207 lines
8.1 KiB
Python
207 lines
8.1 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Sat May 28 05:25:30 2022
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@author: tanu
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"""
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import os
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gene = 'pncA'
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drug = 'pyrazinamide'
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#total_mtblineage_uc = 8
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homedir = os.path.expanduser("~")
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os.chdir( homedir + '/git/LSHTM_analysis/scripts/ml/')
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#---------------------------
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# Version 1: no AAindex
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#from UQ_ML_data import *
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#setvars(gene,drug)
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#from UQ_ML_data import *
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#---------------------------
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from ml_data_dissected import *
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setvars(gene,drug)
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from ml_data_dissected import *
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# from YC run_all_ML: run locally
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#from UQ_yc_RunAllClfs import run_all_ML
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# TT run all ML clfs: baseline mode
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from MultModelsCl_dissected import MultModelsCl_dissected
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############################################################################
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print('\n#####################################################################\n'
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, '\nRunning ML analysis: UQ [without AA index but with active site annotations]'
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, '\nGene name:', gene
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, '\nDrug name:', drug)
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#==================
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# Specify outdir
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#==================
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outdir_ml = outdir + 'ml/uq_v1/dissected'
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print('\nOutput directory:', outdir_ml)
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#%%###########################################################################
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print('\n================================================================\n')
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X_structural_FN = X_stability_FN + X_affinityFN + X_resprop_FN
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X_aaindex_Fnum + X_str_Fnum + X_aap_Fcat
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all_featuresN = X_evolFN + X_structural_FN + X_genomicFN
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print('\n================================================================'
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, '\nTotal Evolutionary features (n):' , len(X_evolFN)
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, '\n--------------Evol. feature colnames:', X_evolFN
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, '\n================================================================'
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, '\n\nTotal structural features (n):', len(X_structural_FN)
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, '\n--------Stability ncols:' , len(X_stability_FN)
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, '\n--------------Common stability colnames:' , X_common_stability_Fnum
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, '\n--------------Foldx colnames:' , X_foldX_Fnum
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, '\n--------Affinity ncols:' , len(X_affinityFN)
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, '\n--------------Common affinity colnames:' , common_affinity_Fnum
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, '\n--------------Gene specific affinity colnames:', gene_affinity_colnames
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, '\n--------Residue prop ncols:' , len(X_resprop_FN)
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, '\n--------------Residue Prop cols:' , X_str_Fnum
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, '\n--------------AA change Prop cols:' , X_aap_Fcat
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, '\n--------------AA index cols:' , X_aaindex_Fnum
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, '\n================================================================'
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, '\n\nTotal Genomic features (n):' , len(X_genomicFN)
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, '\n--------MAF+OR cols:' , len(X_gn_mafor_Fnum)
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, '\n--------------MAF+OR colnames:' , X_gn_mafor_Fnum
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, '\n--------Lineage cols:' , len(X_gn_linegae_Fnum)
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, '\n--------------Lineage cols:' , X_gn_linegae_Fnum
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, '\n--------Other cols:' , len(X_gn_Fcat)
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, '\n--------------Other cols:' , X_gn_Fcat
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, '\n================================================================')
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# Sanity check
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if ( len(X.columns) == len(X_evolFN) + len(X_structural_FN) + len(X_genomicFN)):
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print('\nPass: No. of features match')
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else:
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print('\nFail: Count of feature mismatch'
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, '\nExpected:', len(X_evolFN) + len(X_structural_FN) + len(X_genomicFN)
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, '\nGot:', len(X.columns))
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sys.exit()
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print('\n#####################################################################\n')
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# ###############################################################################
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# #==================
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# # Baseline models
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# #==================
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# mm_skf_scoresD = MultModelsCl_dissected(input_df = X
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# , target = y
