├── README.md └── data and code ├── 1st ├── 1st-FEC2+-LR.csv ├── 1st-FEC2+.csv ├── 1st-FECO - LR.csv ├── 1st-FECO20%.csv ├── 1st-jC2+.csv └── 1st-jCO.csv ├── 2nd ├── Local pH │ └── 2nd-CuSn-local ph-LR.csv ├── MFF │ ├── 2nd-CuSn-MFF-FE-C1.csv │ ├── 2nd-CuSn-MFF-FE-HCOOHCO.csv │ ├── 2nd-CuSn-MFF-jC1.csv │ └── 2nd_Cu-MFF_FEC2+.csv └── one hot │ ├── 2nd-Cu-FEC2+-LR.csv │ ├── 2nd-Cu-jC2+-LR.csv │ ├── 2nd-CuSn-FE-C1-LR.csv │ ├── 2nd-CuSn-FE-HCOOHCO-LR.csv │ └── 2nd-CuSn-FE-jC1-LR.csv ├── 3rd ├── 24-predict-FEC2+.csv ├── 2nd-Cu-FEC2+-LR.csv ├── Features_94_275.csv ├── LRIF_23_275.csv ├── LRIF_title_23.csv ├── Smiles_94.csv ├── Title_275.csv └── Values_94.csv └── code ├── Classifier for LRIF results ├── DTC_LRIF.ipynb ├── GBCT_LRIF.ipynb ├── LogisticRegression_LRIF.ipynb ├── RFC_LRIF.ipynb ├── SVC_LRIF.ipynb └── XGBoostClassifier_LRIF.ipynb ├── Classifier └── 1027mff-classification-problem-final.ipynb ├── LRIF ├── DataPre_afterLRIF.ipynb ├── ECFP(modified)_V6.0.ipynb └── LRIF.ipynb ├── Regression └── 1027final_regression-analysis.ipynb ├── Smiles2RDKitSmiles.py ├── classification_feature_selection ├── GBCT_feature_selection.ipynb ├── LR_feature_selcection.ipynb ├── XGBoost_feature_selection.ipynb ├── decision_tree_feature_selection.ipynb └── random_forest_feature_selection.ipynb └── regression_and_prediction └── final_regression_analysisforpredict-Copy1.ipynb /README.md: -------------------------------------------------------------------------------- 1 | # Machine-learning-for-Cu-CO2RR 2 | This is the Python code and original data of "Machine-Learning Guided Discovery and Optimization of Additives in Preparing Cu Catalyst for Selective Electrochemical CO2 Reduction" from XMU Wang-group. 3 | The original data lies in the files ist,2nd and 3rd,respectively.Represented for the ML discovery and optimization procedure. 4 | in the 2nd round, besides the functional group-based featurization method, we used molecular fragment featurization (MFF) to extract matrix of substructure of a molecule . This MFF method was modified from extended-connectivity fingerprint (ECFP) method in Deepchem(https://deepchem.io/) by skipping hash function calculation step to avoid information loss.Before we formally pubulish this method, this page must be cited if you use it. Named ECFP6.0 here. 5 | LRIF means Random intersection tree part.find which combination is inportant first and generate new. 6 | Other program are basic python code from scikit learn, nothing special. 7 | The prediction of 24 molecules was done by the code named Smiles2RdkitSmiles.py 8 | 9 | This project finally published at Guo, Y. et al. Machine learning part was finished by Yuming Su and Yiheng Dai. 10 | The infomation of this published paper is below: 11 | Machine-Learning-Guided Discovery and Optimization of Additives in Preparing Cu Catalysts for CO2 Reduction. J. Am. Chem. Soc. 143, 5755-5762, doi:10.1021/jacs.1c00339 (2021). 12 | If used, please cite. 13 | https://pubs.acs.org/doi/10.1021/jacs.1c00339?ref=pdf 14 | -------------------------------------------------------------------------------- /data and code/1st/1st-FEC2+-LR.csv: -------------------------------------------------------------------------------- 1 | Cu,Pd,Au,Ag,Bi,Sn,Co,Ni,In,Fe,Zn,Ce,alcoholic hydroxyl ,phenolic hydroxyl ,sulfhydryl,aliphatic amine,aromatic amine,carboxy ,pyridine ,imidazole ,pyrrole ,triazole ,cyano,phosphate ,alkynyl,FEC2+ 2 | 0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0 3 | 0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,10.5 4 | 1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,3.6 5 | 0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,30.2 6 | 0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,19.6 7 | 1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,12.4 8 | 0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,2 9 | 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1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,10.6 61 | 0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,39.7 62 | 0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,20.3 63 | 0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,44.6 64 | 0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0 65 | 1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,32.8 66 | 1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,13.8 67 | 0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,13.5 68 | 0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,38.4 69 | 1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,24.8 70 | 1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,10.7 71 | 1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,11.2 72 | 1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,35.4 73 | 1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,16.6 74 | 1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,21.6 75 | 1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,29.7 76 | 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1,0,0,1,0,0,0,0,0,0,0,0,0,40.8 60 | 1,0,0,0,0,1,0,0,0,0,0,0,0,46.2 61 | 0,0,0,1,0,1,0,0,0,0,0,0,0,48.23 62 | 0,0,0,1,0,1,0,0,0,0,0,0,0,36.5 63 | 0,1,0,0,0,0,0,0,0,0,0,0,0,40.2 64 | 0,0,0,0,1,1,1,0,0,0,0,0,0,0 65 | 0,1,0,1,0,1,0,0,0,0,0,0,0,8.3 66 | 0,0,0,0,0,1,1,0,0,0,0,0,0,0 67 | 0,0,0,1,0,1,0,1,0,0,0,0,0,44.8 68 | 0,0,0,0,1,0,0,0,0,0,0,0,0,0 69 | 0,1,0,0,0,0,0,0,0,0,0,0,0,13.2 70 | 0,0,0,0,0,0,0,0,0,0,0,0,0,16.1 71 | 0,0,0,0,0,1,0,0,0,0,0,0,0,4.7 72 | 1,0,0,0,0,0,0,0,0,0,0,0,1,49.4 73 | 1,0,0,0,0,0,0,0,0,0,0,0,1,25.9 74 | 0,1,0,0,0,0,0,0,0,0,0,0,0,0.3 75 | 0,0,0,1,0,1,0,0,0,0,0,0,0,22.8 76 | 0,0,0,0,0,0,0,0,0,0,0,0,0,0 77 | 1,0,0,0,1,0,0,1,0,0,0,1,0,32.8 78 | 1,0,0,0,0,0,0,0,0,0,0,0,0,52.9 79 | 1,0,0,0,0,0,0,0,0,0,0,0,0,31.3 80 | 1,0,0,0,0,0,0,0,0,0,0,0,0,49.1 81 | 1,0,0,0,0,0,0,0,0,0,0,0,0,11.6 82 | 1,0,0,0,0,1,0,0,0,0,0,0,0,19.3 83 | 1,0,0,0,0,0,0,0,0,0,0,0,0,32.8 84 | 1,0,0,0,0,0,0,0,0,0,0,0,0,42.2 85 | 1,0,0,1,0,0,0,0,0,0,0,0,0,43.6 86 | 1,0,0,1,0,0,0,0,0,0,0,0,0,51.4 87 | 0,0,0,1,0,0,0,0,0,0,0,0,0,31.8 88 | 1,0,0,0,0,0,0,0,0,0,0,0,0,46 89 | 1,0,0,0,0,0,0,0,0,0,0,0,0,51.2 90 | 1,0,0,1,0,0,0,0,0,0,0,0,0,54.2 91 | 1,0,0,1,0,0,0,0,0,0,0,0,0,43.7 92 | 1,0,0,1,0,0,0,0,0,0,0,0,0,44 93 | 1,0,0,0,0,0,0,0,0,0,0,0,0,34.2 94 | 0,1,0,1,0,1,0,0,0,0,0,0,0,45.7 95 | 0,0,0,1,0,1,0,0,1,0,0,0,0,0 96 | 0,0,0,0,0,0,0,0,0,0,0,1,0,12.4 97 | 0,0,0,0,0,0,0,0,0,0,0,1,0,22 98 | 0,0,0,0,0,0,0,0,0,0,0,1,0,8.6 99 | 0,0,0,0,0,1,1,0,0,0,0,0,0,1.5 100 | -------------------------------------------------------------------------------- /data and code/2nd/one hot/2nd-Cu-jC2+-LR.csv: -------------------------------------------------------------------------------- 1 | alcoholic hydroxyl ,phenolic hydroxyl ,sulfhydryl,aliphatic amine,aromatic amine,carboxy ,pyridine ,imidazole,pyrrole ,triazole ,cyano,phosphate ,alkynyl,jC2+ 2 | 0,0,0,0,0,1,0,0,0,1,0,0,0,0.379636364 3 | 0,0,0,0,0,0,0,1,0,0,0,0,0,1.0416 4 | 0,0,0,0,0,1,1,0,0,0,0,0,0,1.488 5 | 0,0,0,0,0,1,1,0,0,0,0,0,0,0.101091667 6 | 0,0,0,0,0,1,1,0,0,0,0,0,0,3.402089069 7 | 0,0,0,1,0,1,0,0,0,0,0,0,0,1.273904762 8 | 0,0,0,0,1,0,0,0,1,0,0,0,0,1.175636364 9 | 0,1,0,0,0,1,0,0,0,0,0,0,0,2.422967742 10 | 0,1,0,0,0,1,0,0,0,0,0,0,0,2.544137931 11 | 1,0,0,0,0,0,0,1,0,0,0,0,0,0.658842593 12 | 1,0,0,1,0,1,0,0,0,0,0,0,0,0.890343434 13 | 1,0,0,0,0,1,0,0,0,0,0,0,0,1.631176471 14 | 0,1,0,0,0,0,0,0,0,0,0,0,0,0.808717949 15 | 1,0,0,1,0,1,0,0,0,0,0,0,0,0.870352941 16 | 1,0,0,0,0,1,0,0,0,0,0,0,0,2.37006 17 | 0,0,0,0,0,1,1,0,0,0,0,0,0,2.957892157 18 | 0,0,1,0,0,0,1,0,0,0,0,0,0,0 19 | 0,0,1,1,0,1,0,0,0,0,0,0,0,0 20 | 0,0,0,0,0,0,1,0,0,0,0,0,0,0 21 | 0,0,0,0,0,0,1,0,0,0,0,0,0,0 22 | 1,1,0,0,0,0,0,0,0,0,0,0,0,3.169157895 23 | 0,0,0,0,0,0,1,0,0,0,0,0,0,0 24 | 0,1,0,0,0,1,0,0,0,0,0,0,0,1.161151515 25 | 0,0,0,0,0,1,0,0,0,0,0,0,0,2.76365625 26 | 0,0,0,0,1,0,1,0,0,0,0,0,0,0.149547619 27 | 0,0,0,1,0,1,0,0,0,0,0,0,0,0.37848 28 | 0,1,0,0,0,0,0,0,0,0,0,0,0,0 29 | 1,0,0,1,0,1,0,0,0,0,0,0,0,2.99804 30 | 0,0,0,0,1,0,0,0,0,0,0,0,0,0 31 | 0,0,0,0,1,0,1,1,0,0,0,0,0,0 32 | 0,0,0,0,0,1,0,0,0,0,0,0,0,0.879050505 33 | 0,0,0,0,0,1,0,0,0,0,0,0,0,2.951666667 34 | 1,0,0,0,0,0,0,0,0,0,0,0,0,1.194666667 35 | 1,0,0,0,0,1,0,0,0,0,0,0,0,2.3066 36 | 0,0,0,0,1,1,0,0,0,0,0,0,0,0.654520833 37 | 0,0,0,0,0,0,1,0,0,0,0,0,0,0.628 38 | 0,0,0,1,0,0,0,0,0,0,0,0,0,0 39 | 1,0,0,0,0,0,0,0,0,0,0,0,0,0.895 40 | 1,0,0,1,0,0,0,0,0,0,0,0,0,1.64964 41 | 1,0,0,1,0,1,0,0,0,0,0,0,0,0.82134 42 | 0,0,0,1,0,1,0,0,0,0,0,0,0,1.71243 43 | 0,0,0,0,0,0,1,0,0,0,0,1,0,3.80424 44 | 1,0,0,0,0,0,0,0,0,0,0,0,0,2.82755 45 | 1,0,0,0,0,0,0,0,0,0,0,0,0,3.11751 46 | 1,0,0,0,0,1,0,0,0,0,0,0,0,0.85725 