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iwatobipen/CP_RDKit.ipynb

Created May 22, 2020 12:59
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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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"1 1 True [0 1 0] : [1 1 1]\n",
"0 0 True [1 0 0] : [1 1 1]\n",
"1 1 True [1 1 0] : [1 1 1]\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": [
{
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.05</td>\n",
" <td>2.135922</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.10</td>\n",
" <td>1.708738</td>\n",
" </tr>\n",
" <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",
" </tr>\n",
" <tr>\n",
" <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": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.7"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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