188 lines
6.6 KiB
Python
Executable file
188 lines
6.6 KiB
Python
Executable file
#!/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 = 'gid'
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drug = 'streptomycin'
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#total_mtblineage_u = 8
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homedir = os.path.expanduser("~")
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os.chdir( homedir + '/git/ML_AI_training/')
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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 UQ_ML_data2 import *
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setvars(gene,drug)
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from UQ_ML_data2 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 UQ_MultModelsCl import MultModelsCl
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#%%###########################################################################
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print('\n#####################################################################\n')
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print('TESTING cmd:'
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, '\nGene name:', gene
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, '\nDrug name:', drug
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, '\nTotal input features:', X.shape
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, '\n', Counter(y))
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print('Strucutral features (n):'
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, len(X_ssFN)
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, '\nThese are:'
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, '\nCommon stablity features:', X_stabilityN
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, '\nFoldX columns:', X_foldX_cols
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, '\nOther struc columns:', X_str
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, '\n================================================================\n')
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print('AAindex features (n):'
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, len(X_aaindexFN)
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, '\nThese are:\n'
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, X_aaindexFN
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, '\n================================================================\n')
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print('Evolutionary features (n):'
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, len(X_evolFN)
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, '\nThese are:\n'
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, X_evolFN
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, '\n================================================================\n')
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print('Genomic features (n):'
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, len(X_genomicFN)
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, '\nThese are:\n'
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, X_genomic_mafor, '\n'
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, X_genomic_linegae
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, '\n================================================================\n')
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print('Categorical features (n):'
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, len(categorical_FN)
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, '\nThese are:\n'
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, categorical_FN
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, '\n================================================================\n')
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if ( len(X.columns) == len(X_ssFN) + len(X_aaindexFN) + len(X_evolFN) + len(X_genomicFN) + len(categorical_FN) ):
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print('\nPass: No. of features match')
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else:
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sys.exit('\nFail: Count of feature mismatch')
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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(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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