47 | 1,0,0,0,0,1,0,0,0,0,0,0,0,1.17813 48 | 0,1,0,0,0,0,1,0,0,0,0,0,0,3.78575 49 | 1,0,0,0,0,0,0,0,0,0,0,0,0,0.97 50 | 0,0,0,0,0,0,0,0,0,0,0,1,0,2.37688 51 | 1,0,0,0,0,0,0,0,0,0,0,0,0,2.47842 52 | 0,0,0,0,0,0,0,0,0,0,0,1,0,1.30036 53 | 0,0,0,0,0,0,1,0,0,0,0,0,0,2.68065 54 | 0,0,0,0,0,0,1,0,0,0,0,0,0,0.07575 55 | 0,1,0,0,0,0,0,0,0,0,0,0,0,2.97375 56 | 1,0,0,0,0,0,0,0,0,0,0,0,0,1.44129 57 | 1,0,0,0,0,0,0,0,0,0,0,0,0,1.80203252 58 | 0,0,0,0,0,1,1,0,0,0,0,0,0,3.20933 59 | 1,0,0,1,0,0,0,0,0,0,0,0,0,2.57856 60 | 1,0,0,0,0,1,0,0,0,0,0,0,0,2.39316 61 | 0,0,0,1,0,1,0,0,0,0,0,0,0,4.012736 62 | 0,0,0,1,0,1,0,0,0,0,0,0,0,1.95275 63 | 0,1,0,0,0,0,0,0,0,0,0,0,0,3.12354 64 | 0,0,0,0,1,1,1,0,0,0,0,0,0,0 65 | 0,1,0,1,0,1,0,0,0,0,0,0,0,0.39259 66 | 0,0,0,0,0,1,1,0,0,0,0,0,0,0 67 | 0,0,0,1,0,1,0,1,0,0,0,0,0,3.258181818 68 | 0,0,0,0,1,0,0,0,0,0,0,0,0,0 69 | 0,1,0,0,0,0,0,0,0,0,0,0,0,1.16028 70 | 0,0,0,0,0,0,0,0,0,0,0,0,0,1.33147 71 | 0,0,0,0,0,1,0,0,0,0,0,0,0,0.25615 72 | 1,0,0,0,0,0,0,0,0,0,0,0,1,3.93224 73 | 1,0,0,0,0,0,0,0,0,0,0,0,1,1.13442 74 | 0,1,0,0,0,0,0,0,0,0,0,0,0,0.027 75 | 0,0,0,1,0,1,0,0,0,0,0,0,0,0.94848 76 | 0,0,0,0,0,0,0,0,0,0,0,0,0,0 77 | 1,0,0,0,1,0,0,1,0,0,0,1,0,1.4104 78 | 1,0,0,0,0,0,0,0,0,0,0,0,0,4.00453 79 | 1,0,0,0,0,0,0,0,0,0,0,0,0,1.10176 80 | 1,0,0,0,0,0,0,0,0,0,0,0,0,1.80197 81 | 1,0,0,0,0,0,0,0,0,0,0,0,0,0.44312 82 | 1,0,0,0,0,1,0,0,0,0,0,0,0,0.85499 83 | 1,0,0,0,0,0,0,0,0,0,0,0,0,2.32552 84 | 1,0,0,0,0,0,0,0,0,0,0,0,0,2.1311 85 | 1,0,0,1,0,0,0,0,0,0,0,0,0,2.18436 86 | 1,0,0,1,0,0,0,0,0,0,0,0,0,3.84986 87 | 0,0,0,1,0,0,0,0,0,0,0,0,0,1.40238 88 | 1,0,0,0,0,0,0,0,0,0,0,0,0,2.116 89 | 1,0,0,0,0,0,0,0,0,0,0,0,0,2.92864 90 | 1,0,0,1,0,0,0,0,0,0,0,0,0,3.2791 91 | 1,0,0,1,0,0,0,0,0,0,0,0,0,2.63948 92 | 1,0,0,1,0,0,0,0,0,0,0,0,0,3.3352 93 | 1,0,0,0,0,0,0,0,0,0,0,0,0,1.2483 94 | 0,1,0,1,0,1,0,0,0,0,0,0,0,2.92937 95 | 0,0,0,1,0,1,0,0,1,0,0,0,0,0 96 | 0,0,0,0,0,0,0,0,0,0,0,1,0,0.68696 97 | 0,0,0,0,0,0,0,0,0,0,0,1,0,2.1736 98 | 0,0,0,0,0,0,0,0,0,0,0,1,0,0.55814 99 | 0,0,0,0,0,1,1,0,0,0,0,0,0,0.1215 100 | -------------------------------------------------------------------------------- /data and code/2nd/one hot/2nd-CuSn-FE-C1-LR.csv: -------------------------------------------------------------------------------- 1 | alcoholic hydroxyl ,phenolic hydroxyl ,sulfhydryl,aliphatic amine,aromatic amine,carboxy ,pyridine ,imidazole,pyrrole ,triazole ,cyano,phosphate ,alkynyl,FEC1 2 | 0,0,0,1,0,1,0,0,0,0,0,0,0,75.5 3 | 0,0,0,1,0,1,0,0,0,0,0,0,0,58.2 4 | 0,0,0,0,0,0,1,0,0,0,0,0,0,67 5 | 0,0,0,1,0,1,0,0,0,0,0,0,0,74.5 6 | 0,1,0,0,0,1,0,0,0,0,0,0,0,72.2 7 | 0,0,1,1,0,1,0,0,0,0,0,0,0,45.6 8 | 0,0,0,1,0,1,0,0,0,0,0,0,0,80.5 9 | 0,0,0,1,0,1,0,0,0,0,0,0,0,82.6 10 | 0,0,0,1,0,1,0,0,0,0,0,0,0,85.55 11 | 0,0,0,1,0,1,0,0,0,0,0,0,0,85 12 | 0,0,1,1,0,1,0,0,0,0,0,0,0,79.5 13 | 0,0,0,1,0,1,0,0,1,0,0,0,0,97 14 | 0,0,0,1,0,1,0,0,0,0,0,0,0,82.1 15 | 0,0,0,0,0,1,1,0,0,0,0,0,0,92.1 16 | 1,0,0,1,0,0,0,0,0,0,0,0,0,59.1 17 | 0,0,1,0,0,0,1,0,0,0,0,0,0,58.6 18 | 0,0,0,0,0,1,0,0,0,1,0,0,0,82.1 19 | 0,0,0,0,0,1,1,0,0,0,0,0,0,91.4 20 | 0,0,0,0,0,0,0,0,0,1,0,0,0,64.6 21 | 0,0,0,0,0,1,1,0,0,0,0,0,0,84 22 | 0,0,0,1,0,0,1,0,0,0,0,0,0,70.6 23 | 0,0,0,0,0,1,1,0,0,0,0,0,0,76.4 24 | 0,0,0,1,0,0,0,0,0,1,0,0,0,83.8 25 | 0,0,0,0,0,0,1,0,0,0,0,0,0,13 26 | 0,0,0,0,0,0,1,0,0,0,0,1,0,92.7 27 | 0,0,0,0,0,0,1,0,0,0,0,0,0,70.4 28 | 0,0,0,0,0,0,1,0,0,0,0,0,0,27.3 29 | 0,0,0,0,1,0,1,1,0,0,0,0,0,56.7 30 | 1,0,0,0,0,0,1,0,0,0,0,0,0,61.9 31 | 1,0,0,0,0,0,1,0,0,0,0,0,0,66.1 32 | 0,0,0,0,0,0,1,0,0,0,0,0,0,54.4 33 | 0,1,0,0,0,0,1,0,0,0,0,0,0,78.3 34 | 0,0,0,0,0,1,1,0,0,0,0,0,0,74 35 | 0,0,0,0,0,0,1,0,0,0,0,0,0,75 36 | 0,0,0,0,0,0,1,0,0,0,0,0,0,91.8 37 | 0,0,0,0,0,0,0,1,0,0,0,0,0,85.8 38 | 0,0,0,0,0,0,0,0,1,0,0,0,0,86.5 39 | 0,0,0,0,0,0,0,1,0,0,0,0,0,83.3 40 | 0,0,0,1,0,0,1,0,0,0,0,0,0,86.3 41 | 0,0,0,0,0,0,0,0,0,0,0,0,0,87 42 | 0,0,0,1,0,0,0,0,0,0,0,0,0,87 43 | 0,0,0,0,0,0,0,0,1,0,0,0,0,79.5 44 | -------------------------------------------------------------------------------- /data and code/2nd/one hot/2nd-CuSn-FE-HCOOHCO-LR.csv: -------------------------------------------------------------------------------- 1 | alcoholic hydroxyl ,phenolic hydroxyl ,sulfhydryl,aliphatic amine,aromatic amine,carboxy ,pyridine ,imidazole,pyrrole ,triazole ,cyano,phosphate ,alkynyl,FEHCOOH/FECO 2 | 0,0,0,1,0,1,0,0,0,0,0,0,0,0.480392157 3 | 0,0,0,1,0,1,0,0,0,0,0,0,0,2.079365079 4 | 0,0,0,0,0,0,1,0,0,0,0,0,0,0.970588235 5 | 0,0,0,1,0,1,0,0,0,0,0,0,0,1.483333333 6 | 0,1,0,0,0,1,0,0,0,0,0,0,0,2.139130435 7 | 0,0,1,1,0,1,0,0,0,0,0,0,0,1.017699115 8 | 0,0,0,1,0,1,0,0,0,0,0,0,0,2.926829268 9 | 0,0,0,1,0,1,0,0,0,0,0,0,0,2.2265625 10 | 0,0,0,1,0,1,0,0,0,0,0,0,0,1.764135703 11 | 0,0,0,1,0,1,0,0,0,0,0,0,0,2.035714286 12 | 0,0,1,1,0,1,0,0,0,0,0,0,0,0.414590747 13 | 0,0,0,1,0,1,0,0,1,0,0,0,0,0.077777778 14 | 0,0,0,1,0,1,0,0,0,0,0,0,0,2.966183575 15 | 0,0,0,0,0,1,1,0,0,0,0,0,0,0.416923077 16 | 1,0,0,1,0,0,0,0,0,0,0,0,0,0.296052632 17 | 0,0,1,0,0,0,1,0,0,0,0,0,0,0.908794788 18 | 0,0,0,0,0,1,0,0,0,1,0,0,0,0.432809773 19 | 0,0,0,0,0,1,1,0,0,0,0,0,0,0.714821764 20 | 0,0,0,0,0,0,0,0,0,1,0,0,0,0.867052023 21 | 0,0,0,0,0,1,1,0,0,0,0,0,0,0.423728814 22 | 0,0,0,1,0,0,1,0,0,0,0,0,0,1.654135338 23 | 0,0,0,0,0,1,1,0,0,0,0,0,0,0.469230769 24 | 0,0,0,1,0,0,0,0,0,1,0,0,0,0.837719298 25 | 0,0,0,0,0,0,1,0,0,0,0,0,0,3.333333333 26 | 0,0,0,0,0,0,1,0,0,0,0,1,0,0.598275862 27 | 0,0,0,0,0,0,1,0,0,0,0,0,0,0 28 | 0,0,0,0,0,0,1,0,0,0,0,0,0,1.219512195 29 | 0,0,0,0,1,0,1,1,0,0,0,0,0,0.327868852 30 | 1,0,0,0,0,0,1,0,0,0,0,0,0,0.650666667 31 | 1,0,0,0,0,0,1,0,0,0,0,0,0,2.032110092 32 | 0,0,0,0,0,0,1,0,0,0,0,0,0,0.732484076 33 | 0,1,0,0,0,0,1,0,0,0,0,0,0,1.501597444 34 | 0,0,0,0,0,1,1,0,0,0,0,0,0,0.321428571 35 | 0,0,0,0,0,0,1,0,0,0,0,0,0,0.630434783 36 | 0,0,0,0,0,0,1,0,0,0,0,0,0,0.669090909 37 | 0,0,0,0,0,0,0,1,0,0,0,0,0,0.640535373 38 | 0,0,0,0,0,0,0,0,1,0,0,0,0,1.189873418 39 | 0,0,0,0,0,0,0,1,0,0,0,0,0,0.772340426 40 | 0,0,0,1,0,0,1,0,0,0,0,0,0,1.241558442 41 | 0,0,0,0,0,0,0,0,0,0,0,0,0,0.673076923 42 | 0,0,0,1,0,0,0,0,0,0,0,0,0,0.611111111 43 | 0,0,0,0,0,0,0,0,1,0,0,0,0,1.5 44 | -------------------------------------------------------------------------------- /data and code/2nd/one hot/2nd-CuSn-FE-jC1-LR.csv: -------------------------------------------------------------------------------- 1 | alcoholic hydroxyl ,phenolic hydroxyl ,sulfhydryl,aliphatic amine,aromatic amine,carboxy ,pyridine ,imidazole,pyrrole ,triazole ,cyano,phosphate ,alkynyl,jC1 2 | 0,0,0,1,0,1,0,0,0,0,0,0,0,6.018881119 3 | 0,0,0,1,0,1,0,0,0,0,0,0,0,3.658897059 4 | 0,0,0,0,0,0,1,0,0,0,0,0,0,4.7168 5 | 0,0,0,1,0,1,0,0,0,0,0,0,0,6.258603239 6 | 0,1,0,0,0,1,0,0,0,0,0,0,0,5.134222222 7 | 0,0,1,1,0,1,0,0,0,0,0,0,0,2.985435897 8 | 0,0,0,1,0,1,0,0,0,0,0,0,0,8.533 9 | 0,0,0,1,0,1,0,0,0,0,0,0,0,4.456728889 10 | 0,0,0,1,0,1,0,0,0,0,0,0,0,5.332616667 11 | 0,0,0,1,0,1,0,0,0,0,0,0,0,10.18768116 12 | 0,0,1,1,0,1,0,0,0,0,0,0,0,5.317666667 13 | 0,0,0,1,0,1,0,0,1,0,0,0,0,6.795987654 14 | 0,0,0,1,0,1,0,0,0,0,0,0,0,5.282016211 15 | 0,0,0,0,0,1,1,0,0,0,0,0,0,7.204266667 16 | 1,0,0,1,0,0,0,0,0,0,0,0,0,2.199833333 17 | 0,0,1,0,0,0,1,0,0,0,0,0,0,4.639166667 18 | 0,0,0,0,0,1,0,0,0,1,0,0,0,6.125303922 19 | 0,0,0,0,0,1,1,0,0,0,0,0,0,7.136585859 20 | 0,0,0,0,0,0,0,0,0,1,0,0,0,1.942306667 21 | 0,0,0,0,0,1,1,0,0,0,0,0,0,5.454814815 22 | 0,0,0,1,0,0,1,0,0,0,0,0,0,4.191875 23 | 0,0,0,0,0,1,1,0,0,0,0,0,0,3.456190476 24 | 0,0,0,1,0,0,0,0,0,1,0,0,0,5.166689977 25 | 0,0,0,0,0,0,1,0,0,0,0,0,0,1.41154321 26 | 0,0,0,0,0,0,1,0,0,0,0,1,0,3.484241379 27 | 0,0,0,0,0,0,1,0,0,0,0,0,0,2.261825641 28 | 0,0,0,0,0,0,1,0,0,0,0,0,0,2.46025 29 | 0,0,0,0,1,0,1,1,0,0,0,0,0,2.7594 30 | 1,0,0,0,0,0,1,0,0,0,0,0,0,2.529057143 31 | 1,0,0,0,0,0,1,0,0,0,0,0,0,3.540022222 32 | 0,0,0,0,0,0,1,0,0,0,0,0,0,3.350133333 33 | 0,1,0,0,0,0,1,0,0,0,0,0,0,3.023780488 34 | 0,0,0,0,0,1,1,0,0,0,0,0,0,5.729032258 35 | 0,0,0,0,0,0,1,0,0,0,0,0,0,3.433823529 36 | 0,0,0,0,0,0,1,0,0,0,0,0,0,4.978384615 37 | 0,0,0,0,0,0,0,1,0,0,0,0,0,4.157214286 38 | 0,0,0,0,0,0,0,0,1,0,0,0,0,5.007388889 39 | 0,0,0,0,0,0,0,1,0,0,0,0,0,5.183111111 40 | 0,0,0,1,0,0,1,0,0,0,0,0,0,4.721117647 41 | 0,0,0,0,0,0,0,0,0,0,0,0,0,5.202424242 42 | 0,0,0,1,0,0,0,0,0,0,0,0,0,4.469411765 43 | 0,0,0,0,0,0,0,0,1,0,0,0,0,5.291166667 44 | -------------------------------------------------------------------------------- /data and code/3rd/24-predict-FEC2+.csv: -------------------------------------------------------------------------------- 1 | alcoholic hydroxyl ,phenolic hydroxyl ,sulfhydryl,aliphatic amine,aromatic amine,carboxy ,pyridine ,imidazole,pyrrole ,triazole ,cyano,phosphate ,alkynyl,,, 2 | 0,0,0,0,0,1,1,0,0,0,0,0,0,,, 3 | 0,0,0,0,0,1,1,0,0,0,0,0,0,,, 4 | 0,0,0,0,0,1,1,0,0,0,0,0,0,,, 5 | 0,0,0,1,0,1,1,0,0,0,0,0,0,,, 6 | 0,0,0,1,0,1,1,0,0,0,0,0,0,,, 7 | 0,0,0,1,0,1,1,0,0,0,0,0,0,,, 8 | 0,0,0,0,0,1,1,0,0,0,0,0,0,,, 9 | 0,0,0,0,0,1,1,0,0,0,0,0,0,,, 10 | 1,0,0,0,0,0,1,0,0,0,0,0,0,,, 11 | 0,0,0,0,0,0,1,0,0,0,0,0,0,,, 12 | 0,0,0,0,0,1,0,0,0,0,0,0,0,,, 13 | 0,0,0,0,0,1,1,0,0,0,0,0,0,,, 14 | 0,0,0,0,0,1,1,0,0,0,0,0,0,,, 15 | 0,0,0,0,0,1,1,0,0,0,0,0,0,,, 16 | 0,0,0,0,0,0,1,0,0,0,0,0,0,,, 17 | 0,0,0,0,0,0,1,0,0,0,0,0,0,,, 18 | 1,0,0,0,0,0,0,0,0,0,0,0,0,,, 19 | 1,0,0,1,0,0,0,0,0,0,0,0,0,,, 20 | 1,0,0,1,0,0,0,0,0,0,0,0,0,,, 21 | 1,0,0,0,0,0,0,0,0,0,0,0,0,,, 22 | 1,0,0,1,0,0,0,0,0,0,0,0,0,,, 23 | 1,0,0,0,0,0,0,0,0,0,0,0,0,,, 24 | 1,0,0,0,0,1,0,0,0,0,0,0,0,,, 25 | 1,0,0,0,1,0,0,0,0,0,0,0,0,,, 26 | -------------------------------------------------------------------------------- /data and code/3rd/2nd-Cu-FEC2+-LR.csv: -------------------------------------------------------------------------------- 1 | alcoholic hydroxyl ,phenolic hydroxyl ,sulfhydryl,aliphatic amine,aromatic amine,carboxy ,pyridine ,imidazole,pyrrole ,triazole ,cyano,phosphate ,alkynyl,FE-C2+ 2 | 0,0,0,0,0,1,0,0,0,1,0,0,0,3.6 3 | 0,0,0,0,0,0,0,1,0,0,0,0,0,12.4 4 | 0,0,0,0,0,1,1,0,0,0,0,0,0,18.6 5 | 0,0,0,0,0,1,1,0,0,0,0,0,0,3.5 6 | 0,0,0,0,0,1,1,0,0,0,0,0,0,42.1 7 | 0,0,0,1,0,1,0,0,0,0,0,0,0,17.6 8 | 0,0,0,0,1,0,0,0,1,0,0,0,0,10.6 9 | 0,1,0,0,0,1,0,0,0,0,0,0,0,32.8 10 | 0,1,0,0,0,1,0,0,0,0,0,0,0,24.8 11 | 1,0,0,0,0,0,0,1,0,0,0,0,0,10.7 12 | 1,0,0,1,0,1,0,0,0,0,0,0,0,11.2 13 | 1,0,0,0,0,1,0,0,0,0,0,0,0,35.4 14 | 0,1,0,0,0,0,0,0,0,0,0,0,0,16.6 15 | 1,0,0,1,0,1,0,0,0,0,0,0,0,21.6 16 | 1,0,0,0,0,1,0,0,0,0,0,0,0,29.7 17 | 0,0,0,0,0,1,1,0,0,0,0,0,0,41.5 18 | 0,0,1,0,0,0,1,0,0,0,0,0,0,0 19 | 0,0,1,1,0,1,0,0,0,0,0,0,0,0 20 | 0,0,0,0,0,0,1,0,0,0,0,0,0,0 21 | 0,0,0,0,0,0,1,0,0,0,0,0,0,0 22 | 1,1,0,0,0,0,0,0,0,0,0,0,0,47.6 23 | 0,0,0,0,0,0,1,0,0,0,0,0,0,0 24 | 0,1,0,0,0,1,0,0,0,0,0,0,0,14.7 25 | 0,0,0,0,0,1,0,0,0,0,0,0,0,36.9 26 | 0,0,0,0,1,0,1,0,0,0,0,0,0,1.1 27 | 0,0,0,1,0,1,0,0,0,0,0,0,0,11.4 28 | 0,1,0,0,0,0,0,0,0,0,0,0,0,0 29 | 1,0,0,1,0,1,0,0,0,0,0,0,0,31.1 30 | 0,0,0,0,1,0,0,0,0,0,0,0,0,0 31 | 0,0,0,0,1,0,1,1,0,0,0,0,0,0 32 | 0,0,0,0,0,1,0,0,0,0,0,0,0,10.6 33 | 0,0,0,0,0,1,0,0,0,0,0,0,0,38.5 34 | 1,0,0,0,0,0,0,0,0,0,0,0,0,25.6 35 | 1,0,0,0,0,1,0,0,0,0,0,0,0,38 36 | 0,0,0,0,1,1,0,0,0,0,0,0,0,17.8 37 | 0,0,0,0,0,0,1,0,0,0,0,0,0,8.4 38 | 0,0,0,1,0,0,0,0,0,0,0,0,0,0 39 | 1,0,0,0,0,0,0,0,0,0,0,0,0,17.9 40 | 1,0,0,1,0,0,0,0,0,0,0,0,0,23.6 41 | 1,0,0,1,0,1,0,0,0,0,0,0,0,16.9 42 | 0,0,0,1,0,1,0,0,0,0,0,0,0,35.9 43 | 0,0,0,0,0,0,1,0,0,0,0,1,0,48.4 44 | 1,0,0,0,0,0,0,0,0,0,0,0,0,48.5 45 | 1,0,0,0,0,0,0,0,0,0,0,0,0,51.7 46 | 1,0,0,0,0,1,0,0,0,0,0,0,0,22.5 47 | 1,0,0,0,0,1,0,0,0,0,0,0,0,17.3 48 | 0,1,0,0,0,0,1,0,0,0,0,0,0,47.5 49 | 1,0,0,0,0,0,0,0,0,0,0,0,0,9.7 50 | 0,0,0,0,0,0,0,0,0,0,0,1,0,40.7 51 | 1,0,0,0,0,0,0,0,0,0,0,0,0,29.4 52 | 0,0,0,0,0,0,0,0,0,0,0,1,0,11.6 53 | 0,0,0,0,0,0,1,0,0,0,0,0,0,34.5 54 | 0,0,0,0,0,0,1,0,0,0,0,0,0,0.75 55 | 0,1,0,0,0,0,0,0,0,0,0,0,0,37.5 56 | 1,0,0,0,0,0,0,0,0,0,0,0,0,32.1 57 | 1,0,0,0,0,0,0,0,0,0,0,0,0,31 58 | 0,0,0,0,0,1,1,0,0,0,0,0,0,48.7 59 | 1,0,0,1,0,0,0,0,0,0,0,0,0,40.8 60 | 1,0,0,0,0,1,0,0,0,0,0,0,0,46.2 61 | 0,0,0,1,0,1,0,0,0,0,0,0,0,48.23 62 | 0,0,0,1,0,1,0,0,0,0,0,0,0,36.5 63 | 0,1,0,0,0,0,0,0,0,0,0,0,0,40.2 64 | 0,0,0,0,1,1,1,0,0,0,0,0,0,0 65 | 0,1,0,1,0,1,0,0,0,0,0,0,0,8.3 66 | 0,0,0,0,0,1,1,0,0,0,0,0,0,0 67 | 0,0,0,1,0,1,0,1,0,0,0,0,0,44.8 68 | 0,0,0,0,1,0,0,0,0,0,0,0,0,0 69 | 0,1,0,0,0,0,0,0,0,0,0,0,0,13.2 70 | 0,0,0,0,0,0,0,0,0,0,0,0,0,16.1 71 | 0,0,0,0,0,1,0,0,0,0,0,0,0,4.7 72 | 1,0,0,0,0,0,0,0,0,0,0,0,1,49.4 73 | 1,0,0,0,0,0,0,0,0,0,0,0,1,25.9 74 | 0,1,0,0,0,0,0,0,0,0,0,0,0,0.3 75 | 0,0,0,1,0,1,0,0,0,0,0,0,0,22.8 76 | 0,0,0,0,0,0,0,0,0,0,0,0,0,0 77 | 1,0,0,0,1,0,0,1,0,0,0,1,0,32.8 78 | 1,0,0,0,0,0,0,0,0,0,0,0,0,52.9 79 | 1,0,0,0,0,0,0,0,0,0,0,0,0,31.3 80 | 1,0,0,0,0,0,0,0,0,0,0,0,0,49.1 81 | 1,0,0,0,0,0,0,0,0,0,0,0,0,11.6 82 | 1,0,0,0,0,1,0,0,0,0,0,0,0,19.3 83 | 1,0,0,0,0,0,0,0,0,0,0,0,0,32.8 84 | 1,0,0,0,0,0,0,0,0,0,0,0,0,42.2 85 | 1,0,0,1,0,0,0,0,0,0,0,0,0,43.6 86 | 1,0,0,1,0,0,0,0,0,0,0,0,0,51.4 87 | 0,0,0,1,0,0,0,0,0,0,0,0,0,31.8 88 | 1,0,0,0,0,0,0,0,0,0,0,0,0,46 89 | 1,0,0,0,0,0,0,0,0,0,0,0,0,51.2 90 | 1,0,0,1,0,0,0,0,0,0,0,0,0,54.2 91 | 1,0,0,1,0,0,0,0,0,0,0,0,0,43.7 92 | 1,0,0,1,0,0,0,0,0,0,0,0,0,44 93 | 1,0,0,0,0,0,0,0,0,0,0,0,0,34.2 94 | 0,1,0,1,0,1,0,0,0,0,0,0,0,45.7 95 | 0,0,0,1,0,1,0,0,1,0,0,0,0,0 96 | -------------------------------------------------------------------------------- /data and code/3rd/LRIF_title_23.csv: -------------------------------------------------------------------------------- 1 | ['With_1_C=O', 'With_1_CO'] 2 | ['With_1_C=O', 'With_1_CO', 'With_1_CNC', 'With_2_CCC', 'With_1_CCCNC', 'With_1_CCN', 'With_1_C[NH3+]'] 3 | ['With_1_C=O', 'With_1_CO', 'With_2_CCC', 'With_2_CCCNC', 'With_1_CC(C)(C)N', 'With_1_CCNCC'] 4 | ['With_1_C=O', 'With_1_CO', 'With_2_CN', 'With_1_CCCN', 'With_1_CCCCN'] 5 | ['With_1_C[O-]', 'With_1_ccn', 'With_1_cc(c)C', 'With_1_COC(c)=O'] 6 | ['With_1_cC(=O)[O-]', 'With_1_cc(c)C', 'With_1_cccc(C)n'] 7 | ['With_1_cC(=O)[O-]', 'With_1_ccn', 'With_1_cc(c)C', 'With_1_ccnc(c)C'] 8 | ['With_1_cc(c)C', 'With_1_cc(n)C(=O)[O-]'] 9 | ['With_1_cc(c)C', 'With_1_cccc(C)n'] 10 | ['With_1_cnc', 'With_1_cc(c)C', 'With_1_COC'] 11 | ['With_1_cnc', 'With_1_cc(c)C', 'With_1_cc(C)n', 'With_1_ccnc(c)C', 'With_1_COC(c)=O'] 12 | ['With_1_cnc', 'With_1_cc(c)C', 'With_1_cccc(C)n'] 13 | ['With_1_cnc', 'With_2_ccn', 'With_1_cc(c)C', 'With_1_ccc(cc)C(=O)[O-]'] 14 | ['With_2_CO', 'With_1_CC', 'With_2_CCO', 'With_1_CC(C)(C)C'] 15 | ['With_2_CO', 'With_1_CC(=O)O', 'With_1_CCO', 'With_1_CC(C)(C)C'] 16 | ['With_2_CO', 'With_1_CC(=O)O', 'With_1_CN', 'With_2_CCN', 'With_1_CC(=N)O'] 17 | ['With_2_CO', 'With_1_CCC', 'With_1_CCN'] 18 | ['With_2_CO', 'With_1_CCO', 'With_1_CC(C)O', 'With_1_C[NH3+]'] 19 | ['With_2_CO', 'With_1_CCO', 'With_1_C[NH3+]'] 20 | ['With_2_CO', 'With_1_CNC', 'With_2_CCO', 'With_2_CCN'] 21 | ['With_2_CO', 'With_2_CCO'] 22 | ['With_2_ccn', 'With_1_cc(c)C', 'With_1_ccc(cc)C(=O)[O-]'] 23 | ['With_4_CO', 'With_1_COC', 'With_1_CC(=O)O', 'With_1_CCO', 'With_2_CC(C)O'] 24 | -------------------------------------------------------------------------------- /data and code/3rd/Smiles_94.csv: -------------------------------------------------------------------------------- 1 | c1nc(C(=O)[O-])n[nH]1 2 | Cc1ncc[nH]1 3 | O=C(O)C1=CN=CC=C1 4 | c1cc(ccc1c1ccncc1)C(=O)O 5 | c1cc(C(=O)[O-])nc(c1)C(=O)[O-] 6 | CC(C)CC(C(O)=O)NC(OCC1=CC=CC=C1)=O 7 | NC1=NNC(N)=N1 8 | c1c(c(cc(c1O)C(=O)O)O)C(=O)O 9 | c1cc(c(cc1C(=O)O)O)C(=O)O 10 | OC1OCCC1 11 | CC([C@@H](C(=O)O)N)O 12 | CC(CO)(CO)C(=O)[O-] 13 | c1c(cc(cc1O)O)O.O.O 14 | OC1[C@@H](C(O)=O)NCC1 15 | C(C(=O)[O-])S 16 | c1cnccc1C(=O)[O-] 17 | c1c[nH]ccc1=S 18 | C([C@@H](C(=O)O)N)S 19 | C1(C2=NC=CC=C2)=NC=CC=C1 20 | c1ccc(cc1)c1ccccn1 21 | Cc1ccc(c(CO)c1CO)O 22 | c1ccnc(c1)c1cccc(c2ccccn2)n1.[Cl](=O)(=O)(=O)O.[Cl](=O)(=O)(=O)O 23 | c1ccc(c(c1)C(=O)O)O 24 | C(#CC(=O)[O-])C(=O)[O-] 25 | c1cc(cnc1)N 26 | C(CC(=O)O)CN 27 | c1ccc(c(c1)O)O 28 | C(C(=O)O)NC(CO)(CO)CO 29 | c1cc(c(cc1c1ccc(c(c1)N)N)N)N.Cl.Cl.Cl.Cl.O.O 30 | C1=NC2C(=N)N=CNC2=N1 31 | C(=C\\C(=O)O)/C(=O)O 32 | c1cc(cc(c1)C(=O)[O-])C(=O)[O-] 33 | C(C(C1C(=C(C(=O)O1)O)O)O)O 34 | CC(C)(CO)C(=O)O 35 | c1cc(c(cc1C(=O)[O-])N)C(=O)[O-] 36 | C1(C2=CC=NC=C2)=CC=NC=C1 37 | CC(=N)S 38 | [C@@H]1([C@@H]([C@H]([C@@H]([C@H]([C@H]1O)O)O)O)O)O 39 | CC(=NC(C=O)C(C(C(CO)O)O)O)O 40 | C1[C@@H](CN[C@H]1C(=O)O)O 41 | C1CNCCC1(C(=O)O)N 42 | OP(C1=CC=NC=C1)(O)=O 43 | C(=O)[C@@H]([C@H]([C@@H]([C@@H](CO)O)O)O)O 44 | C(C(CO)(CO)CO)O 45 | C(C(=O)[O-])S 46 | C[C@H](C(=O)[O-])O 47 | c1c[nH]ccc1=O 48 | c1ccc(cc1)CO 49 | c1cc(cc(c1)S(=O)(=O)[O-])P(c1cccc(c1)S(=O)(=O)[O-])c1cccc(c1)S(=O)(=O)[O-].[Na+].[Na+].