1879, 1899, 1905, 1906, 1911, 1917, 1919, 1928, 1934, 1946, 1949, 1950, 1951, 1956, 1959, 1963, 1970, 1980, 1982, 2000, 2033, 2034]\n"
     ]
    }
   ],
   "source": [
    "print(important_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RcWPBeptZH/N5T/2vaEj9AHgb8FR6fxfgZuA/j7dSRLyvSfFl4yx/DnBOk/I7gdcUrKuZ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\nhGPAaAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12dd306a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#plt.figure(figsize=(6 * 1.618, 6))\n",
    "index = np.arange(len(important_features))\n",
    "bar_width = 0.35\n",
    "plt.bar(index, important_features, color='black', alpha=0.5)\n",
    "plt.xlabel('features')\n",
    "plt.ylabel('importance')\n",
    "plt.title('Feature importance')\n",
    "plt.xticks(index + bar_width, important_features)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate Substructure Fingerprint Descriptors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing AUTOGEN_HDAC_1 in HDAC.sdf (1/707). \n",
      "Processing AUTOGEN_HDAC_2 in HDAC.sdf (2/707). \n",
      "Processing AUTOGEN_HDAC_3 in HDAC.sdf (3/707). \n",
      "Processing AUTOGEN_HDAC_4 in HDAC.sdf (4/707). \n",
      "Processing AUTOGEN_HDAC_5 in HDAC.sdf (5/707). \n",
      "Processing AUTOGEN_HDAC_6 in HDAC.sdf (6/707). Average speed: 1.32 s/mol.\n",
      "Processing AUTOGEN_HDAC_7 in HDAC.sdf (7/707). Average speed: 1.32 s/mol.\n",
      "Processing AUTOGEN_HDAC_9 in HDAC.sdf (9/707). Average speed: 0.30 s/mol.\n",
      "Processing AUTOGEN_HDAC_8 in HDAC.sdf (8/707). Average speed: 0.34 s/mol.\n",
      "Processing AUTOGEN_HDAC_10 in HDAC.sdf (10/707). Average speed: 0.25 s/mol.\n",
      "Processing AUTOGEN_HDAC_11 in HDAC.sdf (11/707). Average speed: 0.26 s/mol.\n",
      "Processing AUTOGEN_HDAC_12 in HDAC.sdf (12/707). Average speed: 0.24 s/mol.\n",
      "Processing AUTOGEN_HDAC_13 in HDAC.sdf (13/707). Average speed: 0.21 s/mol.\n",
      "Processing AUTOGEN_HDAC_14 in HDAC.sdf (14/707). Average speed: 0.19 s/mol.\n",
      "Processing AUTOGEN_HDAC_15 in HDAC.sdf (15/707). Average speed: 0.16 s/mol.\n",
      "Processing AUTOGEN_HDAC_16 in HDAC.sdf (16/707). Average speed: 0.17 s/mol.\n",
      "Processing AUTOGEN_HDAC_18 in HDAC.sdf (18/707). Average speed: 0.14 s/mol.\n",
      "Processing AUTOGEN_HDAC_17 in HDAC.sdf (17/707). Average speed: 0.15 s/mol.\n",
      "Processing AUTOGEN_HDAC_20 in HDAC.sdf (20/707). Average speed: 0.13 s/mol.\n",
      "Processing AUTOGEN_HDAC_19 in HDAC.sdf (19/707). Average speed: 0.14 s/mol.\n",
      "Processing AUTOGEN_HDAC_21 in HDAC.sdf (21/707). Average speed: 0.13 s/mol.\n",
      "Processing AUTOGEN_HDAC_22 in HDAC.sdf (22/707). Average speed: 0.12 s/mol.\n",
      "Processing AUTOGEN_HDAC_23 in HDAC.sdf (23/707). Average speed: 0.12 s/mol.\n",
      "Processing AUTOGEN_HDAC_24 in HDAC.sdf (24/707). Average speed: 0.12 s/mol.\n",
      "Processing AUTOGEN_HDAC_25 in HDAC.sdf (25/707). Average speed: 0.11 s/mol.\n",
      "Processing AUTOGEN_HDAC_26 in HDAC.sdf (26/707). Average speed: 0.11 s/mol.\n",
      "Processing AUTOGEN_HDAC_27 in HDAC.sdf (27/707). Average speed: 0.10 s/mol.\n",
      "Processing AUTOGEN_HDAC_28 in HDAC.sdf (28/707). Average speed: 0.10 s/mol.\n",
      "Processing AUTOGEN_HDAC_29 in HDAC.sdf (29/707). Average speed: 0.09 s/mol.\n",
      "Processing AUTOGEN_HDAC_30 in HDAC.sdf (30/707). Average speed: 0.09 s/mol.\n",
      "Processing AUTOGEN_HDAC_31 in HDAC.sdf (31/707). Average speed: 0.09 s/mol.\n",
      "Processing AUTOGEN_HDAC_32 in HDAC.sdf (32/707). Average speed: 0.09 s/mol.\n",
      "Processing AUTOGEN_HDAC_33 in HDAC.sdf (33/707). Average speed: 0.09 s/mol.\n",
      "Processing AUTOGEN_HDAC_34 in HDAC.sdf (34/707). Average speed: 0.09 s/mol.\n",
      "Processing AUTOGEN_HDAC_35 in HDAC.sdf (35/707). Average speed: 0.08 s/mol.\n",
      "Processing AUTOGEN_HDAC_36 in HDAC.sdf (36/707). Average speed: 0.09 s/mol.\n",
      "Processing AUTOGEN_HDAC_38 in HDAC.sdf (38/707). Average speed: 0.08 s/mol.\n",
      "Processing AUTOGEN_HDAC_37 in HDAC.sdf (37/707). Average speed: 0.08 s/mol.\n",
      "Processing AUTOGEN_HDAC_39 in HDAC.sdf (39/707). Average speed: 0.08 s/mol.\n",
      "Processing AUTOGEN_HDAC_41 in HDAC.sdf (41/707). Average speed: 0.08 s/mol.\n",
      "Processing AUTOGEN_HDAC_40 in HDAC.sdf (40/707). Average speed: 0.08 s/mol.\n",
