07_External_test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# check best performing model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# load the model from disk\n",
    "\n",
    "import pickle\n",
    "\n",
    "path = r'Result/corr cutoff 0.7 single/RF model/'\n",
    "\n",
    "AtomPairs2DFingerprintCount = pickle.load(open(path +'AtomPairs2DFingerprintCount.csv_model0.sav', 'rb'))\n",
    "AtomPairs2DFingerprinter\t= pickle.load(open(path +'AtomPairs2DFingerprinter.csv_model0.sav', 'rb'))\n",
    "EStateFingerprinter\t\t\t= pickle.load(open(path +'EStateFingerprinter.csv_model0.sav', 'rb'))\n",
    "ExtendedFingerprinter\t\t= pickle.load(open(path +'ExtendedFingerprinter.csv_model0.sav', 'rb'))\n",
    "Fingerprinter\t\t\t\t= pickle.load(open(path +'Fingerprinter.csv_model0.sav', 'rb'))\n",
    "GraphOnlyFingerprinter\t\t= pickle.load(open(path +'GraphOnlyFingerprinter.csv_model0.sav', 'rb'))\n",
    "KlekotaRothFingerprintCount = pickle.load(open(path +'KlekotaRothFingerprintCount.csv_model0.sav', 'rb'))\n",
    "KlekotaRothFingerprinter\t= pickle.load(open(path +'KlekotaRothFingerprinter.csv_model0.sav', 'rb'))\n",
    "MACCSFingerprinter\t\t\t= pickle.load(open(path +'MACCSFingerprinter.csv_model0.sav', 'rb'))\n",
    "PubchemFingerprinter\t\t= pickle.load(open(path +'PubchemFingerprinter.csv_model0.sav', 'rb'))\n",
    "SubstructureFingerprintCount= pickle.load(open(path +'SubstructureFingerprintCount.csv_model0.sav', 'rb'))\n",
    "SubstructureFingerprinter\t= pickle.load(open(path +'SubstructureFingerprinter.csv_model0.sav', 'rb'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "model 9 show best prefance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# testing external"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df1  = pd.read_csv('model/Test_gra_ER_alpha.csv' , header = 0)\n",
    "df2  = pd.read_csv('model/Test_les_ER_alpha.csv' , header = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(283, 19)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df1),len(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Convert IC50 to pIC 50\n",
    "\n",
    "from math import log10\n",
    "\n",
    "def pIC50(df0):\n",
    "    pIC50 = []\n",
    "\n",
    "    for i in df0.value:\n",
    "        molar = i*(10**-9) # Converts nM to M\n",
    "        pIC50.append(-log10(molar))\n",
    "    #Y = pIC50\n",
    "\n",
    "    df0['pIC50'] = pIC50\n",
    "    df0 = df0.drop('value', 1)\n",
    "        \n",
    "    return df0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1 = pIC50(df1)\n",
    "df2 = pIC50(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path = r'Fingerprint/'\n",
    "\n",
    "Fp1  = pd.read_csv(path + 'Test_gra_ER_alpha-SubstructureFingerprintCount.csv'  , header = 0)\n",
    "Fp2  = pd.read_csv(path + 'Test_les_ER_alpha-SubstructureFingerprintCount.csv'  , header = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Fp1 = Fp1.rename(columns = {'Name':'chemblId'})\n",
    "Fp2 = Fp2.rename(columns = {'Name':'chemblId'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "def normalized (Fp):\n",
    "    \n",
    "    chemblId = Fp.chemblId\n",
    "    Fp_ix = Fp.ix[:,1:]\n",
    "    min_max_scaler = preprocessing.MinMaxScaler()\n",
    "    np_scaled = min_max_scaler.fit_transform(Fp_ix)\n",
    "    Fp_normalized = pd.DataFrame(np_scaled)\n",
    "    Fp_normalized\n",
    "    Fp_normalized = pd.DataFrame(np_scaled, columns=Fp_ix.columns)\n",
    "    Fp_normalized['chemblId'] = chemblId\n",
    "    \n",
    "    return Fp_normalized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zeromtmu/anaconda2/envs/ER_alpha/lib/python2.7/site-packages/ipykernel_launcher.py:6: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "Fp1_n  = normalized (Fp1 )\n",
    "Fp2_n  = normalized (Fp2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "raw1  = df1.merge(Fp1_n , on='chemblId', how='outer')\n",
    "raw2  = df2.merge(Fp2_n , on='chemblId', how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "raw1 .to_csv('QSAR/Test_gra_ER_alpha.csv' , sep=',' ,index=False)\n",
    "raw2 .to_csv('QSAR/Test_les_ER_alpha.csv' , sep=',' ,index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Ext1  = raw1 [['chemblId', 'pIC50'\n",
    "             ,'SubFPC169', 'SubFPC23', 'SubFPC274', 'SubFPC302', 'SubFPC88', 'SubFPC181', 'SubFPC2', 'SubFPC18', 'SubFPC287', 'SubFPC137', 'SubFPC1', 'SubFPC20', 'SubFPC5', 'SubFPC172', 'SubFPC135', 'SubFPC182', 'SubFPC100', 'SubFPC3', 'SubFPC183', 'SubFPC33']]\n",
    "Ext2  = raw2 [['chemblId', 'pIC50'\n",
    "             ,'SubFPC169', 'SubFPC23', 'SubFPC274', 'SubFPC302', 'SubFPC88', 'SubFPC181', 'SubFPC2', 'SubFPC18', 'SubFPC287', 'SubFPC137', 'SubFPC1', 'SubFPC20', 'SubFPC5', 'SubFPC172', 'SubFPC135', 'SubFPC182', 'SubFPC100', 'SubFPC3', 'SubFPC183', 'SubFPC33']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chemblId</th>\n",
       "      <th>pIC50</th>\n",
       "      <th>SubFPC169</th>\n",
       "      <th>SubFPC23</th>\n",
       "      <th>SubFPC274</th>\n",
       "      <th>SubFPC302</th>\n",
       "      <th>SubFPC88</th>\n",
       "      <th>SubFPC181</th>\n",
       "      <th>SubFPC2</th>\n",
       "      <th>SubFPC18</th>\n",
       "      <th>...</th>\n",
       "      <th>SubFPC1</th>\n",
       "      <th>SubFPC20</th>\n",
       "      <th>SubFPC5</th>\n",
       "      <th>SubFPC172</th>\n",
       "      <th>SubFPC135</th>\n",
       "      <th>SubFPC182</th>\n",
       "      <th>SubFPC100</th>\n",
       "      <th>SubFPC3</th>\n",
       "      <th>SubFPC183</th>\n",
       "      <th>SubFPC33</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEMBL192325</td>\n",
       "      <td>5.30103</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>0.008772</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHEMBL1331671</td>\n",
       "      <td>4.30103</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.653846</td>\n",
       "      <td>0.035088</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        chemblId    pIC50  SubFPC169  SubFPC23  SubFPC274  SubFPC302  \\\n",
       "0   CHEMBL192325  5.30103   0.666667       0.0   0.461538   0.008772   \n",
       "1  CHEMBL1331671  4.30103   0.000000       0.0   0.653846   0.035088   \n",
       "\n",
       "   SubFPC88  SubFPC181  SubFPC2  SubFPC18    ...     SubFPC1  SubFPC20  \\\n",
       "0      0.00        0.0      0.0       0.0    ...         0.0       0.0   \n",
       "1      0.05        0.4      0.0       0.0    ...         0.1       0.0   \n",
       "\n",
       "   SubFPC5  SubFPC172  SubFPC135  SubFPC182  SubFPC100  SubFPC3  SubFPC183  \\\n",
       "0      0.0        0.0        0.2        0.0   0.000000      0.0        0.0   \n",
       "1      0.0        0.0        0.4        0.5   0.055556      0.0        0.0   \n",
       "\n",
       "   SubFPC33  \n",
       "0       0.0  \n",
       "1       0.0  \n",
       "\n",
       "[2 rows x 22 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ext1.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zeromtmu/anaconda2/envs/ER_alpha/lib/python2.7/site-packages/ipykernel_launcher.py:1: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "X_pre = Ext1.ix[:,2:]\n",
    "X_pre = X_pre.as_matrix()\n",
    "X_pre = np.array(X_pre)\n",
    "X_ext = X_pre\n",
    "\n",
    "Y_ext = Ext1[\"pIC50\"].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zeromtmu/anaconda2/envs/ER_alpha/lib/python2.7/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.cross_validation import StratifiedShuffleSplit\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn import cross_validation\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "#Test_score = loaded_model.score(X_ext, Y_ext)\n",
    "AtomPairs2DFingerprintCount.predict(X_ext)\n",
    "AtomPairs2DFingerprinter.predict(X_ext)\n",
    "EStateFingerprinter.predict(X_ext)\n",
    "ExtendedFingerprinter.predict(X_ext)\n",
    "Fingerprinter.predict(X_ext)\n",
    "GraphOnlyFingerprinter.predict(X_ext)\n",
    "KlekotaRothFingerprintCount.predict(X_ext)\n",
    "KlekotaRothFingerprinter.predict(X_ext)\n",
    "MACCSFingerprinter.predict(X_ext)\n",
    "PubchemFingerprinter.predict(X_ext)\n",
    "SubstructureFingerprintCount.predict(X_ext)\n",
    "SubstructureFingerprinter.predict(X_ext)\n",
    "\n",
    "\n",
    "prediction_ext1  = AtomPairs2DFingerprintCount.predict(X_ext)\n",
    "prediction_ext2  = AtomPairs2DFingerprinter.predict(X_ext)\n",
    "prediction_ext3  = EStateFingerprinter.predict(X_ext)\n",
    "prediction_ext4  = ExtendedFingerprinter.predict(X_ext)\n",
    "prediction_ext5  = Fingerprinter.predict(X_ext)\n",
    "prediction_ext6  = GraphOnlyFingerprinter.predict(X_ext)\n",
    "prediction_ext7  = KlekotaRothFingerprintCount.predict(X_ext)\n",
    "prediction_ext8  = KlekotaRothFingerprinter.predict(X_ext)\n",