[Na+] 50 | C1=CC2C(=NC(=O)C2(C=C1)O)O 51 | c1ccc(cc1)P(=O)(c1ccccc1)O 52 | COc1ccnc(c1)C(=O)OC 53 | COc1ccnc(c1)c1cc(ccn1)OC 54 | c1ccn(=O)c(c1)O 55 | C#CCCCCO 56 | c1cc(CO)oc1 57 | COC(=O)c1ccc(C(=O)[O-])nc1 58 | C[C@@H](CO)[NH3+] 59 | C(C(C(=O)O)O)(C(=O)O)O 60 | C(CCN)C[C@@H](C(=O)O)N 61 | C(C[C@@H](C(=O)O)[NH3+])CNC(=N)N 62 | c1cc(=O)oc2cc(c(cc12)O)O 63 | c1cc(c(C(=O)[O-])nc1)N 64 | c1cc(ccc1C[C@@H](C(=O)O)N)O 65 | c1cc2cc(ccc2nc1)C(=O)[O-] 66 | N[C@@H](CC1=CNC=N1)C(O)=O 67 | c1ccc2c(c1)cnc1c2cccn1.O 68 | Cc1cc(c(C)c(C)c1O)O 69 | CC1=CC(=O)C(=C(C)C1=O)C 70 | [C@@H]1([C@H]([C@@H](C(=O)[O-])O[C@H]1C(=O)[O-])C(=O)[O-])C(=O)[O-] 71 | C#CCO 72 | C#C[C@H](CC)O 73 | c1cc(ccc1I)O 74 | C(C(=NCC(=O)O)O)N 75 | c1ccc(cc1)[C@H]1CO1 76 | C([C@@H]1[C@H]([C@H]([C@H](n2cnc3c(N)ncnc23)O1)O)O)O.OP(=O)(O)OP(=O)(O)O 77 | C([C@H]([C@H]([C@@H]([C@H](CO)O)O)O)O)O 78 | C(=O)[C@@H]([C@H]([C@@H](CO)O)O)O 79 | C([C@@H]1[C@H]([C@@H](C(CO)(O)O1)O)O)O 80 | C([C@@H]1[C@H]([C@H]([C@H](O)O1)O)O)O 81 | CC(C)(C(=O)[O-])O 82 | C(C(F)(F)F)O 83 | CCC(O)(O)O 84 | C(CO)N(CCO)CCO 85 | C(CO)NCCO 86 | CC(=O)OC[C@@H]1[C@@H]([C@@H]([C@H](C(OCCOCCOCCN=[N+]=[N-])O1)OC(=O)C)OC(=O)C)OC(=O)C 87 | OC[C@H]1O[C@H]([C@@H]([C@H]([C@H]1O)O)O)O[C@H]2[C@@H]([C@H]([C@H](O[C@@H]2CO)O)O)O 88 | C(=O)[C@@H]([C@H]([C@@H]([C@@H](CO)O)O[C@@H]1[C@@H]([C@H]([C@@H]([C@@H](CO)O1)O)O)O)O)O.O 89 | C(CNC(CO)(CO)CO)CNC(CO)(CO)CO 90 | C([C@@H](CS(=O)(=O)O)O)NC(CO)(CO)CO 91 | C([C@H](CO)O)[NH3+] 92 | C=C.O.O 93 | O=C(O)[C@H](CC1=CC=CC=C1)NC([C@H](CCCCN)NC(CNC([C@H]2N(C([C@H](CC3=CC=C(O)C=C3)NC([C@H](C)N)=O)=O)CCC2)=O)=O)=O 94 | CC(C)(C)OC(=N[C@H](Cc1c[nH]c2ccccc12)C(=O)[O-])O 95 | -------------------------------------------------------------------------------- /data and code/3rd/Title_275.csv: -------------------------------------------------------------------------------- 1 | With_1_cn[nH] 2 | With_1_c[nH]n 3 | With_1_cnc 4 | With_2_cnc 5 | With_3_cnc 6 | With_1_C[O-] 7 | With_2_C[O-] 8 | With_4_C[O-] 9 | With_1_C=O 10 | With_2_C=O 11 | With_4_C=O 12 | With_6_C=O 13 | With_1_cC(=O)[O-] 14 | With_2_cC(=O)[O-] 15 | With_1_Cc 16 | With_3_Cc 17 | With_1_c[nH]c 18 | With_1_cc[nH] 19 | With_2_cc[nH] 20 | With_1_ccn 21 | With_2_ccn 22 | With_4_ccn 23 | With_1_ccc 24 | With_2_ccc 25 | With_3_ccc 26 | With_4_ccc 27 | With_5_ccc 28 | With_6_ccc 29 | With_8_ccc 30 | With_9_ccc 31 | With_10_ccc 32 | With_12_ccc 33 | With_1_cccc(c)C 34 | With_2_cccc(c)C 35 | With_4_cccc(c)C 36 | With_1_ccccn 37 | With_2_ccccn 38 | With_1_ccncc 39 | With_2_ccncc 40 | With_1_CO 41 | With_2_CO 42 | With_3_CO 43 | With_4_CO 44 | With_5_CO 45 | With_6_CO 46 | With_8_CO 47 | With_1_cc(c)C(=O)O 48 | With_2_cc(c)C(=O)O 49 | With_1_cccnc 50 | With_2_cccnc 51 | With_4_cccnc 52 | With_1_cncc(c)C 53 | With_1_cC(=O)O 54 | With_2_cC(=O)O 55 | With_1_cc(c)C 56 | With_2_cc(c)C 57 | With_3_cc(c)C 58 | With_1_ccc(cn)C(=O)O 59 | With_2_ccc(cc)-c(c)c 60 | With_1_ccc(cc)C(=O)O 61 | With_1_cc(c)-c 62 | With_2_cc(c)-c 63 | With_2_cc(-c)ccn 64 | With_4_cc(-c)ccn 65 | With_2_cccc(c)-c 66 | With_1_cccc(C)n 67 | With_2_cccc(C)n 68 | With_1_ccc(nc)C(=O)[O-] 69 | With_2_ccc(nc)C(=O)[O-] 70 | With_1_cc(n)C(=O)[O-] 71 | With_2_cc(n)C(=O)[O-] 72 | With_1_ccccc 73 | With_2_ccccc 74 | With_3_ccccc 75 | With_4_ccccc 76 | With_6_ccccc 77 | With_1_cc(C)n 78 | With_2_cc(C)n 79 | With_1_CNC 80 | With_2_CNC 81 | With_4_CNC 82 | With_1_CCC 83 | With_2_CCC 84 | With_3_CCC 85 | With_5_CCC 86 | With_1_COC 87 | With_2_COC 88 | With_3_COC 89 | With_8_COC 90 | With_1_CC(C)C 91 | With_2_CC(C)C 92 | With_1_cCO 93 | With_2_cCO 94 | With_1_ccc(cc)CO 95 | With_1_CC 96 | With_2_CC 97 | With_3_CC 98 | With_4_CC 99 | With_1_CC(=O)O 100 | With_2_CC(=O)O 101 | With_4_CC(=O)O 102 | With_1_CC(C)N 103 | With_5_CC(C)N 104 | With_1_cN 105 | With_2_cN 106 | With_4_cN 107 | With_1_cO 108 | With_2_cO 109 | With_3_cO 110 | With_1_ccc(C(=O)O)c(c)O 111 | With_2_ccc(C(=O)O)c(c)O 112 | With_1_ccc(O)c(c)C 113 | With_2_ccc(O)c(c)C 114 | With_1_cc(C)cc(c)O 115 | With_2_cc(C)cc(c)O 116 | With_1_cc(c)O 117 | With_2_cc(c)O 118 | With_3_cc(c)O 119 | With_1_CCOC(C)O 120 | With_1_CCO 121 | With_2_CCO 122 | With_3_CCO 123 | With_4_CCO 124 | With_6_CCO 125 | With_1_CC(O)O 126 | With_2_CC(O)O 127 | With_1_CCCCO 128 | With_1_CC(C)O 129 | With_2_CC(C)O 130 | With_3_CC(C)O 131 | With_5_CC(C)O 132 | With_6_CC(C)O 133 | With_1_CN 134 | With_2_CN 135 | With_1_C[C@H](N)C(=O)O 136 | With_1_CC(C)(C)C 137 | With_1_CC(C)(C)CO 138 | With_2_CC(C)(C)CO 139 | With_4_CC(C)(C)CO 140 | With_1_CC(=O)[O-] 141 | With_2_CC(=O)[O-] 142 | With_4_CC(=O)[O-] 143 | With_1_ccc(O)cc 144 | With_3_ccc(O)cc 145 | With_1_CCCNC 146 | With_2_CCCNC 147 | With_1_CCN 148 | With_2_CCN 149 | With_3_CCN 150 | With_1_CCNC(C)C 151 | With_1_CS 152 | With_1_O=C([O-])CS 153 | With_1_CCS 154 | With_1_ccc(cc)C(=O)[O-] 155 | With_2_ccc(cc)C(=O)[O-] 156 | With_1_cc(c)C(=O)[O-] 157 | With_2_cc(c)C(=O)[O-] 158 | With_1_cc[nH]cc 159 | With_2_ccc[nH]c 160 | With_1_cc(-c)n 161 | With_2_cc(-c)n 162 | With_4_cc(-c)n 163 | With_1_cccc(-c)n 164 | With_2_cccc(-c)n 165 | With_4_cccc(-c)n 166 | With_1_ccnc(c)-c 167 | With_2_ccnc(c)-c 168 | With_2_ccc(nc)-c(c)n 169 | With_4_ccc(nc)-c(c)n 170 | With_1_cc(c)CO 171 | With_2_cc(c)CO 172 | With_1_cccc(c)O 173 | With_2_cccc(c)O 174 | With_1_C#CC 175 | With_2_C#CC 176 | With_1_cccc(c)N 177 | With_2_cccc(c)N 178 | With_1_cc(c)N 179 | With_4_cc(c)N 180 | With_1_CCCN 181 | With_1_CCCCN 182 | With_2_CCCCN 183 | With_2_ccc(O)c(c)O 184 | With_1_CCNC(C)(C)C 185 | With_2_CCNC(C)(C)C 186 | With_3_CC(C)(N)CO 187 | With_6_CC(C)(N)CO 188 | With_1_NCC(=O)O 189 | With_1_CNC(CO)(CO)CO 190 | With_2_CNC(CO)(CO)CO 191 | With_1_CC(C)(C)N 192 | With_2_CC(C)(C)N 193 | With_1_C=NC 194 | With_3_C=NC 195 | With_1_C=N 196 | With_1_C=CC 197 | With_2_C=CC 198 | With_4_C=CC 199 | With_1_CC(O)C(O)CO 200 | With_1_CC(O)CO 201 | With_1_C[C@H](O)[C@@H](O)[C@@H](C)O 202 | With_2_C[C@H](O)[C@@H](O)[C@@H](C)O 203 | With_6_C[C@H](O)[C@@H](O)[C@@H](C)O 204 | With_1_CC=O 205 | With_1_CC(=N)O 206 | With_1_CCNCC 207 | With_1_cc(c)P 208 | With_2_cc(c)P 209 | With_3_cc(c)P 210 | With_1_OP 211 | With_2_OP 212 | With_4_OP 213 | With_1_O=P 214 | With_2_O=P 215 | With_1_C[C@H](O)CO 216 | With_2_C[C@H](O)CO 217 | With_1_C[C@@H](O)[C@H](O)[C@@H](C)O 218 | With_1_C[C@H](O)[C@H](O)CO 219 | With_1_C[C@@H](C)O 220 | With_2_C[C@@H](C)O 221 | With_3_C[C@@H](C)O 222 | With_4_C[C@@H](C)O 223 | With_1_C[C@H](O)[C@@H](O)C=O 224 | With_1_C[C@@H](O)C=O 225 | With_1_c=O 226 | With_3_cccc(c)P 227 | With_4_cccc(c)P 228 | With_2_O=S 229 | With_6_O=S 230 | With_1_CC(N)=O 231 | With_5_CC(N)=O 232 | With_1_CC(C)(C)O 233 | With_1_cc(c)OC 234 | With_2_cc(c)OC 235 | With_1_ccnc(c)C 236 | With_1_COC(c)=O 237 | With_1_ccc(cc)OC 238 | With_2_ccc(cc)OC 239 | With_1_COc 240 | With_2_COc 241 | With_1_cc(O)ccn 242 | With_2_cc(O)ccn 243 | With_1_CCCCC 244 | With_1_C#C 245 | With_1_coc 246 | With_1_C[NH3+] 247 | With_1_CC(C)[NH3+] 248 | With_1_CCC[C@@H](C)N 249 | With_2_CCC[C@@H](C)N 250 | With_1_cc(c)c 251 | With_3_cc(c)c 252 | With_1_cccc(c)c 253 | With_3_cccc(c)c 254 | With_1_cC[C@H](N)C(=O)O 255 | With_1_ccc(cc)CC 256 | With_2_ccc(cc)CC 257 | With_1_cc(c)C[C@@H](C)N 258 | With_2_cc(c)C[C@@H](C)N 259 | With_1_cc(c)n 260 | With_1_CCc 261 | With_2_CCc 262 | With_1_cc(n)n 263 | With_1_CO[C@H](CO)[C@H](C)O 264 | With_1_C[C@H](O)[C@H](O)[C@@H](C)O 265 | With_1_C[C@@H](O)CO 266 | With_2_C[C@@H](O)CO 267 | With_2_NCCO 268 | With_3_NCCO 269 | With_1_CN(C)C 270 | With_1_COC(OC)[C@@H](C)O 271 | With_1_CO[C@@H]([C@@H](C)O)[C@@H](C)O 272 | With_1_CO[C@H](CO)[C@@H](C)O 273 | With_1_C[C@H](O)[C@@H](O)C(O)O 274 | With_2_C[C@H](O)[C@@H](O)C(O)O 275 | With_1_C[C@H](C)O[C@@H](C)O 276 | -------------------------------------------------------------------------------- /data and code/3rd/Values_94.csv: -------------------------------------------------------------------------------- 1 | 0.0 2 | 0.0 3 | 0.0 4 | 0.0 5 | 1.0 6 | 0.0 7 | 0.0 8 | 1.0 9 | 1.0 10 | 0.0 11 | 1.0 12 | 1.0 13 | 0.0 14 | 1.0 15 | 1.0 16 | 1.0 17 | 0.0 18 | 0.0 19 | 0.0 20 | 0.0 21 | 1.0 22 | 0.0 23 | 0.0 24 | 1.0 25 | 0.0 26 | 0.0 27 | 0.0 28 | 1.0 29 | 0.0 30 | 0.0 31 | 0.0 32 | 1.0 33 | 1.0 34 | 1.0 35 | 0.0 36 | 0.0 37 | 0.0 38 | 0.0 39 | 1.0 40 | 0.0 41 | 1.0 42 | 1.0 43 | 1.0 44 | 1.0 45 | 1.0 46 | 0.0 47 | 1.0 48 | 0.0 49 | 1.0 50 | 1.0 51 | 0.0 52 | 1.0 53 | 0.0 54 | 1.0 55 | 1.0 56 | 1.0 57 | 1.0 58 | 1.0 59 | 1.0 60 | 1.0 61 | 1.0 62 | 1.0 63 | 0.0 64 | 0.0 65 | 0.0 66 | 1.0 67 | 0.0 68 | 0.0 69 | 0.0 70 | 0.0 71 | 1.0 72 | 1.0 73 | 0.0 74 | 1.0 75 | 0.0 76 | 1.0 77 | 1.0 78 | 0.0 79 | 1.0 80 | 0.0 81 | 0.0 82 | 1.0 83 | 1.0 84 | 1.0 85 | 1.0 86 | 1.0 87 | 1.0 88 | 1.0 89 | 1.0 90 | 1.0 91 | 1.0 92 | 1.0 93 | 1.0 94 | 0.0 95 | -------------------------------------------------------------------------------- /data and code/code/Classifier for LRIF results/DTC_LRIF.