      "Processing AUTOGEN_HDAC_42 in HDAC.sdf (42/707). Average speed: 0.08 s/mol.\n",
      "Processing AUTOGEN_HDAC_43 in HDAC.sdf (43/707). Average speed: 0.07 s/mol.\n",
      "Processing AUTOGEN_HDAC_44 in HDAC.sdf (44/707). Average speed: 0.07 s/mol.\n",
      "Processing AUTOGEN_HDAC_45 in HDAC.sdf (45/707). Average speed: 0.07 s/mol.\n",
      "Processing AUTOGEN_HDAC_46 in HDAC.sdf (46/707). Average speed: 0.07 s/mol.\n",
      "Processing AUTOGEN_HDAC_47 in HDAC.sdf (47/707). Average speed: 0.07 s/mol.\n",
      "Processing AUTOGEN_HDAC_48 in HDAC.sdf (48/707). Average speed: 0.07 s/mol.\n",
      "Processing AUTOGEN_HDAC_49 in HDAC.sdf (49/707). Average speed: 0.07 s/mol.\n",
      "Processing AUTOGEN_HDAC_50 in HDAC.sdf (50/707). Average speed: 0.07 s/mol.\n",
      "Processing AUTOGEN_HDAC_51 in HDAC.sdf (51/707). Average speed: 0.07 s/mol.\n",
      "Processing AUTOGEN_HDAC_52 in HDAC.sdf (52/707). Average speed: 0.07 s/mol.\n",
      "Processing AUTOGEN_HDAC_53 in HDAC.sdf (53/707). Average speed: 0.06 s/mol.\n",
      "Processing AUTOGEN_HDAC_54 in HDAC.sdf (54/707). Average speed: 0.06 s/mol.\n",
      "Processing AUTOGEN_HDAC_55 in HDAC.sdf (55/707). Average speed: 0.06 s/mol.\n",
      "Processing AUTOGEN_HDAC_56 in HDAC.sdf (56/707). Average speed: 0.06 s/mol.\n",
      "Processing AUTOGEN_HDAC_57 in HDAC.sdf (57/707). Average speed: 0.06 s/mol.\n",
      "Processing AUTOGEN_HDAC_58 in HDAC.sdf (58/707). Average speed: 0.06 s/mol.\n",
      "Processing AUTOGEN_HDAC_59 in HDAC.sdf (59/707). Average speed: 0.06 s/mol.\n",
      "Processing AUTOGEN_HDAC_60 in HDAC.sdf (60/707). Average speed: 0.06 s/mol.\n",
      "Processing AUTOGEN_HDAC_61 in HDAC.sdf (61/707). Average speed: 0.06 s/mol.\n",
      "Processing AUTOGEN_HDAC_62 in HDAC.sdf (62/707). Average speed: 0.06 s/mol.\n",
      "Processing AUTOGEN_HDAC_63 in HDAC.sdf (63/707). Average speed: 0.06 s/mol.\n",
      "Processing AUTOGEN_HDAC_64 in HDAC.sdf (64/707). Average speed: 0.06 s/mol.\n",
      "Processing AUTOGEN_HDAC_65 in HDAC.sdf (65/707). Average speed: 0.06 s/mol.\n",
      "Processing AUTOGEN_HDAC_66 in HDAC.sdf (66/707). Average speed: 0.06 s/mol.\n",
      "Processing AUTOGEN_HDAC_67 in HDAC.sdf (67/707). Average speed: 0.06 s/mol.\n",
      "Processing AUTOGEN_HDAC_68 in HDAC.sdf (68/707). Average speed: 0.06 s/mol.\n",
      "Processing AUTOGEN_HDAC_69 in HDAC.sdf (69/707). Average speed: 0.06 s/mol.\n",
      "Processing AUTOGEN_HDAC_70 in HDAC.sdf (70/707). Average speed: 0.06 s/mol.\n",
      "Processing AUTOGEN_HDAC_71 in HDAC.sdf (71/707). Average speed: 0.06 s/mol.\n",
      "Processing AUTOGEN_HDAC_72 in HDAC.sdf (72/707). Average speed: 0.05 s/mol.\n",
      "Processing AUTOGEN_HDAC_73 in HDAC.sdf (73/707). Average speed: 0.05 s/mol.\n",
      "Processing AUTOGEN_HDAC_74 in HDAC.sdf (74/707). Average speed: 0.05 s/mol.\n",
      "Processing AUTOGEN_HDAC_75 in HDAC.sdf (75/707). Average speed: 0.05 s/mol.\n",
      "Processing AUTOGEN_HDAC_76 in HDAC.sdf (76/707). Average speed: 0.05 s/mol.\n",
      "Processing AUTOGEN_HDAC_77 in HDAC.sdf (77/707). Average speed: 0.05 s/mol.\n",
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      "Processing AUTOGEN_HDAC_540 in HDAC.sdf (540/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_539 in HDAC.sdf (539/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_541 in HDAC.sdf (541/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_542 in HDAC.sdf (542/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_543 in HDAC.sdf (543/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_544 in HDAC.sdf (544/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_545 in HDAC.sdf (545/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_546 in HDAC.sdf (546/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_547 in HDAC.sdf (547/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_548 in HDAC.sdf (548/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_549 in HDAC.sdf (549/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_550 in HDAC.sdf (550/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_551 in HDAC.sdf (551/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_552 in HDAC.sdf (552/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_553 in HDAC.sdf (553/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_554 in HDAC.sdf (554/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_555 in HDAC.sdf (555/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_556 in HDAC.sdf (556/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_557 in HDAC.sdf (557/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_558 in HDAC.sdf (558/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_559 in HDAC.sdf (559/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_560 in HDAC.sdf (560/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_561 in HDAC.sdf (561/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_562 in HDAC.sdf (562/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_563 in HDAC.sdf (563/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_564 in HDAC.sdf (564/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_565 in HDAC.sdf (565/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_566 in HDAC.sdf (566/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_567 in HDAC.sdf (567/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_568 in HDAC.sdf (568/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_569 in HDAC.sdf (569/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_570 in HDAC.sdf (570/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_571 in HDAC.sdf (571/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_572 in HDAC.sdf (572/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_573 in HDAC.sdf (573/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_574 in HDAC.sdf (574/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_575 in HDAC.sdf (575/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_577 in HDAC.sdf (577/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_576 in HDAC.sdf (576/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_578 in HDAC.sdf (578/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_579 in HDAC.sdf (579/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_580 in HDAC.sdf (580/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_581 in HDAC.sdf (581/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_582 in HDAC.sdf (582/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_583 in HDAC.sdf (583/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_585 in HDAC.sdf (585/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_584 in HDAC.sdf (584/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_586 in HDAC.sdf (586/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_587 in HDAC.sdf (587/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_588 in HDAC.sdf (588/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_589 in HDAC.sdf (589/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_590 in HDAC.sdf (590/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_591 in HDAC.sdf (591/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_592 in HDAC.sdf (592/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_593 in HDAC.sdf (593/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_594 in HDAC.sdf (594/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_595 in HDAC.sdf (595/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_596 in HDAC.sdf (596/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_597 in HDAC.sdf (597/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_598 in HDAC.sdf (598/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_599 in HDAC.sdf (599/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_600 in HDAC.sdf (600/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_601 in HDAC.sdf (601/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_602 in HDAC.sdf (602/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_603 in HDAC.sdf (603/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_605 in HDAC.sdf (605/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_604 in HDAC.sdf (604/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_606 in HDAC.sdf (606/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_607 in HDAC.sdf (607/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_608 in HDAC.sdf (608/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_609 in HDAC.sdf (609/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_610 in HDAC.sdf (610/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_611 in HDAC.sdf (611/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_612 in HDAC.sdf (612/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_613 