    "prediction_ext9  = MACCSFingerprinter.predict(X_ext)\n",
    "prediction_ext10 = PubchemFingerprinter.predict(X_ext)\n",
    "prediction_ext11 = SubstructureFingerprintCount.predict(X_ext)\n",
    "prediction_ext12 = SubstructureFingerprinter.predict(X_ext)\n",
    "\n",
    "\n",
    "ext    = pd.DataFrame(Y_ext, columns=['Y_ext'])\n",
    "Pext1  = pd.DataFrame(prediction_ext1 , columns=['pred Model 1 '])\n",
    "Pext2  = pd.DataFrame(prediction_ext2 , columns=['pred Model 2 '])\n",
    "Pext3  = pd.DataFrame(prediction_ext3 , columns=['pred Model 3 '])\n",
    "Pext4  = pd.DataFrame(prediction_ext4 , columns=['pred Model 4 '])\n",
    "Pext5  = pd.DataFrame(prediction_ext5 , columns=['pred Model 5 '])\n",
    "Pext6  = pd.DataFrame(prediction_ext6 , columns=['pred Model 6 '])\n",
    "Pext7  = pd.DataFrame(prediction_ext7 , columns=['pred Model 7 '])\n",
    "Pext8  = pd.DataFrame(prediction_ext8 , columns=['pred Model 8 '])\n",
    "Pext9  = pd.DataFrame(prediction_ext9 , columns=['pred Model 9 '])\n",
    "Pext10 = pd.DataFrame(prediction_ext10, columns=['pred Model 10'])\n",
    "Pext11 = pd.DataFrame(prediction_ext11, columns=['pred Model 11'])\n",
    "Pext12 = pd.DataFrame(prediction_ext12, columns=['pred Model 12'])\n",
    "\n",
    "result = pd.concat([ext,Pext1,Pext2,Pext3,Pext4,Pext5\n",
    "                    ,Pext6,Pext7,Pext8,Pext9,Pext10,Pext11,Pext12],axis=1, \n",
    "                   join='inner').to_csv(\"Result/External1_pred.csv\", \n",
    "                    header= ('Real','AtomPairs2DFingerprintCount'\n",
    "                                 ,'AtomPairs2DFingerprinter'\n",
    "                                 ,'EStateFingerprinter'\n",
    "                                 ,'ExtendedFingerprinter'\n",
    "                                 ,'Fingerprinter'\n",
    "                                 ,'GraphOnlyFingerprinter'\n",
    "                                 ,'KlekotaRothFingerprintCount'\n",
    "                                 ,'KlekotaRothFingerprinter'\n",
    "                                 ,'MACCSFingerprinter'\n",
    "                                 ,'PubchemFingerprinter'\n",
    "                                 ,'SubstructureFingerprintCount'\n",
    "                                 ,'SubstructureFingerprinter')\n",
    "                    , index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Real</th>\n",
       "      <th>AtomPairs2DFingerprintCount</th>\n",
       "      <th>AtomPairs2DFingerprinter</th>\n",
       "      <th>EStateFingerprinter</th>\n",
       "      <th>ExtendedFingerprinter</th>\n",
       "      <th>Fingerprinter</th>\n",
       "      <th>GraphOnlyFingerprinter</th>\n",
       "      <th>KlekotaRothFingerprintCount</th>\n",
       "      <th>KlekotaRothFingerprinter</th>\n",
       "      <th>MACCSFingerprinter</th>\n",
       "      <th>PubchemFingerprinter</th>\n",
       "      <th>SubstructureFingerprintCount</th>\n",
       "      <th>SubstructureFingerprinter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.30103</td>\n",
       "      <td>6.700713</td>\n",
       "      <td>5.152328</td>\n",
       "      <td>5.931076</td>\n",
       "      <td>5.451111</td>\n",
       "      <td>5.393469</td>\n",
       "      <td>5.750132</td>\n",
       "      <td>7.149428</td>\n",
       "      <td>6.097815</td>\n",
       "      <td>5.181037</td>\n",
       "      <td>5.395692</td>\n",
       "      <td>6.222095</td>\n",
       "      <td>5.687484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.30103</td>\n",
       "      <td>6.323529</td>\n",
       "      <td>6.069978</td>\n",
       "      <td>5.403990</td>\n",
       "      <td>4.832072</td>\n",
       "      <td>5.746504</td>\n",
       "      <td>5.384854</td>\n",
       "      <td>5.779245</td>\n",
       "      <td>4.675582</td>\n",
       "      <td>4.962622</td>\n",
       "      <td>5.249253</td>\n",
       "      <td>5.026321</td>\n",
       "      <td>4.848480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Real  AtomPairs2DFingerprintCount  AtomPairs2DFingerprinter  \\\n",
       "0  5.30103                     6.700713                  5.152328   \n",
       "1  4.30103                     6.323529                  6.069978   \n",
       "\n",
       "   EStateFingerprinter  ExtendedFingerprinter  Fingerprinter  \\\n",
       "0             5.931076               5.451111       5.393469   \n",
       "1             5.403990               4.832072       5.746504   \n",
       "\n",
       "   GraphOnlyFingerprinter  KlekotaRothFingerprintCount  \\\n",
       "0                5.750132                     7.149428   \n",
       "1                5.384854                     5.779245   \n",
       "\n",
       "   KlekotaRothFingerprinter  MACCSFingerprinter  PubchemFingerprinter  \\\n",
       "0                  6.097815            5.181037              5.395692   \n",
       "1                  4.675582            4.962622              5.249253   \n",
       "\n",
       "   SubstructureFingerprintCount  SubstructureFingerprinter  \n",
       "0                      6.222095                   5.687484  \n",
       "1                      5.026321                   4.848480  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ext  = pd.read_csv('Result/External1_pred.csv' , header = 0)\n",
    "Ext.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zeromtmu/anaconda2/envs/ER_alpha/lib/python2.7/site-packages/matplotlib/axes/_axes.py:545: 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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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1116a5e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pylab as py\n",
    "\n",
    "# m  = SubstructureFingerprintCount\n",
    "cm = plt.cm.RdBu\n",
    "\n",
    "x_test = np.array(Y_ext)\n",
    "y_test = np.array(prediction_ext11)\n",
    "py.scatter(x_test, y_test, s=50, marker='.', alpha=0.3,\n",
    "        c='r', cmap=cm ,edgecolors='r') \n",
    "\n",
    "# plot real values\n",
    "#py.scatter(x_test, Y_ext, s=50, marker='.', alpha=0.3,\n",
    "        #c='b', cmap=cm ,edgecolors='b') \n",
    "# calc the trendline\n",
    "z = np.polyfit(x_test, Y_ext, 1)\n",
    "p = np.poly1d(z)\n",
    "py.plot(x_test,p(x_test),\"b\", alpha=0.5)\n",
    "\n",
    "plt.legend(loc=2,prop={'size':6})\n",
    "plt.xlabel(\"Experimental $pIC_{50}$ values\", fontsize=10)\n",
    "plt.ylabel(\"Predicted $pIC_{50}$ values\", fontsize=10)\n",
    "\n",
    "plt.gca().set_aspect('equal', adjustable='box')\n",
    "\n",
    "min_axis = np.min(np.concatenate([x_test, y_test], axis=0))\n",
    "max_axis = np.max(np.concatenate([x_test, y_test], axis=0))\n",
    "\n",
    "plt.xlim([(min_axis*0.9),(max_axis*1.05)])\n",
    "plt.ylim([(min_axis*0.9),(max_axis*1.05)])\n",
    "plt.tick_params(axis='both', which='major', labelsize=14)\n",
    "\n",
    "# Save plot to file\n",
    "plt.savefig('Result/Test_gra_ER_alpha.pdf', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3dqseGen+EGWK/lgal2H2O4kj3DypREp5e8ZnlUHcHsHKH2xHL30+UiNUbTgb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\nJQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1129bd450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import matplotlib.cm as cm\n",
    "\n",
    "x = Ext.Real\n",
    "\n",
    "colors = iter(cm.rainbow(np.linspace(0, 1, 12)))\n",
    "\n",
    "ax = Ext.plot(x=\"Real\", y='SubstructureFingerprintCount',kind=\"scatter\",        color=next(colors))\n",
    "Ext.plot     (x=\"Real\", y='AtomPairs2DFingerprintCount', kind=\"scatter\", ax=ax, color=next(colors))\n",
    "Ext.plot     (x=\"Real\", y='AtomPairs2DFingerprinter',    kind=\"scatter\", ax=ax, color=next(colors))\n",
    "Ext.plot     (x=\"Real\", y='EStateFingerprinter',         kind=\"scatter\", ax=ax, color=next(colors))\n",
    "Ext.plot     (x=\"Real\", y='ExtendedFingerprinter',       kind=\"scatter\", ax=ax, color=next(colors))\n",
    "Ext.plot     (x=\"Real\", y='Fingerprinter',               kind=\"scatter\", ax=ax, color=next(colors))\n",
    "Ext.plot     (x=\"Real\", y='GraphOnlyFingerprinter',      kind=\"scatter\", ax=ax, color=next(colors))\n",
    "Ext.plot     (x=\"Real\", y='KlekotaRothFingerprintCount', kind=\"scatter\", ax=ax, color=next(colors))\n",
    "Ext.plot     (x=\"Real\", y='KlekotaRothFingerprinter',    kind=\"scatter\", ax=ax, color=next(colors))\n",
    "Ext.plot     (x=\"Real\", y='MACCSFingerprinter',          kind=\"scatter\", ax=ax, color=next(colors))\n",
    "Ext.plot     (x=\"Real\", y='PubchemFingerprinter',        kind=\"scatter\", ax=ax, color=next(colors))\n",
    "Ext.plot     (x=\"Real\", y='SubstructureFingerprinter',   kind=\"scatter\", ax=ax, color=next(colors))\n",
    "\n",
    "# calc the trendline\n",
    "z = np.polyfit(x, x, 1)\n",
    "p = np.poly1d(z)\n",
