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added prelim run for pnca all models with on-hot encoder multi model pipeline

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Tanushree Tunstall 2022-03-07 18:27:58 +00:00
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 7 15:20:42 2022
@author: tanu
"""
fit_time 0.008588
score_time 0.004460
test_acc 0.690148
test_prec 0.690868
test_rec 0.771250
test_f1 0.725441
# RF
fit_time 0.368793
score_time 0.110153
test_acc 0.672537
test_prec 0.664875
test_rec 0.790417
test_f1 0.720224
dtype: float64
#%%
numerical_features: ['ligand_distance', 'ligand_affinity_change'
, 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2'
, 'asa', 'rsa', 'kd_values', 'rd_values'
, 'consurf_score', 'snap2_score', 'snap2_accuracy_pc']
Model F1_Score Precision Recall Accuracy ROC_AUC
0 Logistic Regression 0.734177 0.690476 0.783784 0.700000 0.694922
1 Naive Bayes 0.467290 0.757576 0.337838 0.592857 0.608313
2 K-Nearest Neighbors 0.773006 0.707865 0.851351 0.735714 0.728706
3 SVM 0.766467 0.688172 0.864865 0.721429 0.712735
4 MLP 0.725000 0.674419 0.783784 0.685714 0.679771
5 Decision Tree 0.662069 0.676056 0.648649 0.650000 0.650082
6 Extra Trees 0.748387 0.716049 0.783784 0.721429 0.717649
7 Random Forest 0.722581 0.691358 0.756757 0.692857 0.688984
8 Random Forest2 0.731707 0.666667 0.810811 0.685714 0.678133
9 XGBoost 0.692810 0.670886 0.716216 0.664286 0.661138)
all_features: numerical_features + ['ss_class', 'wt_prop_water', 'mut_prop_water', 'wt_prop_polarity',
'mut_prop_polarity', 'wt_calcprop', 'mut_calcprop', 'active_aa_pos']
Model F1_Score Precision Recall Accuracy ROC_AUC
0 Logistic Regression 0.757764 0.701149 0.824324 0.721429 0.715192
1 Naive Bayes 0.620690 0.633803 0.608108 0.607143 0.607084
2 K-Nearest Neighbors 0.619355 0.592593 0.648649 0.578571 0.574324
3 SVM 0.766467 0.688172 0.864865 0.721429 0.712735
4 MLP 0.738854 0.698795 0.783784 0.707143 0.702498
5 Decision Tree 0.666667 0.701493 0.635135 0.664286 0.666052
6 Extra Trees 0.728395 0.670455 0.797297 0.685714 0.678952
7 Random Forest 0.763636 0.692308 0.851351 0.721429 0.713554
8 Random Forest2 0.746988 0.673913 0.837838 0.700000 0.691646
9 XGBoost 0.710526 0.692308 0.729730 0.685714 0.683047)
#%%
Model F1_Score Precision Recall Accuracy ROC_AUC
0Num Logistic Regression 0.734177 0.690476 0.783784 0.700000 0.694922
0All Logistic Regression 0.757764 0.701149 0.824324 0.721429 0.715192
1Num Naive Bayes 0.467290 0.757576 0.337838 0.592857 0.608313
1All Naive Bayes 0.620690 0.633803 0.608108 0.607143 0.607084
2Num K-Nearest Neighbors 0.773006 0.707865 0.851351 0.735714 0.728706 ** 'Num' is better than 'All'
2All K-Nearest Neighbors 0.619355 0.592593 0.648649 0.578571 0.574324
3Num SVM 0.766467 0.688172 0.864865 0.721429 0.712735
3All SVM 0.766467 0.688172 0.864865 0.721429 0.712735
4Num MLP 0.725000 0.674419 0.783784 0.685714 0.679771
4All MLP 0.738854 0.698795 0.783784 0.707143 0.702498
5Num Decision Tree 0.662069 0.676056 0.648649 0.650000 0.650082 ** marginal, equivalent
5All Decision Tree 0.666667 0.701493 0.635135 0.664286 0.666052
6Num Extra Trees 0.748387 0.716049 0.783784 0.721429 0.717649 ** marginal, equivalent
6All Extra Trees 0.728395 0.670455 0.797297 0.685714 0.678952
7Num Random Forest 0.722581 0.691358 0.756757 0.692857 0.688984
7All Random Forest 0.763636 0.692308 0.851351 0.721429 0.713554
8Num Random Forest2 0.731707 0.666667 0.810811 0.685714 0.678133
8All Random Forest2 0.746988 0.673913 0.837838 0.700000 0.691646
9Num XGBoost 0.692810 0.670886 0.716216 0.664286 0.661138)
9All XGBoost 0.710526 0.692308 0.729730 0.685714 0.683047)