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import numpy as np\n", 10 | "import matplotlib.pyplot as plt\n", 11 | "import time\n", 12 | "c_time = time.strftime(\"%Y%m%d_%H%M%S\", time.localtime())\n", 13 | "c_time_m = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": null, 19 | "metadata": {}, 20 | "outputs": [], 21 | "source": [ 22 | "# 参数\n", 23 | "Version = 'V3.2'\n", 24 | "EPOCH = 1000\n", 25 | "INPUT_X = 'Features_94_343.csv'\n", 26 | "INPUT_Y = 'Values_94.csv'\n", 27 | "INPUT_TITLE = 'Title_343.csv'\n", 28 | "FIND_SPLIT = True\n", 29 | "INPUT_SPLIT = '100.0_Split.csv'\n", 30 | "SAVE_NAME = 'DTC_FeatureImportance_'+c_time+'.png'\n", 31 | "PLOT_NAME3 = 'DTC_ROC_'+c_time\n", 32 | "PLOT_NAME4 = 'DTC_CV_LOOP_'+c_time+'.png'\n", 33 | "SUPTITLE = 'DTC on '+INPUT_X+' and '+INPUT_Y+'\\nEPOCH:'+str(EPOCH)\n", 34 | "LOG_NAME = 'DTC_Log_'+c_time+'.txt'\n", 35 | "CV_LOOP_EPOCH = 300\n", 36 | "FOLD = 6\n", 37 | "TRAIN_TEST_SPLIT = 0.85\n", 38 | "INPUT_LRIF = 'LRIF_148_343.csv'\n", 39 | "INPUT_LRIF_LIST = 'LRIF_title_148.csv'\n", 40 | "LRIF_NAME = 'LRIF_Test_DTC_'+c_time+'.csv'" 41 | ] 42 | }, 43 | { 44 | "cell_type": "code", 45 | "execution_count": null, 46 | "metadata": {}, 47 | "outputs": [], 48 | "source": [ 49 | "X = np.loadtxt(INPUT_X, delimiter=',')\n", 50 | "y = np.loadtxt(INPUT_Y)\n", 51 | "title = np.loadtxt(INPUT_TITLE, dtype='str')\n", 52 | "print('X:', X.shape, ' y:', y.shape)\n", 53 | "lrif_test = np.loadtxt(INPUT_LRIF, delimiter=',')\n", 54 | "print(lrif_test.shape)\n", 55 | "lrif_list = np.loadtxt(INPUT_LRIF_LIST, dtype='str', delimiter='#').reshape(lrif_test.shape[0], 1)\n", 56 | "count_m = np.zeros((lrif_test.shape[0], 2))" 57 | ] 58 | }, 59 | { 60 | "cell_type": "code", 61 | "execution_count": null, 62 | "metadata": {}, 63 | "outputs": [], 64 | "source": [ 65 | "from sklearn.tree import DecisionTreeClassifier\n", 66 | "from sklearn import model_selection\n", 67 | "from sklearn.metrics import mean_squared_error\n", 68 | "from sklearn.model_selection import GridSearchCV\n", 69 | "from sklearn.model_selection import cross_val_score\n", 70 | "from sklearn import tree\n", 71 | "from sklearn.metrics import confusion_matrix\n", 72 | "import graphviz\n", 73 | "import joblib" 74 | ] 75 | }, 76 | { 77 | "cell_type": "code", 78 | "execution_count": null, 79 | "metadata": {}, 80 | "outputs": [], 81 | "source": [ 82 | "import os\n", 83 | "from pathlib import Path\n", 84 | "if FIND_SPLIT:\n", 85 | " DIR = 'DTC_'+Version+'_FindSplit_'+c_time\n", 86 | "else:\n", 87 | " DIR = 'DTC_'+Version+'_TestSplit_'+c_time\n", 88 | "os.mkdir(DIR)\n", 89 | "PLOT_NAME4 = Path('.', DIR, PLOT_NAME4)\n", 90 | "LRIF_NAME = Path('.', DIR, LRIF_NAME)" 91 | ] 92 | }, 93 | { 94 | "cell_type": "code", 95 | "execution_count": null, 96 | "metadata": {}, 97 | "outputs": [], 98 | "source": [ 99 | "# 打乱和切分数据集\n", 100 | "point = round(X.shape[0]*TRAIN_TEST_SPLIT)\n", 101 | "if not FIND_SPLIT:\n", 102 | " permutation = np.loadtxt(INPUT_SPLIT).astype(int).flatten().tolist()\n", 103 | " train_idx = []\n", 104 | " test_idx = []\n", 105 | " for i in range(X.shape[0]):\n", 106 | " if i in permutation:\n", 107 | " train_idx.append(i)\n", 108 | " else:\n", 109 | " test_idx.append(i)\n", 110 | " X_train = X[train_idx, :]\n", 111 | " y_train = y[train_idx]\n", 112 | " X_test = X[test_idx, :]\n", 113 | " y_test = y[test_idx]\n", 114 | " perm_train = np.random.permutation(X_train.shape[0])\n", 115 | " X_train = X_train[perm_train, :]\n", 116 | " y_train = y_train[perm_train]\n", 117 | " perm_test = np.random.permutation(X_test.shape[0])\n", 118 | " X_test = X_test[perm_test, :]\n", 119 | " y_test = y_test[perm_test]\n", 120 | "else:\n", 121 | " permutation = np.random.permutation(y.shape[0])\n", 122 | " X_train = X[permutation[:point], :]\n", 123 | " y_train = y[permutation[:point]]\n", 124 | " X_test = X[permutation[point:], :]\n", 125 | " y_test = y[permutation[point:]]" 126 | ] 127 | }, 128 | { 129 | "cell_type": "code", 130 | "execution_count": null, 131 | "metadata": {}, 132 | "outputs": [], 133 | "source": [ 134 | "def cm_plot(y, yp, path): \n", 135 | " cm = confusion_matrix(y, yp) #混淆矩阵\n", 136 | " plt.figure(figsize=(5,5), dpi=300)\n", 137 | " plt.matshow(cm, cmap=plt.cm.Greens) #画混淆矩阵图，配色风格使用cm.Greens，更多风格请参考官网。\n", 138 | " plt.colorbar() #颜色标签 \n", 139 | " for x in range(len(cm)): #数据标签\n", 140 | " for y in range(len(cm)):\n", 141 | " plt.annotate(cm[x,y], xy=(x, y), horizontalalignment='center', verticalalignment='center')\n", 142 | " plt.ylabel('True label', fontsize=15) #坐标轴标签\n", 143 | " plt.xlabel('Predicted label', fontsize=15) #坐标轴标签\n", 144 | " plt.savefig(path, bbox_inches='tight', dpi=300)\n", 145 | " plt.clf()\n", 146 | " plt.close('all')" 147 | ] 148 | }, 149 | { 150 | "cell_type": "code", 151 | "execution_count": null, 152 | "metadata": {}, 153 | "outputs": [], 154 | "source": [ 155 | "# 搭建随机森林模型\n", 156 | "tuned_parameters = [{'min_impurity_decrease': [0.01], 'max_depth': [4, 5, None], \n", 157 | " 'max_features': [0.95], 'max_leaf_nodes': [4, 5, None], 'class_weight': ['balanced'], \n", 158 | " 'min_samples_leaf': [2, 3], 'min_samples_split': [1, 2, 3]}]\n", 159 | "dtc = DecisionTreeClassifier()\n", 160 | "clf = GridSearchCV(dtc, tuned_parameters, verbose=1, scoring=None, cv=6, n_jobs=-1)\n", 161 | "clf.fit(X, y)\n", 162 | "clf_new = clf.best_estimator_\n", 163 | "best_p = clf.best_params_\n", 164 | "paras = clf_new.get_params()\n", 165 | "print(best_p)\n", 166 | "acc_unique = clf_new.score(X_test, y_test)\n", 167 | "print('Current accuracy:', acc_unique)" 168 | ] 169 | }, 170 | { 171 | "cell_type": "code", 172 | "execution_count": null, 173 | "metadata": { 174 | "scrolled": true 175 | }, 176 | "outputs": [], 177 | "source": [ 178 | "f_i = np.zeros((title.shape[0], 1))\n", 179 | "mse_list = []\n", 180 | "acc_list = []\n", 181 | "mean_acc_list = []\n", 182 | "min_mse = 999.9\n", 183 | "for _ in range(EPOCH):\n", 184 | " # 打乱和切分数据集\n", 185 | " if FIND_SPLIT:\n", 186 | " permutation = np.random.permutation(y.shape[0])\n", 187 | " X_train = X[permutation[:point], :]\n", 188 | " y_train = y[permutation[:point]]\n", 189 | " X_test = X[permutation[point:], :]\n", 190 | " y_test = y[permutation[point:]]\n", 191 | " else:\n", 192 | " perm_train = np.random.permutation(X_train.shape[0])\n", 193 | " X_train = X_train[perm_train, :]\n", 194 | " y_train = y_train[perm_train]\n", 195 | " perm_test = np.random.permutation(X_test.shape[0])\n", 196 | " X_test = X_test[perm_test, :]\n", 197 | " y_test = y_test[perm_test]\n", 198 | " # print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)\n", 199 | " clf_new = DecisionTreeClassifier()\n", 200 | " for k, v in paras.items():\n", 201 | " clf_new.set_params(**{k: v})\n", 202 | " # 拟合模型\n", 203 | " clf_new.fit(X_train, y_train)\n", 204 | " # 计算损失\n", 205 | " y_pred = clf_new.predict(X_test)\n", 206 | " lrif_predict = clf_new.predict(lrif_test)\n", 207 | " for i in range(lrif_test.shape[0]):\n", 208 | " if lrif_predict[i]==1:\n", 209 | " count_m[i, 0] += 1\n", 210 | " else:\n", 211 | " count_m[i, 1] += 1\n", 212 | " acc_count = 0\n", 213 | " for i in range(X_test.shape[0]):\n", 214 | " if y_pred[i]==y_test[i]:\n", 215 | " acc_count += 1\n", 216 | " acc = acc_count*100/X_test.shape[0]\n", 217 | " mse = mean_squared_error(y_test, y_pred)\n", 218 | " mse_list.append(mse)\n", 219 | " acc_list.append(acc)\n", 220 | " mean_acc = np.mean(acc_list)\n", 221 | " mean_acc_list.append(mean_acc)\n", 222 | " print('Round:', _+1, \"MSE: %.4f\" % mse, ' Accuracy: %.4f' % acc, ' current mean acc: %.4f' % mean_acc)\n", 223 | " # 计算特征重要度\n", 224 | " feature_importance = np.array(clf_new.feature_importances_).reshape(title.shape[0], 1)\n", 225 | " f_i = f_i+feature_importance*acc\n", 226 | " if mse=EXACT_PIECE_RESERVE_THRESHOLD):\n", 254 | " continue\n", 255 | " delete.append(i)\n", 256 | "data = np.delete(data, delete, axis=1)\n", 257 | "title = np.array(title).reshape(1, len(title))\n", 258 | "title = np.delete(title, delete, axis=1)\n", 259 | "title = title.flatten().tolist()\n", 260 | "print('After delete sparse coloumns: length of title:', len(title))\n", 261 | "print('After delete sparse coloumns: shape of data:', data.shape)" 262 | ] 263 | }, 264 | { 265 | "cell_type": "code", 266 | "execution_count": 27, 267 | "metadata": {}, 268 | "outputs": [ 269 | { 270 | "name": "stdout", 271 | "output_type": "stream", 272 | "text": [ 273 | "After atom-count control: length of title: 163\n", 274 | "After atom-count control: shape of data: (94, 163)\n" 275 | ] 276 | } 277 | ], 278 | "source": [ 279 | "# 原子数量调控模块：\n", 280 | "if ATOM_COUNT_CONTROL:\n", 281 | " delete = []\n", 282 | " for i in range(data.shape[1]):\n", 283 | " flag = False\n", 284 | " for patt_temp in ATOM_COUNT_CONTROL_OMIT_PATTERN:\n", 285 | " patt = Chem.MolFromSmarts(patt_temp)\n", 286 | " flag = m.HasSubstructMatch(patt)\n", 287 | " if flag:\n", 288 | " break\n", 289 | " if flag:\n", 290 | " continue\n", 291 | " m = Chem.MolFromSmarts(title[i])\n", 292 | " atom_count = len(m.GetAtoms())\n", 293 | " if atom_countSMARTS_MAX_LENGTH:\n", 294 | " delete.append(i)\n", 