    "py.plot(x,p(x),\"b--\", alpha=0.5)\n",
    "\n",
    "plt.legend(loc=2,prop={'size':6})\n",
    "plt.xlabel(\"Experimental $pIC_{50}$ values\", fontsize=10)\n",
    "plt.ylabel(\"Predicted $pIC_{50}$ values\", fontsize=10)\n",
    "\n",
    "plt.gca().set_aspect('equal', adjustable='box')\n",
    "\n",
    "min_axis = np.min(np.concatenate([x_test, y_test], axis=0))\n",
    "max_axis = np.max(np.concatenate([x_test, y_test], axis=0))\n",
    "\n",
    "plt.xlim([(min_axis*0.9),(max_axis*1.05)])\n",
    "plt.ylim([(min_axis*0.9),(max_axis*1.05)])\n",
    "plt.tick_params(axis='both', which='major', labelsize=14)\n",
    "\n",
    "plt.savefig('Result/Test_les_ER_alpha_all.pdf', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test external less than values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zeromtmu/anaconda2/envs/ER_alpha/lib/python2.7/site-packages/ipykernel_launcher.py:1: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "X_pre = Ext2.ix[:,2:]\n",
    "X_pre = X_pre.as_matrix()\n",
    "X_pre = np.array(X_pre)\n",
    "X_ext = X_pre\n",
    "\n",
    "Y_ext = Ext2[\"pIC50\"].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.cross_validation import StratifiedShuffleSplit\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn import cross_validation\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "#Test_score = loaded_model.score(X_ext, Y_ext)\n",
    "AtomPairs2DFingerprintCount.predict(X_ext)\n",
    "AtomPairs2DFingerprinter.predict(X_ext)\n",
    "EStateFingerprinter.predict(X_ext)\n",
    "ExtendedFingerprinter.predict(X_ext)\n",
    "Fingerprinter.predict(X_ext)\n",
    "GraphOnlyFingerprinter.predict(X_ext)\n",
    "KlekotaRothFingerprintCount.predict(X_ext)\n",
    "KlekotaRothFingerprinter.predict(X_ext)\n",
    "MACCSFingerprinter.predict(X_ext)\n",
    "PubchemFingerprinter.predict(X_ext)\n",
    "SubstructureFingerprintCount.predict(X_ext)\n",
    "SubstructureFingerprinter.predict(X_ext)\n",
    "\n",
    "\n",
    "prediction_ext1  = AtomPairs2DFingerprintCount.predict(X_ext)\n",
    "prediction_ext2  = AtomPairs2DFingerprinter.predict(X_ext)\n",
    "prediction_ext3  = EStateFingerprinter.predict(X_ext)\n",
    "prediction_ext4  = ExtendedFingerprinter.predict(X_ext)\n",
    "prediction_ext5  = Fingerprinter.predict(X_ext)\n",
    "prediction_ext6  = GraphOnlyFingerprinter.predict(X_ext)\n",
    "prediction_ext7  = KlekotaRothFingerprintCount.predict(X_ext)\n",
    "prediction_ext8  = KlekotaRothFingerprinter.predict(X_ext)\n",
    "prediction_ext9  = MACCSFingerprinter.predict(X_ext)\n",
    "prediction_ext10 = PubchemFingerprinter.predict(X_ext)\n",
    "prediction_ext11 = SubstructureFingerprintCount.predict(X_ext)\n",
    "prediction_ext12 = SubstructureFingerprinter.predict(X_ext)\n",
    "\n",
    "\n",
    "ext    = pd.DataFrame(Y_ext, columns=['Y_ext'])\n",
    "Pext1  = pd.DataFrame(prediction_ext1 , columns=['pred Model 1 '])\n",
    "Pext2  = pd.DataFrame(prediction_ext2 , columns=['pred Model 2 '])\n",
    "Pext3  = pd.DataFrame(prediction_ext3 , columns=['pred Model 3 '])\n",
    "Pext4  = pd.DataFrame(prediction_ext4 , columns=['pred Model 4 '])\n",
    "Pext5  = pd.DataFrame(prediction_ext5 , columns=['pred Model 5 '])\n",
    "Pext6  = pd.DataFrame(prediction_ext6 , columns=['pred Model 6 '])\n",
    "Pext7  = pd.DataFrame(prediction_ext7 , columns=['pred Model 7 '])\n",
    "Pext8  = pd.DataFrame(prediction_ext8 , columns=['pred Model 8 '])\n",
    "Pext9  = pd.DataFrame(prediction_ext9 , columns=['pred Model 9 '])\n",
    "Pext10 = pd.DataFrame(prediction_ext10, columns=['pred Model 10'])\n",
    "Pext11 = pd.DataFrame(prediction_ext11, columns=['pred Model 11'])\n",
    "Pext12 = pd.DataFrame(prediction_ext12, columns=['pred Model 12'])\n",
    "\n",
    "result = pd.concat([ext,Pext1,Pext2,Pext3,Pext4,Pext5\n",
    "                    ,Pext6,Pext7,Pext8,Pext9,Pext10,Pext11,Pext12],axis=1, \n",
    "                   join='inner').to_csv(\"Result/External2_pred.csv\", \n",
    "                    header= ('Real','AtomPairs2DFingerprintCount'\n",
    "                                 ,'AtomPairs2DFingerprinter'\n",
    "                                 ,'EStateFingerprinter'\n",
    "                                 ,'ExtendedFingerprinter'\n",
    "                                 ,'Fingerprinter'\n",
    "                                 ,'GraphOnlyFingerprinter'\n",
    "                                 ,'KlekotaRothFingerprintCount'\n",
    "                                 ,'KlekotaRothFingerprinter'\n",
    "                                 ,'MACCSFingerprinter'\n",
    "                                 ,'PubchemFingerprinter'\n",
    "                                 ,'SubstructureFingerprintCount'\n",
    "                                 ,'SubstructureFingerprinter')\n",
    "                    , index=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Real</th>\n",
       "      <th>AtomPairs2DFingerprintCount</th>\n",
       "      <th>AtomPairs2DFingerprinter</th>\n",
       "      <th>EStateFingerprinter</th>\n",
       "      <th>ExtendedFingerprinter</th>\n",
       "      <th>Fingerprinter</th>\n",
       "      <th>GraphOnlyFingerprinter</th>\n",
       "      <th>KlekotaRothFingerprintCount</th>\n",
       "      <th>KlekotaRothFingerprinter</th>\n",
       "      <th>MACCSFingerprinter</th>\n",
       "      <th>PubchemFingerprinter</th>\n",
       "      <th>SubstructureFingerprintCount</th>\n",
       "      <th>SubstructureFingerprinter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.80618</td>\n",
       "      <td>5.426787</td>\n",
       "      <td>5.837599</td>\n",
       "      <td>5.098303</td>\n",
       "      <td>4.870237</td>\n",
       "      <td>4.904205</td>\n",
       "      <td>5.748457</td>\n",
       "      <td>6.345521</td>\n",
       "      <td>6.048699</td>\n",
       "      <td>5.642334</td>\n",
       "      <td>4.859867</td>\n",
       "      <td>5.031273</td>\n",
       "      <td>5.694228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.80618</td>\n",
       "      <td>5.972649</td>\n",
       "      <td>5.331801</td>\n",
       "      <td>4.951816</td>\n",
       "      <td>5.040426</td>\n",
       "      <td>5.292382</td>\n",
       "      <td>5.099459</td>\n",
       "      <td>6.279100</td>\n",
       "      <td>5.967516</td>\n",
       "      <td>4.873865</td>\n",
       "      <td>5.033376</td>\n",
       "      <td>5.075039</td>\n",
       "      <td>4.793754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.00000</td>\n",
       "      <td>6.111713</td>\n",
       "      <td>7.579081</td>\n",
       "      <td>6.059669</td>\n",
       "      <td>4.915473</td>\n",
       "      <td>5.435734</td>\n",
       "      <td>5.018502</td>\n",
       "      <td>5.930415</td>\n",
       "      <td>4.785221</td>\n",
       "      <td>5.376392</td>\n",
       "      <td>5.458646</td>\n",
       "      <td>4.940370</td>\n",
       "      <td>5.672814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.00000</td>\n",
       "      <td>6.263317</td>\n",
       "      <td>7.579081</td>\n",
       "      <td>6.059669</td>\n",
       "      <td>4.915473</td>\n",
       "      <td>5.435734</td>\n",
       "      <td>5.018502</td>\n",
       "      <td>6.137459</td>\n",
       "      <td>4.785221</td>\n",
       "      <td>5.376392</td>\n",
       "      <td>5.458646</td>\n",
       "      <td>4.986783</td>\n",
       "      <td>5.672814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.49485</td>\n",
       "      <td>5.845419</td>\n",
       "      <td>5.281777</td>\n",
       "      <td>5.182626</td>\n",
       "      <td>5.203160</td>\n",
       "      <td>5.144779</td>\n",
       "      <td>5.689480</td>\n",
       "      <td>5.861679</td>\n",
       "      <td>5.330744</td>\n",
       "      <td>4.910897</td>\n",
       "      <td>5.096047</td>\n",
       "      <td>6.336229</td>\n",
       "      <td>4.971919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.00000</td>\n",
       "      <td>6.280340</td>\n",
       "      <td>7.579081</td>\n",
       "      <td>6.059669</td>\n",
       "      <td>4.915473</td>\n",
       "      <td>5.435734</td>\n",
       "      <td>5.018502</td>\n",
       "      <td>5.965553</td>\n",
       "      <td>4.785221</td>\n",
       "      <td>5.376392</td>\n",
       "      <td>5.458646</td>\n",
       "      <td>4.939063</td>\n",
       "      <td>5.672814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.00000</td>\n",
       "      <td>5.499081</td>\n",
       "      <td>5.194813</td>\n",
       "      <td>5.494753</td>\n",
       "      <td>4.708510</td>\n",
       "      <td>4.972908</td>\n",
       "      <td>4.789499</td>\n",
       "      <td>5.583900</td>\n",
       "      <td>4.868411</td>\n",
       "      <td>5.191657</td>\n",
       "      <td>5.466651</td>\n",
