99 lines
No EOL
4.9 KiB
Python
99 lines
No EOL
4.9 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Mon Mar 7 15:20:42 2022
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@author: tanu
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"""
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fit_time 0.008588
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score_time 0.004460
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test_acc 0.690148
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test_prec 0.690868
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test_rec 0.771250
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test_f1 0.725441
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# RF
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fit_time 0.368793
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score_time 0.110153
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test_acc 0.672537
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test_prec 0.664875
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test_rec 0.790417
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test_f1 0.720224
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dtype: float64
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#%%
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numerical_features: ['ligand_distance', 'ligand_affinity_change'
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, 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2'
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, 'asa', 'rsa', 'kd_values', 'rd_values'
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, 'consurf_score', 'snap2_score', 'snap2_accuracy_pc']
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Model F1_Score Precision Recall Accuracy ROC_AUC
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0 Logistic Regression 0.734177 0.690476 0.783784 0.700000 0.694922
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1 Naive Bayes 0.467290 0.757576 0.337838 0.592857 0.608313
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2 K-Nearest Neighbors 0.773006 0.707865 0.851351 0.735714 0.728706
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3 SVM 0.766467 0.688172 0.864865 0.721429 0.712735
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4 MLP 0.725000 0.674419 0.783784 0.685714 0.679771
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5 Decision Tree 0.662069 0.676056 0.648649 0.650000 0.650082
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6 Extra Trees 0.748387 0.716049 0.783784 0.721429 0.717649
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7 Random Forest 0.722581 0.691358 0.756757 0.692857 0.688984
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8 Random Forest2 0.731707 0.666667 0.810811 0.685714 0.678133
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9 XGBoost 0.692810 0.670886 0.716216 0.664286 0.661138)
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all_features: numerical_features + ['ss_class', 'wt_prop_water', 'mut_prop_water', 'wt_prop_polarity',
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'mut_prop_polarity', 'wt_calcprop', 'mut_calcprop', 'active_aa_pos']
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Model F1_Score Precision Recall Accuracy ROC_AUC
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0 Logistic Regression 0.757764 0.701149 0.824324 0.721429 0.715192
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1 Naive Bayes 0.620690 0.633803 0.608108 0.607143 0.607084
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2 K-Nearest Neighbors 0.619355 0.592593 0.648649 0.578571 0.574324
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3 SVM 0.766467 0.688172 0.864865 0.721429 0.712735
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4 MLP 0.738854 0.698795 0.783784 0.707143 0.702498
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5 Decision Tree 0.666667 0.701493 0.635135 0.664286 0.666052
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6 Extra Trees 0.728395 0.670455 0.797297 0.685714 0.678952
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7 Random Forest 0.763636 0.692308 0.851351 0.721429 0.713554
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8 Random Forest2 0.746988 0.673913 0.837838 0.700000 0.691646
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9 XGBoost 0.710526 0.692308 0.729730 0.685714 0.683047)
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#%%
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Model F1_Score Precision Recall Accuracy ROC_AUC
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0Num Logistic Regression 0.734177 0.690476 0.783784 0.700000 0.694922
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0All Logistic Regression 0.757764 0.701149 0.824324 0.721429 0.715192
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1Num Naive Bayes 0.467290 0.757576 0.337838 0.592857 0.608313
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1All Naive Bayes 0.620690 0.633803 0.608108 0.607143 0.607084
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2Num K-Nearest Neighbors 0.773006 0.707865 0.851351 0.735714 0.728706 ** 'Num' is better than 'All'
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2All K-Nearest Neighbors 0.619355 0.592593 0.648649 0.578571 0.574324
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3Num SVM 0.766467 0.688172 0.864865 0.721429 0.712735
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3All SVM 0.766467 0.688172 0.864865 0.721429 0.712735
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4Num MLP 0.725000 0.674419 0.783784 0.685714 0.679771
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4All MLP 0.738854 0.698795 0.783784 0.707143 0.702498
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5Num Decision Tree 0.662069 0.676056 0.648649 0.650000 0.650082 ** marginal, equivalent
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5All Decision Tree 0.666667 0.701493 0.635135 0.664286 0.666052
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6Num Extra Trees 0.748387 0.716049 0.783784 0.721429 0.717649 ** marginal, equivalent
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6All Extra Trees 0.728395 0.670455 0.797297 0.685714 0.678952
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7Num Random Forest 0.722581 0.691358 0.756757 0.692857 0.688984
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7All Random Forest 0.763636 0.692308 0.851351 0.721429 0.713554
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8Num Random Forest2 0.731707 0.666667 0.810811 0.685714 0.678133
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8All Random Forest2 0.746988 0.673913 0.837838 0.700000 0.691646
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9Num XGBoost 0.692810 0.670886 0.716216 0.664286 0.661138)
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9All XGBoost 0.710526 0.692308 0.729730 0.685714 0.683047)
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#%%
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Model F1_Score Precision Recall Accuracy ROC_AUC
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0 Logistic Regression 0.757764 0.701149 0.824324 0.721429 0.715192
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1 Naive Bayes 0.628571 0.666667 0.594595 0.628571 0.630631
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2 K-Nearest Neighbors 0.666667 0.623529 0.716216 0.621429 0.615684
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3 SVM 0.766467 0.688172 0.864865 0.721429 0.712735
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4 MLP 0.726115 0.686747 0.770270 0.692857 0.688165
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5 Decision Tree 0.647482 0.692308 0.608108 0.650000 0.652539
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6 Extra Trees 0.760736 0.696629 0.837838 0.721429 0.714373
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7 Random Forest 0.736196 0.674157 0.810811 0.692857 0.685708
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8 Random Forest2 0.736196 0.674157 0.810811 0.692857 0.685708
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9 XGBoost 0.710526 0.692308 0.729730 0.685714 0.683047) |