in HDAC.sdf (613/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_614 in HDAC.sdf (614/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_615 in HDAC.sdf (615/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_616 in HDAC.sdf (616/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_617 in HDAC.sdf (617/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_618 in HDAC.sdf (618/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_619 in HDAC.sdf (619/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_620 in HDAC.sdf (620/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_621 in HDAC.sdf (621/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_622 in HDAC.sdf (622/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_623 in HDAC.sdf (623/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_624 in HDAC.sdf (624/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_625 in HDAC.sdf (625/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_626 in HDAC.sdf (626/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_627 in HDAC.sdf (627/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_628 in HDAC.sdf (628/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_629 in HDAC.sdf (629/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_630 in HDAC.sdf (630/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_631 in HDAC.sdf (631/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_632 in HDAC.sdf (632/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_633 in HDAC.sdf (633/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_634 in HDAC.sdf (634/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_635 in HDAC.sdf (635/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_636 in HDAC.sdf (636/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_637 in HDAC.sdf (637/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_639 in HDAC.sdf (639/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_638 in HDAC.sdf (638/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_640 in HDAC.sdf (640/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_641 in HDAC.sdf (641/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_642 in HDAC.sdf (642/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_643 in HDAC.sdf (643/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_644 in HDAC.sdf (644/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_645 in HDAC.sdf (645/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_646 in HDAC.sdf (646/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_647 in HDAC.sdf (647/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_648 in HDAC.sdf (648/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_649 in HDAC.sdf (649/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_650 in HDAC.sdf (650/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_651 in HDAC.sdf (651/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_652 in HDAC.sdf (652/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_653 in HDAC.sdf (653/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_654 in HDAC.sdf (654/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_655 in HDAC.sdf (655/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_656 in HDAC.sdf (656/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_657 in HDAC.sdf (657/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_658 in HDAC.sdf (658/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_659 in HDAC.sdf (659/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_660 in HDAC.sdf (660/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_661 in HDAC.sdf (661/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_662 in HDAC.sdf (662/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_663 in HDAC.sdf (663/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_664 in HDAC.sdf (664/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_665 in HDAC.sdf (665/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_666 in HDAC.sdf (666/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_667 in HDAC.sdf (667/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_668 in HDAC.sdf (668/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_669 in HDAC.sdf (669/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_670 in HDAC.sdf (670/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_671 in HDAC.sdf (671/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_672 in HDAC.sdf (672/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_673 in HDAC.sdf (673/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_674 in HDAC.sdf (674/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_675 in HDAC.sdf (675/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_676 in HDAC.sdf (676/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_677 in HDAC.sdf (677/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_678 in HDAC.sdf (678/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_679 in HDAC.sdf (679/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_680 in HDAC.sdf (680/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_681 in HDAC.sdf (681/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_682 in HDAC.sdf (682/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_683 in HDAC.sdf (683/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_684 in HDAC.sdf (684/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_685 in HDAC.sdf (685/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_686 in HDAC.sdf (686/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_687 in HDAC.sdf (687/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_688 in HDAC.sdf (688/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_689 in HDAC.sdf (689/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_690 in HDAC.sdf (690/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_691 in HDAC.sdf (691/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_692 in HDAC.sdf (692/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_693 in HDAC.sdf (693/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_694 in HDAC.sdf (694/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_695 in HDAC.sdf (695/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_697 in HDAC.sdf (697/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_696 in HDAC.sdf (696/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_698 in HDAC.sdf (698/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_699 in HDAC.sdf (699/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_700 in HDAC.sdf (700/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_701 in HDAC.sdf (701/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_702 in HDAC.sdf (702/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_704 in HDAC.sdf (704/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_703 in HDAC.sdf (703/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_705 in HDAC.sdf (705/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_706 in HDAC.sdf (706/707). Average speed: 0.04 s/mol.\n",
      "Processing AUTOGEN_HDAC_707 in HDAC.sdf (707/707). Average speed: 0.04 s/mol.\n",
      "Descriptor calculation completed in 26.810 secs . Average speed: 0.04 s/mol.\n"
     ]
    }
   ],
   "source": [
    "! java -jar /Users/saw/Downloads/HDAC-Chembl/PaDEL-Descriptor/PaDEL-Descriptor.jar -fingerprints -descriptortypes /Users/saw/Downloads/HDAC-Chembl/PaDEL-Descriptor/descriptors_SubFP.xml  -dir /Users/saw/Downloads/HDAC-Chembl -file /Users/saw/Downloads/HDAC-Chembl/PaDEL-Descriptor/descriptors_output_file.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SubFPC1</th>\n",
       "      <th>SubFPC2</th>\n",
       "      <th>SubFPC3</th>\n",
       "      <th>SubFPC4</th>\n",
       "      <th>SubFPC5</th>\n",
       "      <th>SubFPC6</th>\n",
       "      <th>SubFPC7</th>\n",
       "      <th>SubFPC8</th>\n",
       "      <th>SubFPC9</th>\n",
       "      <th>SubFPC10</th>\n",
       "      <th>SubFPC11</th>\n",
       "      <th>SubFPC12</th>\n",
       "      <th>SubFPC13</th>\n",
       "      <th>SubFPC14</th>\n",
       "      <th>SubFPC15</th>\n",
       "      <th>SubFPC16</th>\n",
       "      <th>SubFPC17</th>\n",
       "      <th>SubFPC18</th>\n",
       "      <th>SubFPC19</th>\n",
       "      <th>SubFPC20</th>\n",
       "      <th>SubFPC21</th>\n",
       "      <th>SubFPC22</th>\n",
       "      <th>SubFPC23</th>\n",
       "      <th>SubFPC24</th>\n",
       "      <th>SubFPC25</th>\n",
       "      <th>SubFPC26</th>\n",
       "      <th>SubFPC27</th>\n",
       "      <th>SubFPC28</th>\n",
       "      <th>SubFPC29</th>\n",
       "      <th>SubFPC30</th>\n",
       "      <th>SubFPC31</th>\n",
       "      <th>SubFPC32</th>\n",