       "      <td>5.750671</td>\n",
       "      <td>5.304336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.00000</td>\n",
       "      <td>5.622617</td>\n",
       "      <td>5.437802</td>\n",
       "      <td>5.695614</td>\n",
       "      <td>4.745483</td>\n",
       "      <td>5.090434</td>\n",
       "      <td>4.942497</td>\n",
       "      <td>6.078986</td>\n",
       "      <td>4.803409</td>\n",
       "      <td>5.291443</td>\n",
       "      <td>5.347929</td>\n",
       "      <td>5.898515</td>\n",
       "      <td>5.653088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.00000</td>\n",
       "      <td>6.102702</td>\n",
       "      <td>7.579081</td>\n",
       "      <td>6.059669</td>\n",
       "      <td>4.915473</td>\n",
       "      <td>5.435734</td>\n",
       "      <td>5.018502</td>\n",
       "      <td>5.930415</td>\n",
       "      <td>4.785221</td>\n",
       "      <td>5.376392</td>\n",
       "      <td>5.458646</td>\n",
       "      <td>4.893090</td>\n",
       "      <td>5.672814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.00000</td>\n",
       "      <td>5.286751</td>\n",
       "      <td>5.486779</td>\n",
       "      <td>5.306639</td>\n",
       "      <td>4.902943</td>\n",
       "      <td>4.911051</td>\n",
       "      <td>4.972238</td>\n",
       "      <td>5.737661</td>\n",
       "      <td>4.817709</td>\n",
       "      <td>5.601506</td>\n",
       "      <td>5.441802</td>\n",
       "      <td>5.545840</td>\n",
       "      <td>5.315491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4.00000</td>\n",
       "      <td>6.063293</td>\n",
       "      <td>5.540261</td>\n",
       "      <td>5.189541</td>\n",
       "      <td>4.805250</td>\n",
       "      <td>5.804443</td>\n",
       "      <td>5.058115</td>\n",
       "      <td>6.169007</td>\n",
       "      <td>5.416063</td>\n",
       "      <td>4.902603</td>\n",
       "      <td>5.196105</td>\n",
       "      <td>5.024680</td>\n",
       "      <td>5.044253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.00000</td>\n",
       "      <td>5.588428</td>\n",
       "      <td>5.437802</td>\n",
       "      <td>5.695614</td>\n",
       "      <td>4.745483</td>\n",
       "      <td>5.090434</td>\n",
       "      <td>4.942497</td>\n",
       "      <td>6.078986</td>\n",
       "      <td>4.803409</td>\n",
       "      <td>5.291443</td>\n",
       "      <td>5.347929</td>\n",
       "      <td>5.885940</td>\n",
       "      <td>5.653088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.00000</td>\n",
       "      <td>6.385211</td>\n",
       "      <td>6.675351</td>\n",
       "      <td>5.645940</td>\n",
       "      <td>4.830024</td>\n",
       "      <td>5.323866</td>\n",
       "      <td>4.975591</td>\n",
       "      <td>5.224861</td>\n",
       "      <td>4.752166</td>\n",
       "      <td>5.056987</td>\n",
       "      <td>5.477895</td>\n",
       "      <td>4.960880</td>\n",
       "      <td>5.258483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.00000</td>\n",
       "      <td>5.622617</td>\n",
       "      <td>5.437802</td>\n",
       "      <td>5.695614</td>\n",
       "      <td>4.745483</td>\n",
       "      <td>5.090434</td>\n",
       "      <td>4.942497</td>\n",
       "      <td>6.078986</td>\n",
       "      <td>4.803409</td>\n",
       "      <td>5.291443</td>\n",
       "      <td>5.347929</td>\n",
       "      <td>5.917203</td>\n",
       "      <td>5.653088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.00000</td>\n",
       "      <td>5.267442</td>\n",
       "      <td>5.612802</td>\n",
       "      <td>5.432337</td>\n",
       "      <td>4.842255</td>\n",
       "      <td>4.847351</td>\n",
       "      <td>4.923630</td>\n",
       "      <td>5.562015</td>\n",
       "      <td>4.977256</td>\n",
       "      <td>5.394249</td>\n",
       "      <td>5.414097</td>\n",
       "      <td>5.525131</td>\n",
       "      <td>5.397371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4.00000</td>\n",
       "      <td>5.261263</td>\n",
       "      <td>5.612802</td>\n",
       "      <td>5.432337</td>\n",
       "      <td>4.842255</td>\n",
       "      <td>4.847351</td>\n",
       "      <td>4.923630</td>\n",
       "      <td>5.562015</td>\n",
       "      <td>4.977256</td>\n",
       "      <td>5.394249</td>\n",
       "      <td>5.414097</td>\n",
       "      <td>5.531475</td>\n",
       "      <td>5.397371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4.00000</td>\n",
       "      <td>5.499081</td>\n",
       "      <td>5.194813</td>\n",
       "      <td>5.494753</td>\n",
       "      <td>4.708510</td>\n",
       "      <td>4.972908</td>\n",
       "      <td>4.789499</td>\n",
       "      <td>5.583900</td>\n",
       "      <td>4.868411</td>\n",
       "      <td>5.191657</td>\n",
       "      <td>5.466651</td>\n",
       "      <td>5.750671</td>\n",
       "      <td>5.304336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>4.00000</td>\n",
       "      <td>6.189609</td>\n",
       "      <td>5.784457</td>\n",
       "      <td>5.335586</td>\n",
       "      <td>4.701461</td>\n",
       "      <td>5.742343</td>\n",
       "      <td>5.091935</td>\n",
       "      <td>5.949057</td>\n",
       "      <td>5.458321</td>\n",
       "      <td>4.830885</td>\n",
       "      <td>5.096914</td>\n",
       "      <td>5.041381</td>\n",
       "      <td>5.015855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>4.00000</td>\n",
       "      <td>6.135686</td>\n",
       "      <td>7.579081</td>\n",
       "      <td>6.059669</td>\n",
       "      <td>4.915473</td>\n",
       "      <td>5.435734</td>\n",
       "      <td>5.018502</td>\n",
       "      <td>5.965553</td>\n",
       "      <td>4.785221</td>\n",
       "      <td>5.376392</td>\n",
       "      <td>5.458646</td>\n",
       "      <td>4.919895</td>\n",
       "      <td>5.672814</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Real  AtomPairs2DFingerprintCount  AtomPairs2DFingerprinter  \\\n",
       "0   5.80618                     5.426787                  5.837599   \n",
       "1   5.80618                     5.972649                  5.331801   \n",
       "2   4.00000                     6.111713                  7.579081   \n",
       "3   4.00000                     6.263317                  7.579081   \n",
       "4   8.49485                     5.845419                  5.281777   \n",
       "5   4.00000                     6.280340                  7.579081   \n",
       "6   4.00000                     5.499081                  5.194813   \n",
       "7   4.00000                     5.622617                  5.437802   \n",
       "8   4.00000                     6.102702                  7.579081   \n",
       "9   4.00000                     5.286751                  5.486779   \n",
       "10  4.00000                     6.063293                  5.540261   \n",
       "11  4.00000                     5.588428                  5.437802   \n",
       "12  4.00000                     6.385211                  6.675351   \n",
       "13  4.00000                     5.622617                  5.437802   \n",
       "14  4.00000                     5.267442                  5.612802   \n",
       "15  4.00000                     5.261263                  5.612802   \n",
       "16  4.00000                     5.499081                  5.194813   \n",
       "17  4.00000                     6.189609                  5.784457   \n",
       "18  4.00000                     6.135686                  7.579081   \n",
       "\n",
       "    EStateFingerprinter  ExtendedFingerprinter  Fingerprinter  \\\n",
       "0              5.098303               4.870237       4.904205   \n",
       "1              4.951816               5.040426       5.292382   \n",
       "2              6.059669               4.915473       5.435734   \n",
       "3              6.059669               4.915473       5.435734   \n",
       "4              5.182626               5.203160       5.144779   \n",
       "5              6.059669               4.915473       5.435734   \n",
       "6              5.494753               4.708510       4.972908   \n",
       "7              5.695614               4.745483       5.090434   \n",
       "8              6.059669               4.915473       5.435734   \n",
       "9              5.306639               4.902943       4.911051   \n",
       "10             5.189541               4.805250       5.804443   \n",
       "11             5.695614               4.745483       5.090434   \n",
       "12             5.645940               4.830024       5.323866   \n",
       "13             5.695614               4.745483       5.090434   \n",
       "14             5.432337               4.842255       4.847351   \n",
