CP_RDKit.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import RDConfig\n",
    "from rdkit.Chem import DataStructs\n",
    "from rdkit.Chem import AllChem\n",
    "from rdkit.Chem.Draw import IPythonConsole\n",
    "from rdkit.Chem import Draw\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from nonconformist.nc import ClassifierNc\n",
    "from nonconformist.nc import ClassifierAdapter\n",
    "from nonconformist.icp import IcpClassifier\n",
    "from nonconformist.evaluation import ClassIcpCvHelper\n",
    "from nonconformist.evaluation import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = os.path.join(RDConfig.RDDocsDir, 'Book/data/solubility.train.sdf')\n",
    "test =  os.path.join(RDConfig.RDDocsDir, 'Book/data/solubility.test.sdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainmol = [m for m in Chem.SDMolSupplier(train)]\n",
    "testmol = [m for m in Chem.SDMolSupplier(test)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'(A) low', '(C) high', '(B) medium'}\n"
     ]
    }
   ],
   "source": [
    "labels = set([m.GetProp('SOL_classification') for m in trainmol])\n",
    "print(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "label2cls = {'(A) low':0, '(B) medium':1, '(C) high':2}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fp2arr(fp):\n",
    "    arr = np.zeros((1,))\n",
    "    DataStructs.ConvertToNumpyArray(fp, arr)\n",
    "    return arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainfps = [AllChem.GetMorganFingerprintAsBitVect(m, 2, 1024) for m in trainmol]\n",
    "trainfps = np.array([fp2arr(fp) for fp in trainfps])\n",
    "\n",
    "testfps = [AllChem.GetMorganFingerprintAsBitVect(m, 2, 1024) for m in testmol]\n",
    "testfps = np.array([fp2arr(fp) for fp in testfps])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1025, 1024) (1025,) (257, 1024) (257,)\n"
     ]
    }
   ],
   "source": [
    "train_cls = [label2cls[m.GetProp('SOL_classification')] for m in trainmol]\n",
    "train_cls = np.array(train_cls)\n",
    "test_cls = [label2cls[m.GetProp('SOL_classification')] for m in testmol]\n",
    "test_cls = np.array(test_cls)\n",
    "print(trainfps.shape, train_cls.shape, testfps.shape, test_cls.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train data is devided to train and calibration data\n",
    "ids = np.random.permutation(train_cls.size)\n",
    "# Use first 700 data for train and second set is used for calibration\n",
    "trainX, calibX = trainfps[ids[:700],:],trainfps[ids[700:],:] \n",
    "trainY, calibY = train_cls[ids[:700]],train_cls[ids[700:]] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "testX = testfps\n",
    "testY = test_cls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier(n_estimators=500, random_state=794)\n",
    "nc = ClassifierNc(ClassifierAdapter(rf))\n",
    "icp = IcpClassifier(nc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "icp.fit(trainX, trainY)\n",
    "icp.calibrate(calibX, calibY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = icp.predict(testX)\n",
    "pred95 = icp.predict(testX, significance=0.05).astype(np.int32)\n",
    "pred80 = icp.predict(testX, significance=0.2).astype(np.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nonconformist.evaluation import class_avg_c, class_n_correct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "244"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_n_correct(pred, testY, significance=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "232"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_n_correct(pred, testY, significance=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "0 0 True [1 0 0] : [1 1 0]\n",
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     ]
    }
   ],
   "source": [
    "tp = 0\n",
    "for idx, j in enumerate(testY):\n",
    "    print(j, np.argmax(pred[idx]), j == np.argmax(pred[idx]) , pred80[idx], \":\", pred95[idx])\n",
    "    if j == np.argmax(pred[idx]):\n",
    "        tp += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6848249027237354\n"
     ]
    }
   ],
   "source": [
    "print(tp/testY.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0155642023346303\n",
      "1.2996108949416343\n"
     ]
    }
   ],
   "source": [
    "print(class_avg_c(pred, testY, significance=0.05))\n",
    "print(class_avg_c(pred, testY, significance=0.2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "icpmodel = ClassIcpCvHelper(icp)\n",
    "res = cross_val_score(icpmodel,\n",
    "                trainfps,\n",
    "                train_cls,\n",
    "                iterations=10,\n",
    "                scoring_funcs=[class_avg_c],\n",
    "                significance_levels=[0.05, 0.1, 0.2],\n",
    "               )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iter</th>\n",
       "      <th>fold</th>\n",
       "      <th>significance</th>\n",
       "      <th>class_avg_c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>2.135922</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1.708738</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.330097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>2.495146</td>\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1.922330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.281553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.05</td>\n",
       "      <td>2.194175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1.582524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.271845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.05</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   iter  fold  significance  class_avg_c\n",
       "0     0     0          0.05     2.135922\n",
       "1     0     0          0.10     1.708738\n",
       "2     0     0          0.20     1.330097\n",
       "3     0     1          0.05     2.495146\n",
       "4     0     1          0.10     1.922330\n",
       "5     0     1          0.20     1.281553\n",
       "6     0     2          0.05     2.194175\n",
       "7     0     2          0.10     1.582524\n",
       "8     0     2          0.20     1.271845\n",
       "9     0     3          0.05     2.000000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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