       "      <th>SubFPC33</th>\n",
       "      <th>SubFPC34</th>\n",
       "      <th>SubFPC35</th>\n",
       "      <th>SubFPC36</th>\n",
       "      <th>SubFPC37</th>\n",
       "      <th>SubFPC38</th>\n",
       "      <th>SubFPC39</th>\n",
       "      <th>SubFPC40</th>\n",
       "      <th>SubFPC41</th>\n",
       "      <th>SubFPC42</th>\n",
       "      <th>SubFPC43</th>\n",
       "      <th>SubFPC44</th>\n",
       "      <th>SubFPC45</th>\n",
       "      <th>SubFPC46</th>\n",
       "      <th>SubFPC47</th>\n",
       "      <th>SubFPC48</th>\n",
       "      <th>SubFPC49</th>\n",
       "      <th>SubFPC50</th>\n",
       "      <th>SubFPC51</th>\n",
       "      <th>SubFPC52</th>\n",
       "      <th>SubFPC53</th>\n",
       "      <th>SubFPC54</th>\n",
       "      <th>SubFPC55</th>\n",
       "      <th>SubFPC56</th>\n",
       "      <th>SubFPC57</th>\n",
       "      <th>SubFPC58</th>\n",
       "      <th>SubFPC59</th>\n",
       "      <th>SubFPC60</th>\n",
       "      <th>SubFPC61</th>\n",
       "      <th>SubFPC62</th>\n",
       "      <th>SubFPC63</th>\n",
       "      <th>SubFPC64</th>\n",
       "      <th>SubFPC65</th>\n",
       "      <th>SubFPC66</th>\n",
       "      <th>SubFPC67</th>\n",
       "      <th>SubFPC68</th>\n",
       "      <th>SubFPC69</th>\n",
       "      <th>SubFPC70</th>\n",
       "      <th>SubFPC71</th>\n",
       "      <th>SubFPC72</th>\n",
       "      <th>SubFPC73</th>\n",
       "      <th>SubFPC74</th>\n",
       "      <th>SubFPC75</th>\n",
       "      <th>SubFPC76</th>\n",
       "      <th>SubFPC77</th>\n",
       "      <th>SubFPC78</th>\n",
       "      <th>SubFPC79</th>\n",
       "      <th>SubFPC80</th>\n",
       "      <th>SubFPC81</th>\n",
       "      <th>SubFPC82</th>\n",
       "      <th>SubFPC83</th>\n",
       "      <th>SubFPC84</th>\n",
       "      <th>SubFPC85</th>\n",
       "      <th>SubFPC86</th>\n",
       "      <th>SubFPC87</th>\n",
       "      <th>SubFPC88</th>\n",
       "      <th>SubFPC89</th>\n",
       "      <th>SubFPC90</th>\n",
       "      <th>SubFPC91</th>\n",
       "      <th>SubFPC92</th>\n",
       "      <th>SubFPC93</th>\n",
       "      <th>SubFPC94</th>\n",
       "      <th>SubFPC95</th>\n",
       "      <th>SubFPC96</th>\n",
       "      <th>SubFPC97</th>\n",
       "      <th>SubFPC98</th>\n",
       "      <th>SubFPC99</th>\n",
       "      <th>SubFPC100</th>\n",
       "      <th>SubFPC101</th>\n",
       "      <th>SubFPC102</th>\n",
       "      <th>SubFPC103</th>\n",
       "      <th>SubFPC104</th>\n",
       "      <th>SubFPC105</th>\n",
       "      <th>SubFPC106</th>\n",
       "      <th>SubFPC107</th>\n",
       "      <th>SubFPC108</th>\n",
       "      <th>SubFPC109</th>\n",
       "      <th>SubFPC110</th>\n",
       "      <th>SubFPC111</th>\n",
       "      <th>SubFPC112</th>\n",
       "      <th>SubFPC113</th>\n",
       "      <th>SubFPC114</th>\n",
       "      <th>SubFPC115</th>\n",
       "      <th>SubFPC116</th>\n",
       "      <th>SubFPC117</th>\n",
       "      <th>SubFPC118</th>\n",
       "      <th>SubFPC119</th>\n",
       "      <th>SubFPC120</th>\n",
       "      <th>SubFPC121</th>\n",
       "      <th>SubFPC122</th>\n",
       "      <th>SubFPC123</th>\n",
       "      <th>SubFPC124</th>\n",
       "      <th>SubFPC125</th>\n",
       "      <th>SubFPC126</th>\n",
       "      <th>SubFPC127</th>\n",
       "      <th>SubFPC128</th>\n",
       "      <th>SubFPC129</th>\n",
       "      <th>SubFPC130</th>\n",
       "      <th>SubFPC131</th>\n",
       "      <th>SubFPC132</th>\n",
       "      <th>SubFPC133</th>\n",
       "      <th>SubFPC134</th>\n",
       "      <th>SubFPC135</th>\n",
       "      <th>SubFPC136</th>\n",
       "      <th>SubFPC137</th>\n",
       "      <th>SubFPC138</th>\n",
       "      <th>SubFPC139</th>\n",
       "      <th>SubFPC140</th>\n",
       "      <th>SubFPC141</th>\n",
       "      <th>SubFPC142</th>\n",
       "      <th>SubFPC143</th>\n",
       "      <th>SubFPC144</th>\n",
       "      <th>SubFPC145</th>\n",
       "      <th>SubFPC146</th>\n",
       "      <th>SubFPC147</th>\n",
       "      <th>SubFPC148</th>\n",
       "      <th>SubFPC149</th>\n",
       "      <th>SubFPC150</th>\n",
       "      <th>SubFPC151</th>\n",
       "      <th>SubFPC152</th>\n",
       "      <th>SubFPC153</th>\n",