       "15             5.432337               4.842255       4.847351   \n",
       "16             5.494753               4.708510       4.972908   \n",
       "17             5.335586               4.701461       5.742343   \n",
       "18             6.059669               4.915473       5.435734   \n",
       "\n",
       "    GraphOnlyFingerprinter  KlekotaRothFingerprintCount  \\\n",
       "0                 5.748457                     6.345521   \n",
       "1                 5.099459                     6.279100   \n",
       "2                 5.018502                     5.930415   \n",
       "3                 5.018502                     6.137459   \n",
       "4                 5.689480                     5.861679   \n",
       "5                 5.018502                     5.965553   \n",
       "6                 4.789499                     5.583900   \n",
       "7                 4.942497                     6.078986   \n",
       "8                 5.018502                     5.930415   \n",
       "9                 4.972238                     5.737661   \n",
       "10                5.058115                     6.169007   \n",
       "11                4.942497                     6.078986   \n",
       "12                4.975591                     5.224861   \n",
       "13                4.942497                     6.078986   \n",
       "14                4.923630                     5.562015   \n",
       "15                4.923630                     5.562015   \n",
       "16                4.789499                     5.583900   \n",
       "17                5.091935                     5.949057   \n",
       "18                5.018502                     5.965553   \n",
       "\n",
       "    KlekotaRothFingerprinter  MACCSFingerprinter  PubchemFingerprinter  \\\n",
       "0                   6.048699            5.642334              4.859867   \n",
       "1                   5.967516            4.873865              5.033376   \n",
       "2                   4.785221            5.376392              5.458646   \n",
       "3                   4.785221            5.376392              5.458646   \n",
       "4                   5.330744            4.910897              5.096047   \n",
       "5                   4.785221            5.376392              5.458646   \n",
       "6                   4.868411            5.191657              5.466651   \n",
       "7                   4.803409            5.291443              5.347929   \n",
       "8                   4.785221            5.376392              5.458646   \n",
       "9                   4.817709            5.601506              5.441802   \n",
       "10                  5.416063            4.902603              5.196105   \n",
       "11                  4.803409            5.291443              5.347929   \n",
       "12                  4.752166            5.056987              5.477895   \n",
       "13                  4.803409            5.291443              5.347929   \n",
       "14                  4.977256            5.394249              5.414097   \n",
       "15                  4.977256            5.394249              5.414097   \n",
       "16                  4.868411            5.191657              5.466651   \n",
       "17                  5.458321            4.830885              5.096914   \n",
       "18                  4.785221            5.376392              5.458646   \n",
       "\n",
       "    SubstructureFingerprintCount  SubstructureFingerprinter  \n",
       "0                       5.031273                   5.694228  \n",
       "1                       5.075039                   4.793754  \n",
       "2                       4.940370                   5.672814  \n",
       "3                       4.986783                   5.672814  \n",
       "4                       6.336229                   4.971919  \n",
       "5                       4.939063                   5.672814  \n",
       "6                       5.750671                   5.304336  \n",
       "7                       5.898515                   5.653088  \n",
       "8                       4.893090                   5.672814  \n",
       "9                       5.545840                   5.315491  \n",
       "10                      5.024680                   5.044253  \n",
       "11                      5.885940                   5.653088  \n",
       "12                      4.960880                   5.258483  \n",
       "13                      5.917203                   5.653088  \n",
       "14                      5.525131                   5.397371  \n",
       "15                      5.531475                   5.397371  \n",
       "16                      5.750671                   5.304336  \n",
       "17                      5.041381                   5.015855  \n",
       "18                      4.919895                   5.672814  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ext2  = pd.read_csv('Result/External2_pred.csv' , header = 0)\n",
    "Ext2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19, 13)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ext2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQ0AAAESCAYAAADnkoBGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111cb0ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pylab as py\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pylab as py\n",
    "\n",
    "cm = plt.cm.RdBu\n",
    "\n",
    "x_test = np.array(Y_ext)\n",
    "y_test = np.array(prediction_ext11)\n",
    "py.scatter(x_test, y_test, s=50, marker='.', alpha=0.3,\n",
    "        c='r', cmap=cm ,edgecolors='r') \n",
    "\n",
    "# plot real values\n",
    "#py.scatter(x_test, Y_ext, s=50, marker='.', alpha=0.3,\n",
    "        #c='b', cmap=cm ,edgecolors='b') \n",
    "# calc the trendline\n",
    "z = np.polyfit(x_test, Y_ext, 1)\n",
    "p = np.poly1d(z)\n",
    "py.plot(x_test,p(x_test),\"b\", alpha=0.5)\n",
    "\n",
    "plt.legend(loc=2,prop={'size':6})\n",
    "plt.xlabel(\"Experimental $pIC_{50}$ values\", fontsize=10)\n",
    "plt.ylabel(\"Predicted $pIC_{50}$ values\", fontsize=10)\n",
    "\n",
    "plt.gca().set_aspect('equal', adjustable='box')\n",
    "\n",
    "min_axis = np.min(np.concatenate([x_test, y_test], axis=0))\n",
    "max_axis = np.max(np.concatenate([x_test, y_test], axis=0))\n",
    "\n",
    "plt.xlim([(min_axis*0.9),(max_axis*1.05)])\n",
    "plt.ylim([(min_axis*0.9),(max_axis*1.05)])\n",
    "plt.tick_params(axis='both', which='major', labelsize=14)\n",
    "\n",
    "# Save plot to file\n",
    "plt.savefig('Result/Test_les_ER_alpha.pdf', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bOCL7C8NaGhUi9LbqpEkH90YpQTAKukg6OuDd7z7Up2FWRtNSbdEYUdXXq3Eh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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112db4c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import matplotlib.cm as cm\n",
    "\n",
    "x = Ext.Real\n",
    "x2 = Ext2.Real\n",
    "\n",
    "colors = iter(cm.rainbow(np.linspace(0, 1, 12)))\n",
    "\n",
    "ax = Ext.plot (x=\"Real\", y='SubstructureFingerprintCount',kind=\"scatter\",  c='r', alpha=0.3, edgecolors='r')\n",
    "Ext2.plot     (x=\"Real\", y='SubstructureFingerprintCount', kind=\"scatter\", ax=ax, c='b', alpha=0.3, edgecolors='b')\n",
    "\n",
    "# calc the trendline\n",
    "z = np.polyfit(x, x, 1)\n",
    "p = np.poly1d(z)\n",
    "py.plot(x,p(x),\"--\", alpha=0.5)\n",
    "\n",
    "plt.legend(loc=2,prop={'size':6})\n",
    "plt.xlabel(\"Experimental $pIC_{50}$\", fontsize=10)\n",
    "plt.ylabel(\"Predicted $pIC_{50}$\", fontsize=10)\n",
    "\n",
    "plt.gca().set_aspect('equal', adjustable='box')\n",
    "\n",
    "min_axis = np.min(np.concatenate([x_test, y_test], axis=0))\n",
    "max_axis = np.max(np.concatenate([x_test, y_test], axis=0))\n",
    "\n",
    "plt.xlim([(min_axis*0.9),(max_axis*1.05)])\n",
    "plt.ylim([(min_axis*0.9),(max_axis*1.05)])\n",
    "plt.tick_params(axis='both', which='major', labelsize=14)\n",
    "\n",
    "plt.savefig('Result/Test_ER_alpha_all.pdf', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>acdAcidicPka</th>\n",
       "      <th>acdBasicPka</th>\n",
       "      <th>acdLogd</th>\n",
       "      <th>acdLogp</th>\n",
       "      <th>alogp</th>\n",
       "      <th>chemblId</th>\n",
       "      <th>knownDrug</th>\n",
       "      <th>molecularFormula</th>\n",
       "      <th>molecularWeight</th>\n",
       "      <th>numRo5Violations</th>\n",
       "      <th>...</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>2.57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.24</td>\n",
       "      <td>1.25</td>\n",
       "      <td>2.33</td>\n",
       "      <td>CHEMBL192325</td>\n",
       "      <td>No</td>\n",
       "      <td>C15H10O4</td>\n",