295 | " data = np.delete(data, delete, axis=1)\n", 296 | " title = np.array(title).reshape(1, len(title))\n", 297 | " title = np.delete(title, delete, axis=1)\n", 298 | " title = title.flatten().tolist()\n", 299 | " print('After atom-count control: length of title:', len(title))\n", 300 | " print('After atom-count control: shape of data:', data.shape)" 301 | ] 302 | }, 303 | { 304 | "cell_type": "code", 305 | "execution_count": 28, 306 | "metadata": {}, 307 | "outputs": [], 308 | "source": [ 309 | "# 这一块里，会删除某一指定的特征列，将其转化为表示含有0,1,...,n个基团的布尔表示列，其中n为所有样本中含基团的最大值，最后会自动把空列删除\n", 310 | "if TURN_TO_BOOL:\n", 311 | " if ALL_TURN_TO_BOOL:\n", 312 | " BOOL_PATT_LIST = title\n", 313 | " for bool_patt in BOOL_PATT_LIST:\n", 314 | " print('Processing feature: ', bool_patt)\n", 315 | " print('title length(before turn to bool):', len(title))\n", 316 | " print('data shape(before turn to bool):', data.shape)\n", 317 | " index_t = title.index(bool_patt)\n", 318 | " print('Target conlumn index:', index_t)\n", 319 | " feature_max = int(max(data[:, index_t]))\n", 320 | " print('max number of target patt:', feature_max)\n", 321 | " # 制作新的bool数据数组\n", 322 | " if BOOL_TURN_MODE=='a':\n", 323 | " matrix_bool = np.zeros((data.shape[0], feature_max))\n", 324 | " else:\n", 325 | " matrix_bool = np.zeros((data.shape[0], feature_max+1))\n", 326 | " for i in range(data.shape[0]):\n", 327 | " if BOOL_TURN_MODE=='a':\n", 328 | " if data[i, index_t]>=1:\n", 329 | " matrix_bool[i, int(data[i, index_t]-1)] = 1\n", 330 | " else:\n", 331 | " matrix_bool[i, int(data[i, index_t])] = 1\n", 332 | " # 删除对应的数据列和标题列\n", 333 | " data = np.delete(data, [index_t], axis=1)\n", 334 | " title = np.array(title).reshape(1, len(title))\n", 335 | " title = np.delete(title, [index_t], axis=1)\n", 336 | " title = title.flatten().tolist()\n", 337 | " # 制作额外的标题\n", 338 | " new_title = []\n", 339 | " if BOOL_TURN_MODE=='a':\n", 340 | " for i in range(feature_max):\n", 341 | " new_title.append('With_'+str(i+1)+'_'+bool_patt)\n", 342 | " else:\n", 343 | " for i in range(feature_max+1):\n", 344 | " new_title.append('With_'+str(i)+'_'+bool_patt)\n", 345 | " data = np.hstack((data, matrix_bool))\n", 346 | " title = title+new_title\n", 347 | " print('title length(after turn to bool):', len(title))\n", 348 | " print('data shape(after turn to bool):', data.shape, '\\n')\n", 349 | " # 将空列自动删除，V5.1加入\n", 350 | " delete = []\n", 351 | " for i in range(data.shape[1]):\n", 352 | " if sum(data[:, i])==0:\n", 353 | " delete.append(i)\n", 354 | " print(data.shape)\n", 355 | " print(len(title))\n", 356 | " data = np.delete(data, delete, axis=1)\n", 357 | " title = np.array(title).reshape(1, len(title))\n", 358 | " title = np.delete(title, delete, axis=1)\n", 359 | " title = title.flatten().tolist()\n", 360 | " print(data.shape)\n", 361 | " print(len(title))" 362 | ] 363 | }, 364 | { 365 | "cell_type": "code", 366 | "execution_count": 29, 367 | "metadata": {}, 368 | "outputs": [ 369 | { 370 | "name": "stdout", 371 | "output_type": "stream", 372 | "text": [ 373 | "(94, 163)\n", 374 | "163\n" 375 | ] 376 | } 377 | ], 378 | "source": [ 379 | "# 数据集的整理\n", 380 | "print(data.shape)\n", 381 | "print(len(title))\n", 382 | "X = data\n", 383 | "y = dataset_sparse.get_shard(0)[1]\n", 384 | "title = np.array(title).reshape(X.shape[1], )\n", 385 | "smiles_out = dataset_sparse.get_shard(0)[3]" 386 | ] 387 | }, 388 | { 389 | "cell_type": "code", 390 | "execution_count": 30, 391 | "metadata": { 392 | "scrolled": true 393 | }, 394 | "outputs": [], 395 | "source": [ 396 | "# 如果需要包含描述符，这会在这里进行计算和矩阵的合并\n", 397 | "if INCLUDE_DESC:\n", 398 | " from rdkit.Chem import Descriptors\n", 399 | " loader_desc = dc.data.CSVLoader(\n", 400 | " tasks=[dataset.columns.values[1]], smiles_field=dataset.columns.values[0],\n", 401 | " featurizer=featurizer_desc)\n", 402 | " dataset_desc = loader_desc.featurize(dataset_file)\n", 403 | " print('Desc:')\n", 404 | " Desc_list = dc.feat.RDKitDescriptors.allowedDescriptors\n", 405 | " print(dataset_desc.get_shard(0)[0].shape)\n", 406 | " allow = []\n", 407 | " for i in Descriptors.descList:\n", 408 | " if i[0] in Desc_list:\n", 409 | " allow.append(i[0])\n", 410 | " print(len(allow))\n", 411 | " X_desc = dataset_desc.get_shard(0)[0]\n", 412 | " Desc_list = np.array(allow).reshape(1, len(allow))\n", 413 | " delete = []\n", 414 | " for i in range(X_desc.shape[1]):\n", 415 | " if max(X_desc[:, i])==min(X_desc[:, i]):\n", 416 | " delete.append(i)\n", 417 | " X_desc = np.delete(X_desc, delete, axis=1)\n", 418 | " Desc_list = np.delete(Desc_list, delete, axis=1).flatten().tolist()\n", 419 | " X = np.hstack((X, X_desc))\n", 420 | " title = title.flatten().tolist()\n", 421 | " title = title+Desc_list\n", 422 | " title = np.array(title).reshape(len(title), )" 423 | ] 424 | }, 425 | { 426 | "cell_type": "code", 427 | "execution_count": 31, 428 | "metadata": {}, 429 | "outputs": [], 430 | "source": [ 431 | "# 创建一个目录来保存生成的数据包\n", 432 | "import os\n", 433 | "import time\n", 434 | "from pathlib import Path\n", 435 | "if INCLUDE_DESC:\n", 436 | " DIR = time.strftime(\"%Y%m%d_%H%M%S\", time.localtime())+'_'+str(X.shape[0])+'_'+str(data.shape[1])+'+'+str(X_desc.shape[1])\n", 437 | "else:\n", 438 | " DIR = time.strftime(\"%Y%m%d_%H%M%S\", time.localtime())+'_'+str(X.shape[0])+'_'+str(X.shape[1])\n", 439 | "os.mkdir(DIR)" 440 | ] 441 | }, 442 | { 443 | "cell_type": "code", 444 | "execution_count": 32, 445 | "metadata": {}, 446 | "outputs": [ 447 | { 448 | "name": "stdout", 449 | "output_type": "stream", 450 | "text": [ 451 | "(163,) (94, 163)\n" 452 | ] 453 | } 454 | ], 455 | "source": [ 456 | "print(title.shape, X.shape)\n", 457 | "# 保存一个带Title但是不带标签值的数据包\n", 458 | "out = np.vstack((title, X))\n", 459 | "if INCLUDE_DESC:\n", 460 | " OUT_NAME0 = 'Data_'+str(data.shape[0])+'_'+str(data.shape[1])+'+'+str(X_desc.shape[1])+'_withTitle_Nolabel.csv'\n", 461 | "else:\n", 462 | " OUT_NAME0 = 'Data_'+str(data.shape[0])+'_'+str(data.shape[1])+'_withTitle_Nolabel.csv'\n", 463 | "dir_out0 = Path('.', DIR, OUT_NAME0)\n", 464 | "np.savetxt(dir_out0, out, fmt='%s', delimiter=',')" 465 | ] 466 | }, 467 | { 468 | "cell_type": "code", 469 | "execution_count": 33, 470 | "metadata": {}, 471 | "outputs": [ 472 | { 473 | "name": "stdout", 474 | "output_type": "stream", 475 | "text": [ 476 | "X: (94, 163) y: (94, 1) title: (163,) smiles_list: (94,)\n" 477 | ] 478 | } 479 | ], 480 | "source": [ 481 | "# 输出其它数据包\n", 482 | "print('X: ', X.shape, ' y: ', y.shape, ' title:', title.shape, ' smiles_list:', smiles_out.shape)\n", 483 | "if INCLUDE_DESC:\n", 484 | " OUT_NAME1 = 'Features_'+str(data.shape[0])+'_'+str(data.shape[1])+'+'+str(X_desc.shape[1])+'.csv'\n", 485 | "else:\n", 486 | " OUT_NAME1 = 'Features_'+str(data.shape[0])+'_'+str(data.shape[1])+'.csv'\n", 487 | "dir_out1 = Path('.', DIR, OUT_NAME1)\n", 488 | "OUT_NAME2 = 'Values_'+str(data.shape[0])+'.csv'\n", 489 | "dir_out2 = Path('.', DIR, OUT_NAME2)\n", 490 | "if INCLUDE_DESC:\n", 491 | " OUT_NAME3 = 'Title_'+str(data.shape[1])+'+'+str(X_desc.shape[1])+'.csv'\n", 492 | "else:\n", 493 | " OUT_NAME3 = 'Title_'+str(data.shape[1])+'.csv'\n", 494 | "dir_out3 = Path('.', DIR, OUT_NAME3)\n", 495 | "OUT_NAME4 = 'SMILES_'+str(data.shape[0])+'.csv'\n", 496 | "dir_out4 = Path('.', DIR, OUT_NAME4)\n", 497 | "np.savetxt(dir_out1, X, fmt='%s', delimiter=',')\n", 498 | "np.savetxt(dir_out2, y, fmt='%s')\n", 499 | "np.savetxt(dir_out3, title, fmt='%s')\n", 500 | "np.savetxt(dir_out4, smiles_out, fmt='%s')" 501 | ] 502 | }, 503 | { 504 | "cell_type": "code", 505 | "execution_count": 34, 506 | "metadata": {}, 507 | "outputs": [ 508 | { 509 | "name": "stdout", 510 | "output_type": "stream", 511 | "text": [ 512 | "(94, 163) (94, 1) (164,)\n", 513 | "(95, 164)\n" 514 | ] 515 | } 516 | ], 517 | "source": [ 518 | "# 输出一个带Title和Label的无SMILES完整数据集\n", 519 | "title = title.flatten().tolist()\n", 520 | "title = title+['value']\n", 521 | "title = np.array(title).reshape(len(title), )\n", 522 | "print(X.shape, y.shape, title.shape)\n", 523 | "out = np.vstack((title, np.hstack((X, y))))\n", 524 | "print(out.shape)\n", 525 | "if INCLUDE_DESC:\n", 526 | " OUT_NAME5 = 'Data_'+str(data.shape[0])+'_'+str(data.shape[1])+'+'+str(X_desc.shape[1])+'_withTitle_withLabel.csv'\n", 527 | "else:\n", 528 | " OUT_NAME5 = 'Data_'+str(data.shape[0])+'_'+str(data.shape[1])+'_withTitle_withLabel.csv'\n", 529 | "dir_out5 = Path('.', DIR, OUT_NAME5)\n", 530 | "np.savetxt(dir_out5, out, fmt='%s', delimiter=',')" 531 | ] 532 | } 533 | ], 534 | "metadata": { 535 | "kernelspec": { 536 | "display_name": "PythonForDYH", 537 | "language": "python", 538 | "name": "dyhpy" 539 | }, 540 | "language_info": { 541 | "codemirror_mode": { 542 | "name": "ipython", 543 | "version": 3 544 | }, 545 | "file_extension": ".py", 546 | "mimetype": "text/x-python", 547 | "name": "python", 548 | "nbconvert_exporter": "python", 549 | "pygments_lexer": "ipython3", 550 | "version": "3.7.6" 551 | } 552 | }, 553 | "nbformat": 4, 554 | "nbformat_minor": 2 555 | } 556 | -------------------------------------------------------------------------------- /data and code/code/LRIF/LRIF.