       "      <th>SubFPC154</th>\n",
       "      <th>SubFPC155</th>\n",
       "      <th>SubFPC156</th>\n",
       "      <th>SubFPC157</th>\n",
       "      <th>SubFPC158</th>\n",
       "      <th>SubFPC159</th>\n",
       "      <th>SubFPC160</th>\n",
       "      <th>SubFPC161</th>\n",
       "      <th>SubFPC162</th>\n",
       "      <th>SubFPC163</th>\n",
       "      <th>SubFPC164</th>\n",
       "      <th>SubFPC165</th>\n",
       "      <th>SubFPC166</th>\n",
       "      <th>SubFPC167</th>\n",
       "      <th>SubFPC168</th>\n",
       "      <th>SubFPC169</th>\n",
       "      <th>SubFPC170</th>\n",
       "      <th>SubFPC171</th>\n",
       "      <th>SubFPC172</th>\n",
       "      <th>SubFPC173</th>\n",
       "      <th>SubFPC174</th>\n",
       "      <th>SubFPC175</th>\n",
       "      <th>SubFPC176</th>\n",
       "      <th>SubFPC177</th>\n",
       "      <th>SubFPC178</th>\n",
       "      <th>SubFPC179</th>\n",
       "      <th>SubFPC180</th>\n",
       "      <th>SubFPC181</th>\n",
       "      <th>SubFPC182</th>\n",
       "      <th>SubFPC183</th>\n",
       "      <th>SubFPC184</th>\n",
       "      <th>SubFPC185</th>\n",
       "      <th>SubFPC186</th>\n",
       "      <th>SubFPC187</th>\n",
       "      <th>SubFPC188</th>\n",
       "      <th>SubFPC189</th>\n",
       "      <th>SubFPC190</th>\n",
       "      <th>SubFPC191</th>\n",
       "      <th>SubFPC192</th>\n",
       "      <th>SubFPC193</th>\n",
       "      <th>SubFPC194</th>\n",
       "      <th>SubFPC195</th>\n",
       "      <th>SubFPC196</th>\n",
       "      <th>SubFPC197</th>\n",
       "      <th>SubFPC198</th>\n",
       "      <th>SubFPC199</th>\n",
       "      <th>SubFPC200</th>\n",
       "      <th>SubFPC201</th>\n",
       "      <th>SubFPC202</th>\n",
       "      <th>SubFPC203</th>\n",
       "      <th>SubFPC204</th>\n",
       "      <th>SubFPC205</th>\n",
       "      <th>SubFPC206</th>\n",
       "      <th>SubFPC207</th>\n",
       "      <th>SubFPC208</th>\n",
       "      <th>SubFPC209</th>\n",
       "      <th>SubFPC210</th>\n",
       "      <th>SubFPC211</th>\n",
       "      <th>SubFPC212</th>\n",
       "      <th>SubFPC213</th>\n",
       "      <th>SubFPC214</th>\n",
       "      <th>SubFPC215</th>\n",
       "      <th>SubFPC216</th>\n",
       "      <th>SubFPC217</th>\n",
       "      <th>SubFPC218</th>\n",
       "      <th>SubFPC219</th>\n",
       "      <th>SubFPC220</th>\n",
       "      <th>SubFPC221</th>\n",
       "      <th>SubFPC222</th>\n",
       "      <th>SubFPC223</th>\n",
       "      <th>SubFPC224</th>\n",
       "      <th>SubFPC225</th>\n",
       "      <th>SubFPC226</th>\n",
       "      <th>SubFPC227</th>\n",
       "      <th>SubFPC228</th>\n",
       "      <th>SubFPC229</th>\n",
       "      <th>SubFPC230</th>\n",
       "      <th>SubFPC231</th>\n",
       "      <th>SubFPC232</th>\n",
       "      <th>SubFPC233</th>\n",
       "      <th>SubFPC234</th>\n",
       "      <th>SubFPC235</th>\n",
       "      <th>SubFPC236</th>\n",
       "      <th>SubFPC237</th>\n",
       "      <th>SubFPC238</th>\n",
       "      <th>SubFPC239</th>\n",
       "      <th>SubFPC240</th>\n",
       "      <th>SubFPC241</th>\n",
       "      <th>SubFPC242</th>\n",
       "      <th>SubFPC243</th>\n",
       "      <th>SubFPC244</th>\n",
       "      <th>SubFPC245</th>\n",
       "      <th>SubFPC246</th>\n",
       "      <th>SubFPC247</th>\n",
       "      <th>SubFPC248</th>\n",
       "      <th>SubFPC249</th>\n",
       "      <th>SubFPC250</th>\n",
       "      <th>SubFPC251</th>\n",
       "      <th>SubFPC252</th>\n",
       "      <th>SubFPC253</th>\n",
       "      <th>SubFPC254</th>\n",
       "      <th>SubFPC255</th>\n",
       "      <th>SubFPC256</th>\n",
       "      <th>SubFPC257</th>\n",
       "      <th>SubFPC258</th>\n",
       "      <th>SubFPC259</th>\n",
       "      <th>SubFPC260</th>\n",
       "      <th>SubFPC261</th>\n",
       "      <th>SubFPC262</th>\n",
       "      <th>SubFPC263</th>\n",
       "      <th>SubFPC264</th>\n",
       "      <th>SubFPC265</th>\n",
       "      <th>SubFPC266</th>\n",
       "      <th>SubFPC267</th>\n",
       "      <th>SubFPC268</th>\n",
       "      <th>SubFPC269</th>\n",
       "      <th>SubFPC270</th>\n",
       "      <th>SubFPC271</th>\n",