       "      <td>254.24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.021739</td>\n",
       "      <td>0.021739</td>\n",
       "      <td>0.008772</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.129870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12.76</td>\n",
       "      <td>4.54</td>\n",
       "      <td>3.13</td>\n",
       "      <td>3.13</td>\n",
       "      <td>2.68</td>\n",
       "      <td>CHEMBL1331671</td>\n",
       "      <td>No</td>\n",
       "      <td>C17H14ClN3O2</td>\n",
       "      <td>327.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.028986</td>\n",
       "      <td>0.028986</td>\n",
       "      <td>0.035088</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.142857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 341 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   acdAcidicPka  acdBasicPka  acdLogd  acdLogp  alogp       chemblId  \\\n",
       "0          2.57          NaN    -2.24     1.25   2.33   CHEMBL192325   \n",
       "1         12.76         4.54     3.13     3.13   2.68  CHEMBL1331671   \n",
       "\n",
       "  knownDrug molecularFormula  molecularWeight  numRo5Violations    ...     \\\n",
       "0        No         C15H10O4           254.24               0.0    ...      \n",
       "1        No     C17H14ClN3O2           327.76               0.0    ...      \n",
       "\n",
       "  SubFPC298 SubFPC299  SubFPC300 SubFPC301 SubFPC302 SubFPC303 SubFPC304  \\\n",
       "0       0.0       0.0   0.021739  0.021739  0.008772  0.333333       0.0   \n",
       "1       0.0       0.0   0.028986  0.028986  0.035088  0.000000       0.0   \n",
       "\n",
       "  SubFPC305 SubFPC306 SubFPC307  \n",
       "0       0.0       0.0  0.129870  \n",
       "1       0.0       0.0  0.142857  \n",
       "\n",
       "[2 rows x 341 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw1.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>acdAcidicPka</th>\n",
       "      <th>acdBasicPka</th>\n",
       "      <th>acdLogd</th>\n",
       "      <th>acdLogp</th>\n",
       "      <th>alogp</th>\n",
       "      <th>chemblId</th>\n",
       "      <th>knownDrug</th>\n",
       "      <th>molecularFormula</th>\n",
       "      <th>molecularWeight</th>\n",
       "      <th>numRo5Violations</th>\n",
       "      <th>...</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>8.19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.06</td>\n",
       "      <td>2.15</td>\n",
       "      <td>1.24</td>\n",
       "      <td>CHEMBL1532515</td>\n",
       "      <td>No</td>\n",
       "      <td>C17H22N4O4S</td>\n",
       "      <td>378.45</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.214286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.70</td>\n",
       "      <td>1.94</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1.93</td>\n",
       "      <td>CHEMBL1566820</td>\n",
       "      <td>No</td>\n",
       "      <td>C18H19N5O3S</td>\n",
       "      <td>385.44</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.571429</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 339 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   acdAcidicPka  acdBasicPka  acdLogd  acdLogp  alogp       chemblId  \\\n",
       "0          8.19          NaN     2.06     2.15   1.24  CHEMBL1532515   \n",
       "1         10.70         1.94     0.63     0.63   1.93  CHEMBL1566820   \n",
       "\n",
       "  knownDrug molecularFormula  molecularWeight  numRo5Violations    ...     \\\n",
       "0        No      C17H22N4O4S           378.45                 0    ...      \n",
       "1        No      C18H19N5O3S           385.44                 0    ...      \n",
       "\n",
       "  SubFPC298  SubFPC299 SubFPC300 SubFPC301 SubFPC302 SubFPC303 SubFPC304  \\\n",
       "0       0.0        0.0  0.578947  0.578947  0.666667       0.0       0.0   \n",
       "1       0.0        0.0  0.315789  0.315789  1.000000       0.0       0.0   \n",
       "\n",
       "  SubFPC305 SubFPC306 SubFPC307  \n",
       "0       0.0       0.0  0.214286  \n",
       "1       0.0       0.0  0.571429  \n",
       "\n",
       "[2 rows x 339 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw2.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from pandas.tools.plotting import scatter_matrix\n",
    "# Plot the Figures Inline\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "def PCA_plot(data):\n",
    "    # PCA's components graphed in 2D and 3D\n",
    "    # Apply Scaling \n",
    "    from sklearn.decomposition import PCA\n",
    "    from mpl_toolkits.mplot3d import Axes3D\n",
    "    from sklearn.preprocessing import StandardScaler\n",
    "    from matplotlib import pyplot as plt\n",
    "    data_pca = data\n",
    "    \n",
    "    # Apply Scaling \n",
    "    X = data_pca.drop('values', axis=1).as_matrix().astype(np.float)\n",
    "    y = data_pca['values'].values\n",
    "    \n",
    "    # Formatting\n",
    "    target_names = [\">\",\"<\"]\n",
    "    colors = ['red','blue']\n",
    "    lw = 2\n",
    "    alpha = 0.3\n",
    "\n",
    "    # 2 Components PCA\n",
    "    plt.style.use('seaborn-whitegrid')\n",
    "    plt.figure(2, figsize=(20, 8))\n",
    "\n",
    "    plt.subplot(1, 2, 1)\n",
    "    pca = PCA(n_components=2)\n",
    "    X_std = StandardScaler().fit_transform(X)\n",
    "    X_r = pca.fit_transform(X_std)\n",
    "    #X_r = pca.fit(X).transform(X)\n",
    "        \n",
    "    for color, i, target_name in zip(colors, ['>','<'], target_names):\n",
    "        plt.scatter(X_r[y == i, 0], X_r[y == i, 1], \n",
    "                    color=color, \n",
    "                    alpha=alpha, \n",
    "                    lw=lw,\n",
    "                    label=target_name)\n",
    "    plt.legend(loc='best', shadow=False, scatterpoints=1)\n",
    "    plt.title('First two PCA directions');\n",
    "\n",
    "    # 3 Components PCA\n",
    "    ax = plt.subplot(1, 2, 2, projection='3d')\n",
    "\n",
    "    pca = PCA(n_components=3)\n",
    "    X_reduced = pca.fit_transform(X_std)\n",
    "    for color, i, target_name in zip(colors, ['>','<'], target_names):\n",
    "        ax.scatter(X_reduced[y == i, 0], X_reduced[y == i, 1], X_reduced[y == i, 2], \n",
    "                   color=color,\n",
    "                   alpha=alpha,\n",
    "                   lw=lw, \n",
    "                   label=target_name)\n",
    "    plt.legend(loc='best', shadow=False, scatterpoints=1)\n",
    "    ax.set_title(\"First three PCA directions\")\n",
    "    ax.set_xlabel(\"1st eigenvector\")\n",
    "    ax.set_ylabel(\"2nd eigenvector\")\n",
    "    ax.set_zlabel(\"3rd eigenvector\")\n",
    "\n",
    "    # rotate the axes\n",
    "    ax.view_init(30, 10)\n",
    "    plt.savefig('Result/Testing PCA.pdf', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['acdAcidicPka',\n",
       " 'acdBasicPka',\n",
       " 'acdLogd',\n",
       " 'acdLogp',\n",
       " 'alogp',\n",
       " 'chemblId',\n",
       " 'knownDrug',\n",
       " 'molecularFormula',\n",
       " 'molecularWeight',\n",
       " 'numRo5Violations',\n",
       " 'passesRuleOfThree',\n",
       " 'preferredCompoundName',\n",
       " 'rotatableBonds',\n",
       " 'smiles',\n",
       " 'species',\n",
       " 'stdInChiKey',\n",
       " 'synonyms',\n",
       " 'activity_comment',\n",
       " 'assay_chemblid',\n",
       " 'assay_description',\n",
       " 'assay_type',\n",
       " 'bioactivity_type',\n",
       " 'name_in_reference',\n",
       " 'operator',\n",
       " 'organism',\n",
       " 'parent_cmpd_chemblid',\n",
       " 'reference',\n",
       " 'target_chemblid',\n",
       " 'target_confidence',\n",
       " 'target_name',\n",
       " 'units',\n",
       " 'STATUS',\n",
       " 'SMILES_desalt',\n",
       " 'pIC50',\n",
       " 'SubFPC1',\n",
       " 'SubFPC2',\n",
       " 'SubFPC3',\n",
       " 'SubFPC4',\n",
       " 'SubFPC5',\n",
       " 'SubFPC6',\n",
       " 'SubFPC7',\n",
       " 'SubFPC8',\n",
       " 'SubFPC9',\n",
       " 'SubFPC10',\n",
       " 'SubFPC11',\n",
       " 'SubFPC12',\n",
       " 'SubFPC13',\n",
       " 'SubFPC14',\n",
       " 'SubFPC15',\n",
       " 'SubFPC16',\n",
       " 'SubFPC17',\n",
       " 'SubFPC18',\n",
       " 'SubFPC19',\n",
       " 'SubFPC20',\n",
       " 'SubFPC21',\n",
       " 'SubFPC22',\n",
       " 'SubFPC23',\n",
       " 'SubFPC24',\n",
       " 'SubFPC25',\n",
       " 'SubFPC26',\n",
       " 'SubFPC27',\n",
       " 'SubFPC28',\n",
       " 'SubFPC29',\n",
       " 'SubFPC30',\n",
       " 'SubFPC31',\n",
       " 'SubFPC32',\n",
       " 'SubFPC33',\n",
       " 'SubFPC34',\n",
       " 'SubFPC35',\n",
       " 'SubFPC36',\n",
       " 'SubFPC37',\n",
       " 'SubFPC38',\n",
       " 'SubFPC39',\n",
       " 'SubFPC40',\n",
       " 'SubFPC41',\n",