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# LRIF(Loop Random Intersection Forest) V4.2 " 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": null, 13 | "metadata": {}, 14 | "outputs": [], 15 | "source": [ 16 | "import time\n", 17 | "# 参数\n", 18 | "VERSION = '4.2'\n", 19 | "X_INPUT_NAME = 'Features_94_343.csv'\n", 20 | "Y_INPUT_NAME = 'Values_94.csv'\n", 21 | "TITLE_INPUT_NAME = 'Title_343.csv'\n", 22 | "RECEIVE_THRESHOLD = 12\n", 23 | "TREE_NUM = 20000\n", 24 | "BRANCH_NUM = 3\n", 25 | "MAX_LEVEL = 10\n", 26 | "RATIO_1 = 0.90\n", 27 | "RATIO_2 = 0.20\n", 28 | "RATIO_3 = 0.20\n", 29 | "RATIO_4 = 0.80\n", 30 | "RATIO_5 = 0.00\n", 31 | "INDEX_FIT = True\n", 32 | "WEIGHT_WAY = 4\n", 33 | "SUPTITLE = 'Importance of Features from LRIF V'+VERSION+'\\nDate: '+time.strftime(\"%Y%m%d_%H%M%S\", time.localtime())\n", 34 | "DIR_NAME = 'LRIF_'+time.strftime(\"%Y%m%d_%H%M%S\", time.localtime())+'--Version '+VERSION+'--'\n", 35 | "PLOT_NAME = 'LRIF_V'+VERSION+'_'+time.strftime(\"%Y%m%d_%H%M%S\", time.localtime())+'.png'\n", 36 | "LOG_NAME = 'Log_LRIF_V'+VERSION+'_'+time.strftime(\"%Y%m%d_%H%M%S\", time.localtime())+'.txt'\n", 37 | "DISPLAY_NUM = 30\n", 38 | "REMAIN_NUM = 1200" 39 | ] 40 | }, 41 | { 42 | "cell_type": "code", 43 | "execution_count": null, 44 | "metadata": {}, 45 | "outputs": [], 46 | "source": [ 47 | "import numpy as np\n", 48 | "import matplotlib.pyplot as plt" 49 | ] 50 | }, 51 | { 52 | "cell_type": "code", 53 | "execution_count": null, 54 | "metadata": {}, 55 | "outputs": [], 56 | "source": [ 57 | "X = np.loadtxt(X_INPUT_NAME, delimiter=',').astype(int)\n", 58 | "y = np.loadtxt(Y_INPUT_NAME).astype(int)\n", 59 | "title = np.loadtxt(TITLE_INPUT_NAME, dtype=str)\n", 60 | "print(X.shape, y.shape, title.shape)" 61 | ] 62 | }, 63 | { 64 | "cell_type": "code", 65 | "execution_count": null, 66 | "metadata": {}, 67 | "outputs": [], 68 | "source": [ 69 | "def intersection(x1, x2, index, flag):\n", 70 | " global RATIO_1, RATIO_2, RATIO_3, RATIO_4, RATIO_5\n", 71 | " new_index = np.zeros((x1.shape[0], ))\n", 72 | " for i in range(x1.shape[0]):\n", 73 | " if index[i, ] == 1:\n", 74 | " if flag:\n", 75 | " if x1[i, ] == x2[i, ]:\n", 76 | " if np.random.rand() <= RATIO_1:\n", 77 | " new_index[i, ] = 1\n", 78 | " else:\n", 79 | " if x1[i, ] == 1:\n", 80 | " if np.random.rand() <= RATIO_2:\n", 81 | " new_index[i, ] = 1\n", 82 | " else:\n", 83 | " if np.random.rand() <= RATIO_3:\n", 84 | " new_index[i, ] = 1\n", 85 | " else:\n", 86 | " if x1[i, ] == x2[i, ]:\n", 87 | " if np.random.rand() >= RATIO_4:\n", 88 | " new_index[i, ] = 1\n", 89 | " else:\n", 90 | " new_index[i, ] = 1\n", 91 | " else:\n", 92 | " if flag:\n", 93 | " if x2[i, ] == x1[i, ]:\n", 94 | " if np.random.rand() <= RATIO_5:\n", 95 | " new_index[i, ] = 1\n", 96 | " else:\n", 97 | " if x2[i, ] != x1[i, ]:\n", 98 | " if np.random.rand() <= RATIO_5:\n", 99 | " new_index[i, ] = 1\n", 100 | " return new_index" 101 | ] 102 | }, 103 | { 104 | "cell_type": "code", 105 | "execution_count": null, 106 | "metadata": { 107 | "scrolled": true 108 | }, 109 | "outputs": [], 110 | "source": [ 111 | "Out = []\n", 112 | "X_label_1 = X[np.argwhere(y==1).flatten().tolist(), :]\n", 113 | "X_label_0 = X[np.argwhere(y==0).flatten().tolist(), :]\n", 114 | "leaf_sum = []\n", 115 | "level_sum = []\n", 116 | "for _ in range(TREE_NUM):\n", 117 | " leaf_count = 0\n", 118 | " none_leaf_count = 0\n", 119 | " node_count = 1\n", 120 | " X_1 = X_label_1[np.random.randint(0, X_label_1.shape[0]), :]\n", 121 | " index_list = []\n", 122 | " for level in range(MAX_LEVEL):\n", 123 | " next_node_count = 0\n", 124 | " if node_count == 0:\n", 125 | " print('One tree done! Total leaf:', leaf_count, ',Total none leaf:', none_leaf_count, ',Total level:', level+1, \n", 126 | " '\\nTotal:', _+1, 'tree(s) has(have) finished their mission.\\n')\n", 127 | " break\n", 128 | " next_index_list = []\n", 129 | " if level == 0:\n", 130 | " if INDEX_FIT:\n", 131 | " index_list.append(X_1)\n", 132 | " else:\n", 133 | " index_list.append(np.ones((X_1.shape[0], )))\n", 134 | " for node in range(node_count):\n", 135 | " k = np.random.randint(0, X.shape[0])\n", 136 | " X_2 = X[k, :]\n", 137 | " f = True if y[k, ] == 1 else False\n", 138 | " node_out = intersection(X_1, X_2, index_list[node], f)\n", 139 | " if (sum(node_out>0) <= RECEIVE_THRESHOLD):\n", 140 | " if sum(node_out) > 0:\n", 141 | " Out.append(node_out)\n", 142 | " leaf_count += 1\n", 143 | " else:\n", 144 | " next_node_count += BRANCH_NUM\n", 145 | " none_leaf_count += 1\n", 146 | " for i in range(BRANCH_NUM):\n", 147 | " next_index_list.append(node_out)\n", 148 | " node_count = next_node_count\n", 149 | " index_list = next_index_list\n", 150 | " print('One tree done! Total leaf:', leaf_count, ',Total none leaf:', none_leaf_count, ',Total level:', level+1, \n", 151 | " '\\nTotal:', _+1, 'tree(s) has(have) finished their mission.\\n')\n", 152 | " leaf_sum.append(leaf_count)\n", 153 | " level_sum.append(level+1)" 154 | ] 155 | }, 156 | { 157 | "cell_type": "code", 158 | "execution_count": null, 159 | "metadata": { 160 | "scrolled": true 161 | }, 162 | "outputs": [], 163 | "source": [ 164 | "print('Total number of effective leaf output:', len(Out))\n", 165 | "count = np.zeros((X.shape[1], ))\n", 166 | "for out in Out:\n", 167 | " for i in range(len(out)):\n", 168 | " if out[i]>0:\n", 169 | " count[i, ] += 1.0" 170 | ] 171 | }, 172 | { 173 | "cell_type": "code", 174 | "execution_count": null, 175 | "metadata": {}, 176 | "outputs": [], 177 | "source": [ 178 | "count = count.astype(float)\n", 179 | "for i in range(count.shape[0]):\n", 180 | " if WEIGHT_WAY==0:\n", 181 | " temp = 1\n", 182 | " elif WEIGHT_WAY==1:\n", 183 | " temp = sum(X[:, i])\n", 184 | " elif WEIGHT_WAY==2:\n", 185 | " if sum(X[:, i])>1:\n", 186 | " temp = 0.5*sum(X[:, i])*(sum(X[:, i])-1)\n", 187 | " else:\n", 188 | " temp = 1\n", 189 | " elif WEIGHT_WAY==3:\n", 190 | " if sum(X_label_1[:, i])>0:\n", 191 | " temp = sum(X[:, i])*sum(X_label_1[:, i])\n", 192 | " else:\n", 193 | " temp = sum(X[:, i])\n", 194 | " elif WEIGHT_WAY == 4:\n", 195 | " temp_list = []\n", 196 | " temp_list.append((sum(X_label_1[:, i])**2+(X_label_1.shape[0]-sum(X_label_1[:, i]))**2)*RATIO_1)\n", 197 | " temp_list.append((X_label_1.shape[0]-sum(X_label_1[:, i]))*sum(X_label_1[:, i])*RATIO_2)\n", 198 | " temp_list.append(sum(X_label_1[:, i])*(X_label_1.shape[0]-sum(X_label_1[:, i]))*RATIO_3)\n", 199 | " temp_list.append((sum(X_label_1[:, i])**2+(X_label_1.shape[0]-sum(X_label_1[:, i]))**2)*RATIO_5)\n", 200 | " temp_list.append((sum(X_label_0[:, i])*sum(X_label_1[:, i])+(X_label_0.shape[0]-sum(X_label_0[:, i]))*(X_label_1.shape[0]-sum(X_label_1[:, i])))*RATIO_4)\n", 201 | " temp_list.append(sum(X_label_1[:, i])*(X_label_0.shape[0]-sum(X_label_0[:, i]))+sum(X_label_0[:, i])*(X_label_1.shape[0]-sum(X_label_1[:, i])))\n", 202 | " temp_list.append((sum(X_label_0[:, i])*(X_label_1.shape[0]-sum(X_label_1[:, i]))+(X_label_0.shape[0]-sum(X_label_0[:, i]))*sum(X_label_1[:, i]))*RATIO_5)\n", 203 | " temp = sum(temp_list)\n", 204 | " count[i, ] = count[i, ]*X.shape[0]*X_label_1.shape[0]/temp" 205 | ] 206 | }, 207 | { 208 | "cell_type": "code", 209 | "execution_count": null, 210 | "metadata": {}, 211 | "outputs": [], 212 | "source": [ 213 | "p = np.argsort(-count)\n", 214 | "score = []\n", 215 | "score_count = []\n", 216 | "sum_temp = sum(count)\n", 217 | "for i in range(len(p)):\n", 218 | " score_ratio = 100*count[p[i], ]/sum_temp\n", 219 | " score.append(score_ratio)\n", 220 | " score_count.append(sum(score))" 221 | ] 222 | }, 223 | { 224 | "cell_type": "code", 225 | "execution_count": null, 226 | "metadata": {}, 227 | "outputs": [], 228 | "source": [ 229 | "import os\n", 230 | "from pathlib import Path\n", 231 | "DIR = DIR_NAME\n", 232 | "os.mkdir(DIR)\n", 233 | "PLOT_NAME = Path('.', DIR, PLOT_NAME)\n", 234 | "LOG_NAME = Path('.', DIR, LOG_NAME)" 235 | ] 236 | }, 237 | { 238 | "cell_type": "code", 239 | "execution_count": null, 240 | "metadata": { 241 | "scrolled": false 242 | }, 243 | "outputs": [], 