       "      <th>SubFPC272</th>\n",
       "      <th>SubFPC273</th>\n",
       "      <th>SubFPC274</th>\n",
       "      <th>SubFPC275</th>\n",
       "      <th>SubFPC276</th>\n",
       "      <th>SubFPC277</th>\n",
       "      <th>SubFPC278</th>\n",
       "      <th>SubFPC279</th>\n",
       "      <th>SubFPC280</th>\n",
       "      <th>SubFPC281</th>\n",
       "      <th>SubFPC282</th>\n",
       "      <th>SubFPC283</th>\n",
       "      <th>SubFPC284</th>\n",
       "      <th>SubFPC285</th>\n",
       "      <th>SubFPC286</th>\n",
       "      <th>SubFPC287</th>\n",
       "      <th>SubFPC288</th>\n",
       "      <th>SubFPC289</th>\n",
       "      <th>SubFPC290</th>\n",
       "      <th>SubFPC291</th>\n",
       "      <th>SubFPC292</th>\n",
       "      <th>SubFPC293</th>\n",
       "      <th>SubFPC294</th>\n",
       "      <th>SubFPC295</th>\n",
       "      <th>SubFPC296</th>\n",
       "      <th>SubFPC297</th>\n",
       "      <th>SubFPC298</th>\n",
       "      <th>SubFPC299</th>\n",
       "      <th>SubFPC300</th>\n",
       "      <th>SubFPC301</th>\n",
       "      <th>SubFPC302</th>\n",
       "      <th>SubFPC303</th>\n",
       "      <th>SubFPC304</th>\n",
       "      <th>SubFPC305</th>\n",
       "      <th>SubFPC306</th>\n",
       "      <th>SubFPC307</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "   SubFPC1  SubFPC2  SubFPC3  SubFPC4  SubFPC5  SubFPC6  SubFPC7  SubFPC8  SubFPC9  SubFPC10    ...      SubFPC298  SubFPC299  SubFPC300  SubFPC301  SubFPC302  SubFPC303  SubFPC304  SubFPC305  SubFPC306  SubFPC307\n",
       "0      0.0      6.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0       0.0    ...            0.0        0.0       11.0       11.0       10.0        0.0        0.0        0.0        0.0       10.0\n",
       "1      0.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0       0.0    ...            0.0        0.0        0.0        0.0       10.0        0.0        0.0        0.0        0.0        9.0\n",
       "2      0.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0       0.0    ...            0.0        0.0        0.0        0.0       12.0        0.0        0.0        0.0        0.0       12.0\n",
       "3      0.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0       0.0    ...            0.0        0.0        8.0        8.0        9.0        0.0        0.0        0.0        0.0       21.0\n",
       "4      0.0      6.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0       0.0    ...            0.0        0.0       11.0       11.0       10.0        0.0        0.0        0.0        0.0       10.0\n",
       "\n",
       "[5 rows x 307 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subFPC = pd.read_csv(\"PaDEL-Descriptor/descriptors_output_file.csv\")\n",
    "subFPC.drop(\"Name\", inplace = True, axis = 1)\n",
    "subFPC.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Random Forest Model using Substructure Fingerprint Count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='sqrt', max_leaf_nodes=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           n_estimators=500, n_jobs=1, oob_score=False, random_state=None,\n",
       "           verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(subFPC, pIC50,\n",
    "        test_size = 0.2)\n",
    "rf = RandomForestRegressor(n_estimators = 500, max_features = 'sqrt')\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Importance plot using SubStructure Fingerprint Count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MAEAZauPvU/30K6sOA9jozervV+/551cdBrDR6+t7/pZVq9VUq9VWPZ8+fXop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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12d9c6630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "importance = np.mean([tree.feature_importances_ for tree in rf.estimators_],axis=0)\n",
    "std = np.std([tree.feature_importances_ for tree in rf.estimators_],axis=0)\n",
    "ind = np.argsort(importance)[-20:]\n",
    "plt.figure(figsize=(15,15))\n",
    "plt.title(\"Feature Importances for Random Forest (500 trees)\", size = 20)\n",
    "plt.barh(range(20),importance[ind],\n",
    "       color=\"r\", xerr=std[ind], alpha=0.4, align=\"center\")\n",
    "plt.yticks(range(20), subFPC.columns[ind])\n",
    "plt.ylim([-1, len(ind)])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}