       " 'SubFPC42',\n",
       " 'SubFPC43',\n",
       " 'SubFPC44',\n",
       " 'SubFPC45',\n",
       " 'SubFPC46',\n",
       " 'SubFPC47',\n",
       " 'SubFPC48',\n",
       " 'SubFPC49',\n",
       " 'SubFPC50',\n",
       " 'SubFPC51',\n",
       " 'SubFPC52',\n",
       " 'SubFPC53',\n",
       " 'SubFPC54',\n",
       " 'SubFPC55',\n",
       " 'SubFPC56',\n",
       " 'SubFPC57',\n",
       " 'SubFPC58',\n",
       " 'SubFPC59',\n",
       " 'SubFPC60',\n",
       " 'SubFPC61',\n",
       " 'SubFPC62',\n",
       " 'SubFPC63',\n",
       " 'SubFPC64',\n",
       " 'SubFPC65',\n",
       " 'SubFPC66',\n",
       " 'SubFPC67',\n",
       " 'SubFPC68',\n",
       " 'SubFPC69',\n",
       " 'SubFPC70',\n",
       " 'SubFPC71',\n",
       " 'SubFPC72',\n",
       " 'SubFPC73',\n",
       " 'SubFPC74',\n",
       " 'SubFPC75',\n",
       " 'SubFPC76',\n",
       " 'SubFPC77',\n",
       " 'SubFPC78',\n",
       " 'SubFPC79',\n",
       " 'SubFPC80',\n",
       " 'SubFPC81',\n",
       " 'SubFPC82',\n",
       " 'SubFPC83',\n",
       " 'SubFPC84',\n",
       " 'SubFPC85',\n",
       " 'SubFPC86',\n",
       " 'SubFPC87',\n",
       " 'SubFPC88',\n",
       " 'SubFPC89',\n",
       " 'SubFPC90',\n",
       " 'SubFPC91',\n",
       " 'SubFPC92',\n",
       " 'SubFPC93',\n",
       " 'SubFPC94',\n",
       " 'SubFPC95',\n",
       " 'SubFPC96',\n",
       " 'SubFPC97',\n",
       " 'SubFPC98',\n",
       " 'SubFPC99',\n",
       " 'SubFPC100',\n",
       " 'SubFPC101',\n",
       " 'SubFPC102',\n",
       " 'SubFPC103',\n",
       " 'SubFPC104',\n",
       " 'SubFPC105',\n",
       " 'SubFPC106',\n",
       " 'SubFPC107',\n",
       " 'SubFPC108',\n",
       " 'SubFPC109',\n",
       " 'SubFPC110',\n",
       " 'SubFPC111',\n",
       " 'SubFPC112',\n",
       " 'SubFPC113',\n",
       " 'SubFPC114',\n",
       " 'SubFPC115',\n",
       " 'SubFPC116',\n",
       " 'SubFPC117',\n",
       " 'SubFPC118',\n",
       " 'SubFPC119',\n",
       " 'SubFPC120',\n",
       " 'SubFPC121',\n",
       " 'SubFPC122',\n",
       " 'SubFPC123',\n",
       " 'SubFPC124',\n",
       " 'SubFPC125',\n",
       " 'SubFPC126',\n",
       " 'SubFPC127',\n",
       " 'SubFPC128',\n",
       " 'SubFPC129',\n",
       " 'SubFPC130',\n",
       " 'SubFPC131',\n",
       " 'SubFPC132',\n",
       " 'SubFPC133',\n",
       " 'SubFPC134',\n",
       " 'SubFPC135',\n",
       " 'SubFPC136',\n",
       " 'SubFPC137',\n",
       " 'SubFPC138',\n",
       " 'SubFPC139',\n",
       " 'SubFPC140',\n",
       " 'SubFPC141',\n",
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       " 'SubFPC149',\n",
       " 'SubFPC150',\n",
       " 'SubFPC151',\n",
       " 'SubFPC152',\n",
       " 'SubFPC153',\n",
       " 'SubFPC154',\n",
       " 'SubFPC155',\n",
       " 'SubFPC156',\n",
       " 'SubFPC157',\n",
       " 'SubFPC158',\n",
       " 'SubFPC159',\n",
       " 'SubFPC160',\n",
       " 'SubFPC161',\n",
       " 'SubFPC162',\n",
       " 'SubFPC163',\n",
       " 'SubFPC164',\n",
       " 'SubFPC165',\n",
       " 'SubFPC166',\n",
       " 'SubFPC167',\n",
       " 'SubFPC168',\n",
       " 'SubFPC169',\n",
       " 'SubFPC170',\n",
       " 'SubFPC171',\n",
       " 'SubFPC172',\n",
       " 'SubFPC173',\n",
       " 'SubFPC174',\n",
       " 'SubFPC175',\n",
       " 'SubFPC176',\n",
       " 'SubFPC177',\n",
       " 'SubFPC178',\n",
       " 'SubFPC179',\n",
       " 'SubFPC180',\n",
       " 'SubFPC181',\n",
       " 'SubFPC182',\n",
       " 'SubFPC183',\n",
       " 'SubFPC184',\n",
       " 'SubFPC185',\n",
       " 'SubFPC186',\n",
       " 'SubFPC187',\n",
       " 'SubFPC188',\n",
       " 'SubFPC189',\n",
       " 'SubFPC190',\n",
       " 'SubFPC191',\n",
       " 'SubFPC192',\n",
       " 'SubFPC193',\n",
       " 'SubFPC194',\n",
       " 'SubFPC195',\n",
       " 'SubFPC196',\n",
       " 'SubFPC197',\n",
       " 'SubFPC198',\n",
       " 'SubFPC199',\n",
       " 'SubFPC200',\n",
       " 'SubFPC201',\n",
       " 'SubFPC202',\n",
       " 'SubFPC203',\n",
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       " 'SubFPC206',\n",
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       " 'SubFPC209',\n",
       " 'SubFPC210',\n",
       " 'SubFPC211',\n",
       " 'SubFPC212',\n",
       " 'SubFPC213',\n",
       " 'SubFPC214',\n",
       " 'SubFPC215',\n",
       " 'SubFPC216',\n",
       " 'SubFPC217',\n",
       " 'SubFPC218',\n",
       " 'SubFPC219',\n",
       " 'SubFPC220',\n",
       " 'SubFPC221',\n",
       " 'SubFPC222',\n",
       " 'SubFPC223',\n",
       " 'SubFPC224',\n",
       " 'SubFPC225',\n",
       " 'SubFPC226',\n",
       " 'SubFPC227',\n",
       " 'SubFPC228',\n",
       " 'SubFPC229',\n",
       " 'SubFPC230',\n",
       " 'SubFPC231',\n",
       " 'SubFPC232',\n",
       " 'SubFPC233',\n",
       " 'SubFPC234',\n",
       " 'SubFPC235',\n",
       " 'SubFPC236',\n",
       " 'SubFPC237',\n",
       " 'SubFPC238',\n",
       " 'SubFPC239',\n",
       " 'SubFPC240',\n",
       " 'SubFPC241',\n",
       " 'SubFPC242',\n",
       " 'SubFPC243',\n",
       " 'SubFPC244',\n",
       " 'SubFPC245',\n",
       " 'SubFPC246',\n",
       " 'SubFPC247',\n",
       " 'SubFPC248',\n",
       " 'SubFPC249',\n",
       " 'SubFPC250',\n",
       " 'SubFPC251',\n",
       " 'SubFPC252',\n",
       " 'SubFPC253',\n",
       " 'SubFPC254',\n",
       " 'SubFPC255',\n",
       " 'SubFPC256',\n",
       " 'SubFPC257',\n",
       " 'SubFPC258',\n",
       " 'SubFPC259',\n",
       " 'SubFPC260',\n",
       " 'SubFPC261',\n",
       " 'SubFPC262',\n",
       " 'SubFPC263',\n",
       " 'SubFPC264',\n",
       " 'SubFPC265',\n",
       " 'SubFPC266',\n",
       " 'SubFPC267',\n",
       " 'SubFPC268',\n",
       " 'SubFPC269',\n",
       " 'SubFPC270',\n",
       " 'SubFPC271',\n",
       " 'SubFPC272',\n",
       " 'SubFPC273',\n",
       " 'SubFPC274',\n",
       " 'SubFPC275',\n",
       " 'SubFPC276',\n",
       " 'SubFPC277',\n",
       " 'SubFPC278',\n",
       " 'SubFPC279',\n",
       " 'SubFPC280',\n",
       " 'SubFPC281',\n",
       " 'SubFPC282',\n",
       " 'SubFPC283',\n",
       " 'SubFPC284',\n",
       " 'SubFPC285',\n",
       " 'SubFPC286',\n",
       " 'SubFPC287',\n",
       " 'SubFPC288',\n",
       " 'SubFPC289',\n",
       " 'SubFPC290',\n",
       " 'SubFPC291',\n",
       " 'SubFPC292',\n",
       " 'SubFPC293',\n",
       " 'SubFPC294',\n",
       " 'SubFPC295',\n",
       " 'SubFPC296',\n",
       " 'SubFPC297',\n",
       " 'SubFPC298',\n",
       " 'SubFPC299',\n",
       " 'SubFPC300',\n",
       " 'SubFPC301',\n",
       " 'SubFPC302',\n",
       " 'SubFPC303',\n",
       " 'SubFPC304',\n",
       " 'SubFPC305',\n",
       " 'SubFPC306',\n",
       " 'SubFPC307']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(raw1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
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       "   SubFPC1  SubFPC2  SubFPC3  SubFPC4  SubFPC5  SubFPC6  SubFPC7  SubFPC8  \\\n",
       "0      0.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "1      0.1      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "\n",
       "   SubFPC9  SubFPC10    ...      SubFPC298  SubFPC299  SubFPC300  SubFPC301  \\\n",
       "0      0.0       0.0    ...            0.0        0.0   0.021739   0.021739   \n",
       "1      0.0       0.0    ...            0.0        0.0   0.028986   0.028986   \n",
       "\n",
       "   SubFPC302  SubFPC303  SubFPC304  SubFPC305  SubFPC306  SubFPC307  \n",
       "0   0.008772   0.333333        0.0        0.0        0.0   0.129870  \n",
       "1   0.035088   0.000000        0.0        0.0        0.0   0.142857  \n",
       "\n",
       "[2 rows x 307 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca1 = raw1[raw1.columns[34:]]\n",
    "pca1.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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       "   SubFPC1  SubFPC2  SubFPC3  SubFPC4  SubFPC5  SubFPC6  SubFPC7  SubFPC8  \\\n",
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       "1      0.0    0.000      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "\n",
       "   SubFPC9  SubFPC10    ...      SubFPC298  SubFPC299  SubFPC300  SubFPC301  \\\n",
       "0      0.0       0.0    ...            0.0        0.0   0.578947   0.578947   \n",
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       "\n",
       "   SubFPC302  SubFPC303  SubFPC304  SubFPC305  SubFPC306  SubFPC307  \n",
       "0   0.666667        0.0        0.0        0.0        0.0   0.214286  \n",
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       "\n",
       "[2 rows x 307 columns]"
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     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca2 = raw2[raw2.columns[32:]]\n",