244 | "source": [ 245 | "plt.figure(figsize=(22,14), dpi=200)\n", 246 | "plt.subplot(211)\n", 247 | "plt.bar(np.linspace(1, len(p), len(p)), count[:, ])\n", 248 | "plt.title('Appearance score of features', fontsize=17)\n", 249 | "plt.ylabel('Appearance score of features')\n", 250 | "plt.xlabel('Index')\n", 251 | "plt.subplot(212)\n", 252 | "plt.tick_params(axis='x', labelsize=5)\n", 253 | "plt.bar(title[p[:DISPLAY_NUM, ], ].flatten().tolist(), count[p[:DISPLAY_NUM, ], ].flatten().tolist())\n", 254 | "plt.title(str(DISPLAY_NUM)+' Max Coef', fontsize=17)\n", 255 | "plt.ylabel('Appearance score of features')\n", 256 | "plt.xlabel('Feature Name')\n", 257 | "plt.suptitle(SUPTITLE, fontsize=20)\n", 258 | "plt.savefig(PLOT_NAME, bbox_inches='tight')" 259 | ] 260 | }, 261 | { 262 | "cell_type": "code", 263 | "execution_count": null, 264 | "metadata": {}, 265 | "outputs": [], 266 | "source": [ 267 | "f1 = open(LOG_NAME, 'w+')\n", 268 | "f1.write('Output of Loop Random Intersection Forest:\\n\\n')\n", 269 | "f1.write('Data: '+X_INPUT_NAME+' '+Y_INPUT_NAME+' '+TITLE_INPUT_NAME+'\\n')\n", 270 | "f1.write('Data Shape: '+str(X.shape)+', '+str(y.shape)+'\\n\\n')\n", 271 | "f1.write('Threshold: '+str(RECEIVE_THRESHOLD)+'\\n')\n", 272 | "f1.write('Tree Num: '+str(TREE_NUM)+'\\n')\n", 273 | "f1.write('Node Num(new branch, per node): '+str(BRANCH_NUM)+'\\n')\n", 274 | "f1.write('Max level: '+str(MAX_LEVEL)+'\\n')\n", 275 | "f1.write('Ratio 1: '+str(RATIO_1)+'\\n')\n", 276 | "f1.write('Ratio 2: '+str(RATIO_2)+'\\n')\n", 277 | "f1.write('Ratio 3: '+str(RATIO_3)+'\\n')\n", 278 | "f1.write('Ratio 4: '+str(RATIO_4)+'\\n')\n", 279 | "f1.write('Ratio 5: '+str(RATIO_5)+'\\n')\n", 280 | "if INDEX_FIT:\n", 281 | " f1.write('First index input: Same as X_1\\n\\n')\n", 282 | "else:\n", 283 | " f1.write('First index input: All 1\\n\\n')\n", 284 | "f1.write('Total leaf node: '+str(sum(leaf_sum))+'\\n')\n", 285 | "f1.write('Average leaf node: '+str(sum(leaf_sum)/TREE_NUM)+' per tree\\n')\n", 286 | "f1.write('Total level: '+str(sum(level_sum))+'\\n')\n", 287 | "f1.write('Average level: '+str(sum(level_sum)/TREE_NUM)+' per tree\\n\\n')\n", 288 | "f1.write('Total number of effective leaf output: '+str(len(Out))+'\\n\\n\\n')\n", 289 | "f1.write(str(DISPLAY_NUM)+' Maximum Features:\\n')\n", 290 | "for i in range(DISPLAY_NUM):\n", 291 | " f1.write('Name: '+str(title[p[i, ], ])+' Time-Count-Score: '+str(count[p[i, ], ])+\n", 292 | " ' Importance: '+str(score[i])+'% Sum of Importance: '+str(score_count[i])+'%\\n')" 293 | ] 294 | }, 295 | { 296 | "cell_type": "code", 297 | "execution_count": null, 298 | "metadata": {}, 299 | "outputs": [], 300 | "source": [ 301 | "print('Total number of effective leaf output:', len(Out))\n", 302 | "set_list = np.unique(Out, axis=0)\n", 303 | "print(set_list.shape)\n", 304 | "# for out in Out:\n", 305 | "# flag = True\n", 306 | "# for s in set_list:\n", 307 | "# if out.flatten().tolist() is s:\n", 308 | "# flag = False\n", 309 | "# break\n", 310 | "# if flag:\n", 311 | "# set_list.append(out)\n", 312 | "print('Total number of unique output:', len(set_list))" 313 | ] 314 | }, 315 | { 316 | "cell_type": "code", 317 | "execution_count": null, 318 | "metadata": {}, 319 | "outputs": [], 320 | "source": [ 321 | "pi = sum(y)/X.shape[0]\n", 322 | "print(pi)\n", 323 | "def cal_i_score(set_in, x_data, y_data):\n", 324 | " global pi\n", 325 | " index_temp = np.argwhere(set_in==1).flatten().tolist()\n", 326 | " list_temp = []\n", 327 | " i_s = 0.0\n", 328 | " for i in range(x_data.shape[0]):\n", 329 | " flag = True\n", 330 | " for f in list_temp:\n", 331 | " if x_data[i, index_temp].flatten().tolist() == f.tolist():\n", 332 | " flag = False\n", 333 | " break\n", 334 | " if flag:\n", 335 | " list_temp.append(x_data[i, index_temp])\n", 336 | " for l in list_temp:\n", 337 | " l_count = 0.0\n", 338 | " l_1_count = 0.0\n", 339 | " for i in range(x_data.shape[0]):\n", 340 | " if x_data[i, index_temp].flatten().tolist() == l.tolist():\n", 341 | " l_count += 1.0\n", 342 | " if y_data[i] == 1:\n", 343 | " l_1_count +=1.0\n", 344 | " i_s += (l_1_count-pi*l_count)**2\n", 345 | " return i_s" 346 | ] 347 | }, 348 | { 349 | "cell_type": "code", 350 | "execution_count": null, 351 | "metadata": { 352 | "scrolled": true 353 | }, 354 | "outputs": [], 355 | "source": [ 356 | "i_score = []\n", 357 | "for s in set_list:\n", 358 | " i_score_temp = cal_i_score(s, X, y)\n", 359 | " i_score.append(i_score_temp)\n", 360 | "y_std = np.var(y)\n", 361 | "d_temp = y_std*X.shape[0]" 362 | ] 363 | }, 364 | { 365 | "cell_type": "code", 366 | "execution_count": null, 367 | "metadata": {}, 368 | "outputs": [], 369 | "source": [ 370 | "p = np.argsort(-np.array(i_score).reshape(len(i_score), )).flatten().tolist()\n", 371 | "new_list = []\n", 372 | "new_score_list = []\n", 373 | "f1.write('\\n\\n\\nThe most effective '+str(REMAIN_NUM)+' featrue-sets to be put into the next greedy dropping step:\\n\\n')\n", 374 | "for i in range(REMAIN_NUM):\n", 375 | " new_list.append(set_list[p[i]])\n", 376 | " new_score_list.append(i_score[p[i]])\n", 377 | " f1.write('Feature list: '+str(title[np.argwhere(new_list[i]==1), ].flatten().tolist())+'\\n I-Score: '+str(new_score_list[i]/d_temp)+'\\n\\n')" 378 | ] 379 | }, 380 | { 381 | "cell_type": "code", 382 | "execution_count": null, 383 | "metadata": {}, 384 | "outputs": [], 385 | "source": [ 386 | "out_list = []\n", 387 | "out_score = []\n", 388 | "for i in range(len(new_list)):\n", 389 | " l = new_list[i]\n", 390 | " old_score = new_score_list[i]\n", 391 | " continue_flag = True\n", 392 | " while continue_flag:\n", 393 | " next_score = []\n", 394 | " next_delete = []\n", 395 | " filled_pos = np.argwhere(l==1).flatten().tolist()\n", 396 | " for p in filled_pos:\n", 397 | " next_delete.append(p)\n", 398 | " l[p] = 0\n", 399 | " next_score.append(cal_i_score(l, X, y))\n", 400 | " l[p] = 1\n", 401 | " if max(next_score) >= old_score:\n", 402 | " l[next_delete[next_score.index(max(next_score))]] = 0\n", 403 | " old_score = max(next_score)\n", 404 | " else:\n", 405 | " continue_flag = False\n", 406 | " out_list.append(title[np.argwhere(l==1), ].flatten().tolist())\n", 407 | " out_score.append(old_score)\n", 408 | "p = np.argsort(-np.array(out_score).reshape(len(out_score), ))\n", 409 | "f1.write('\\n\\n\\nThe most effective featrue-sets after greedy dropping:\\n\\n')\n", 410 | "old = []\n", 411 | "for i in p:\n", 412 | " if str(out_list[i]) not in old:\n", 413 | " f1.write('Feature list: '+str(out_list[i])+'\\n I-Score: '+str(out_score[i]/d_temp)+'\\n\\n')\n", 414 | " old.append(str(out_list[i]))\n", 415 | " else:\n", 416 | " continue\n", 417 | "f1.close()" 418 | ] 419 | } 420 | ], 421 | "metadata": { 422 | "kernelspec": { 423 | "display_name": "PythonForDYH", 424 | "language": "python", 425 | "name": "dyhpy" 426 | }, 427 | "language_info": { 428 | "codemirror_mode": { 429 | "name": "ipython", 430 | "version": 3 431 | }, 432 | "file_extension": ".py", 433 | "mimetype": "text/x-python", 434 | "name": "python", 435 | "nbconvert_exporter": "python", 436 | "pygments_lexer": "ipython3", 437 | "version": "3.7.6" 438 | } 439 | }, 440 | "nbformat": 4, 441 | "nbformat_minor": 2 442 | } 443 | -------------------------------------------------------------------------------- /data and code/code/Smiles2RDKitSmiles.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # coding: utf-8 3 | 4 | # In[ ]: 5 | 6 | 7 | import numpy as np 8 | import rdkit 9 | from rdkit import Chem 10 | 11 | 12 | # In[ ]: 13 | 14 | 15 | smiles = np.loadtxt('Predict_Smiles.csv', dtype=str, comments='!') 16 | s_l = [] 17 | for smi in smiles: 18 | s_l.append(Chem.MolToSmiles(Chem.MolFromSmiles(smi))) 19 | s_l = np.array(s_l).reshape(len(s_l), 1) 20 | np.savetxt('Predict888_Smiles_24.csv', s_l, fmt='%s') 21 | 22 | 23 | # In[ ]: 24 | 25 | 26 | t = np.loadtxt('Title_137.csv', dtype=str, comments='!') 27 | m_out = np.zeros((24, 137)) 28 | for i in range(s_l.shape[0]): 29 | smi = s_l[i, 0] 30 | m = Chem.MolFromSmiles(smi) 31 | for j in range(137): 32 | f = t[j, ] 33 | patt = Chem.MolFromSmarts(f) 34 | m_out[i, j] = len(m.GetSubstructMatches(patt)) 35 | np.savetxt('Predict888_24_137.csv', m_out, fmt='%d', delimiter=',') 36 | 37 | 38 | # In[ ]: 39 | 40 | 41 | from syba.syba import SybaClassifier 42 | syba = SybaClassifier() 43 | syba.fitDefaultScore() 44 | syba_out = np.zeros((24, 1)) 45 | for i in range(s_l.shape[0]): 46 | syba_out[i, 0] = syba.predict(s_l[i, 0]) 47 | np.savetxt('Predict888_SYBA_24.csv', syba_out, fmt='%f', delimiter=',') 48 | 49 | 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