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# , var_type = 'mixed'
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# , skf_cv = skf_cv
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# , blind_test_input_df = X_bts
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# , blind_test_target = y_bts)
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# baseline_all = pd.DataFrame(mm_skf_scoresD)
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# baseline_all = baseline_all.T
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# #baseline_train = baseline_all.filter(like='train_', axis=1)
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# baseline_CT = baseline_all.filter(like='test_', axis=1)
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# baseline_CT.sort_values(by=['test_mcc'], ascending=False, inplace=True)
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# baseline_BT = baseline_all.filter(like='bts_', axis=1)
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# baseline_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
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# # Write csv
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# baseline_CT.to_csv(outdir_ml + gene.lower() + '_baseline_CT_allF.csv')
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# baseline_BT.to_csv(outdir_ml + gene.lower() + '_baseline_BT_allF.csv')
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# #%% SMOTE NC: Oversampling [Numerical + categorical]
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# mm_skf_scoresD7 = MultModelsCl(input_df = X_smnc
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# , target = y_smnc
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# , var_type = 'mixed'
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# , skf_cv = skf_cv
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# , blind_test_input_df = X_bts
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# , blind_test_target = y_bts)
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# smnc_all = pd.DataFrame(mm_skf_scoresD7)
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# smnc_all = smnc_all.T
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# smnc_CT = smnc_all.filter(like='test_', axis=1)
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# smnc_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True)
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# smnc_BT = smnc_all.filter(like='bts_', axis=1)
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# smnc_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
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# # Write csv
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# smnc_CT.to_csv(outdir_ml + gene.lower() + '_smnc_CT_allF.csv')
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# smnc_BT.to_csv(outdir_ml + gene.lower() + '_smnc_BT_allF.csv')
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# #%% ROS: Numerical + categorical
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# mm_skf_scoresD3 = MultModelsCl(input_df = X_ros
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# , target = y_ros
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# , var_type = 'mixed'
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# , skf_cv = skf_cv
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# , blind_test_input_df = X_bts
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# , blind_test_target = y_bts)
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# ros_all = pd.DataFrame(mm_skf_scoresD3)
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# ros_all = ros_all.T
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# ros_CT = ros_all.filter(like='test_', axis=1)
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# ros_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True)
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# ros_BT = ros_all.filter(like='bts_', axis=1)
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# ros_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
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# # Write csv
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# ros_CT.to_csv(outdir_ml + gene.lower() + '_ros_CT_allF.csv')
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# ros_BT.to_csv(outdir_ml + gene.lower() + '_ros_BT_allF.csv')
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# #%% RUS: Numerical + categorical
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# mm_skf_scoresD4 = MultModelsCl(input_df = X_rus
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# , target = y_rus
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# , var_type = 'mixed'
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# , skf_cv = skf_cv
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# , blind_test_input_df = X_bts
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# , blind_test_target = y_bts)
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# rus_all = pd.DataFrame(mm_skf_scoresD4)
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# rus_all = rus_all.T
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# rus_CT = rus_all.filter(like='test_', axis=1)
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# rus_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True)
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# rus_BT = rus_all.filter(like='bts_' , axis=1)
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# rus_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
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# # Write csv
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# rus_CT.to_csv(outdir_ml + gene.lower() + '_rus_CT_allF.csv')
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# rus_BT.to_csv(outdir_ml + gene.lower() + '_rus_BT_allF.csv')
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# #%% ROS + RUS Combined: Numerical + categorical
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# mm_skf_scoresD8 = MultModelsCl(input_df = X_rouC
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# , target = y_rouC
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# , var_type = 'mixed'
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# , skf_cv = skf_cv
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# , blind_test_input_df = X_bts
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# , blind_test_target = y_bts)
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# rouC_all = pd.DataFrame(mm_skf_scoresD8)
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# rouC_all = rouC_all.T
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# rouC_CT = rouC_all.filter(like='test_', axis=1)
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# rouC_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True)
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# rouC_BT = rouC_all.filter(like='bts_', axis=1)
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# rouC_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
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# # Write csv
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# rouC_CT.to_csv(outdir_ml + gene.lower() + '_rouC_CT_allF.csv')
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# rouC_BT.to_csv(outdir_ml + gene.lower() + '_rouC_BT_allF.csv')
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