    "pca2.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zeromtmu/anaconda2/envs/ER_alpha/lib/python2.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/Users/zeromtmu/anaconda2/envs/ER_alpha/lib/python2.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    },
    {
     "data": {
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       "   SubFPC1  SubFPC2  SubFPC3  SubFPC4  SubFPC5  SubFPC6  SubFPC7  SubFPC8  \\\n",
       "0      0.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "1      0.1      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "\n",
       "   SubFPC9  SubFPC10   ...    SubFPC299  SubFPC300  SubFPC301  SubFPC302  \\\n",
       "0      0.0       0.0   ...          0.0   0.021739   0.021739   0.008772   \n",
       "1      0.0       0.0   ...          0.0   0.028986   0.028986   0.035088   \n",
       "\n",
       "   SubFPC303  SubFPC304  SubFPC305  SubFPC306  SubFPC307  values  \n",
       "0   0.333333        0.0        0.0        0.0   0.129870       >  \n",
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       "[2 rows x 308 columns]"
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     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca1['values'] = \">\"\n",
    "pca2['values'] = \"<\"\n",
    "\n",
    "frames = [pca1,pca2]\n",
    "pca = pd.concat(frames)\n",
    "pca.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(302, 308)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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wDocj7RBFtOBFpLEQxUonQJJlGQ6HIxLsDFcFUuxxqcZOYmMIlCZNA9ragEAA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\nEREREWkF/h9eySW8kLb3jQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112cf4310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PCA_plot(pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chemblId</th>\n",
       "      <th>pIC50</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>...</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>CHEMBL370037</td>\n",
       "      <td>8.221849</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.085714</td>\n",
       "      <td>0.085714</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.354839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHEMBL189073</td>\n",
       "      <td>5.762708</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.057143</td>\n",
       "      <td>0.057143</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.419355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHEMBL365290</td>\n",
       "      <td>6.943095</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.085714</td>\n",
       "      <td>0.085714</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.322581</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 309 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       chemblId     pIC50   SubFPC1  SubFPC2  SubFPC3  SubFPC4   SubFPC5  \\\n",
       "0  CHEMBL370037  8.221849  0.166667      0.0      0.0      0.0  0.333333   \n",
       "1  CHEMBL189073  5.762708  0.000000      0.0      0.0      0.0  0.000000   \n",
       "2  CHEMBL365290  6.943095  0.166667      0.0      0.0      0.0  0.000000   \n",
       "\n",
       "   SubFPC6  SubFPC7  SubFPC8    ...      SubFPC298  SubFPC299  SubFPC300  \\\n",
       "0      0.0      0.0      0.0    ...            0.0        0.0   0.085714   \n",
       "1      0.0      0.0      0.0    ...            0.0        0.0   0.057143   \n",
       "2      0.0      0.0      0.0    ...            0.0        0.0   0.085714   \n",
       "\n",
       "   SubFPC301  SubFPC302  SubFPC303  SubFPC304  SubFPC305  SubFPC306  SubFPC307  \n",
       "0   0.085714       0.04       0.25        0.0        0.0        0.0   0.354839  \n",
       "1   0.057143       0.00       0.00        0.0        0.0        0.0   0.419355  \n",
       "2   0.085714       0.00       0.00        0.0        0.0        0.0   0.322581  \n",
       "\n",
       "[3 rows x 309 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train  = pd.read_csv('QSAR/ER_alpha_SubstructureFingerprintCount.csv' , header = 0)\n",
    "train.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
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       "    SubFPC1  SubFPC2  SubFPC3  SubFPC4   SubFPC5  SubFPC6  SubFPC7  SubFPC8  \\\n",
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       "   SubFPC9  SubFPC10    ...      SubFPC298  SubFPC299  SubFPC300  SubFPC301  \\\n",
       "0      0.0       0.0    ...            0.0        0.0   0.085714   0.085714   \n",
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       "\n",
       "   SubFPC302  SubFPC303  SubFPC304  SubFPC305  SubFPC306  SubFPC307  \n",
       "0       0.04       0.25        0.0        0.0        0.0   0.354839  \n",
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     "execution_count": 35,
     "metadata": {},
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   "source": [
    "pca3 = train[train.columns[2:]]\n",
    "pca3.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zeromtmu/anaconda2/envs/ER_alpha/lib/python2.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
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       "      <th>values</th>\n",
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       "\n",
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     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "pca3['values'] = \"Training\"\n",
    "\n",
    "frames = [pca1,pca2,pca3]\n",
    "pca = pd.concat(frames)\n",
    "pca.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def PCA_plot(data):\n",
    "    # PCA's components graphed in 2D and 3D\n",
    "    # Apply Scaling \n",
    "    from sklearn.decomposition import PCA\n",
    "    from mpl_toolkits.mplot3d import Axes3D\n",
    "    from sklearn.preprocessing import StandardScaler\n",
    "    from matplotlib import pyplot as plt\n",
    "    data_pca = data\n",
    "    \n",
    "    # Apply Scaling \n",
    "    X = data_pca.drop('values', axis=1).as_matrix().astype(np.float)\n",
    "    y = data_pca['values'].values\n",
    "    \n",
    "    # Formatting\n",
    "    target_names = [\">\",\"<\",\"Training\"]\n",
    "    colors = ['red','blue',\"green\"]\n",
    "    lw = 2\n",
    "    alpha = 0.3\n",
    "\n",
    "    # 2 Components PCA\n",
    "    plt.style.use('seaborn-whitegrid')\n",
    "    plt.figure(2, figsize=(20, 8))\n",
    "\n",
    "    plt.subplot(1, 2, 1)\n",
    "    pca = PCA(n_components=2)\n",
    "    X_std = StandardScaler().fit_transform(X)\n",
    "    X_r = pca.fit_transform(X_std)\n",
    "    #X_r = pca.fit(X).transform(X)\n",
    "        \n",
    "    for color, i, target_name in zip(colors, [\">\",\"<\",\"Training\"], target_names):\n",
    "        plt.scatter(X_r[y == i, 0], X_r[y == i, 1], \n",
    "                    color=color, \n",
    "                    alpha=alpha, \n",
    "                    lw=lw,\n",
    "                    label=target_name)\n",
    "    plt.xlabel(\"PC1\", fontsize=12)\n",
    "    plt.ylabel(\"PC2\", fontsize=12)\n",
    "    plt.legend(loc='best', shadow=False, scatterpoints=1)\n",
    "    plt.title('First two PCA directions');\n",
    "\n",
    "    # 3 Components PCA\n",
    "    ax = plt.subplot(1, 2, 2, projection='3d')\n",
    "\n",
    "    pca = PCA(n_components=3)\n",
    "    X_reduced = pca.fit_transform(X_std)\n",
    "    for color, i, target_name in zip(colors, [\">\",\"<\",\"Training\"], target_names):\n",
    "        ax.scatter(X_reduced[y == i, 0], X_reduced[y == i, 1], X_reduced[y == i, 2], \n",
    "                   color=color,\n",
    "                   alpha=alpha,\n",
    "                   lw=lw, \n",
    "                   label=target_name)\n",
    "    plt.legend(loc='best', shadow=False, scatterpoints=1)\n",
    "    ax.set_title(\"First three PCA directions\")\n",
    "    ax.set_xlabel(\"PC1\", fontsize=12)\n",
    "    ax.set_ylabel(\"PC2\", fontsize=12)\n",
    "    ax.set_zlabel(\"PC3\", fontsize=12)\n",
    "\n",
    "    # rotate the axes\n",
    "    ax.view_init(30, 10)\n",
    "    plt.savefig('Result/PCA.pdf', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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QUpjR8xkTma6uLqxYsQJbtmwBAFxxxRW9JkuJJjbRHp80aRL+9a9/YcqUKfjo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DnkOA6u6zI2Njb0ub52u41t26TT6dDn9XXzJrGf/Ww9HHJdtBs30F57DbtYoJ2/gY1DyrLBgXTr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42NjQDceOONfPjDH56VdQkhTh8SDom5Z6wWnOZm+L//U2HFsmVw/vnqgrijY9yX8Tl9+Fw+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\ngIKJQwAAAAAFE4cAAAAACvYDnAYwXDLnNdMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112da4e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PCA_plot(pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pca .to_csv('SubFiles/PCA data.csv' , sep=',' ,index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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