141 lines
No EOL
5.3 KiB
Python
Executable file
141 lines
No EOL
5.3 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 Mon Jun 20 13:05:23 2022
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@author: tanu
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"""
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#%%Imports ####################################################################
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import re
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import argparse
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import os, sys
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# gene = 'pncA'
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# drug = 'pyrazinamide'
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#total_mtblineage_uc = 8
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###############################################################################
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#%% command line args: case sensitive
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arg_parser = argparse.ArgumentParser()
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arg_parser.add_argument('-d', '--drug', help = 'drug name', default = '')
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arg_parser.add_argument('-g', '--gene', help = 'gene name', default = '')
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args = arg_parser.parse_args()
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drug = args.drug
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gene = args.gene
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###############################################################################
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homedir = os.path.expanduser("~")
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sys.path.append(homedir + '/git/LSHTM_analysis/scripts/ml')
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###############################################################################
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#==================
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# Import data
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#==================
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from ml_data_7030 import *
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setvars(gene,drug)
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from ml_data_7030 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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#====================
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# Import ML functions
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#====================
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from MultClfs import *
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#==================
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# other vars
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#==================
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tts_split_7030 = '70_30'
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OutFile_suffix = '7030'
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#==================
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# Specify outdir
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#==================
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outdir_ml = outdir + 'ml/tts_7030/'
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print('\nOutput directory:', outdir_ml)
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#outFile_wf = outdir_ml + gene.lower() + '_baselineC_' + OutFile_suffix + '.csv'
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outFile_wf = outdir_ml + gene.lower() + '_baselineC_noOR' + OutFile_suffix + '.csv'
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#%% Running models ############################################################
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print('\n#####################################################################\n'
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, '\nStarting--> Running ML analysis: Baseline modes (No FS)'
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, '\nGene name:', gene
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, '\nDrug name:', drug
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, '\n#####################################################################\n')
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paramD = {
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'baseline_paramD': { 'input_df' : X
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, 'target' : y
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, 'var_type' : 'mixed'
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, 'resampling_type': 'none'}
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, 'smnc_paramD': { 'input_df' : X_smnc
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, 'target' : y_smnc
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, 'var_type' : 'mixed'
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, 'resampling_type' : 'smnc'}
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, 'ros_paramD': { 'input_df' : X_ros
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, 'target' : y_ros
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, 'var_type' : 'mixed'
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, 'resampling_type' : 'ros'}
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, 'rus_paramD' : { 'input_df' : X_rus
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, 'target' : y_rus
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, 'var_type' : 'mixed'
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, 'resampling_type' : 'rus'}
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, 'rouC_paramD' : { 'input_df' : X_rouC
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, 'target' : y_rouC
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, 'var_type' : 'mixed'
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, 'resampling_type' : 'rouC'}
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}
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##==============================================================================
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## Dict with no CV BT formatted df
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## mmD = {}
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## for k, v in paramD.items():
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## # print(mmD[k])
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## scores_7030D = MultModelsCl(**paramD[k]
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## , tts_split_type = tts_split_7030
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## , skf_cv = skf_cv
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## , blind_test_df = X_bts
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## , blind_test_target = y_bts
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## , add_cm = True
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## , add_yn = True
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## , return_formatted_output = False)
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## mmD[k] = scores_7030D
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##==============================================================================
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## Initial run to get the dict of dicts for each sampling type containing CV, BT and metadata DFs
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mmDD = {}
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for k, v in paramD.items():
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scores_7030D = MultModelsCl(**paramD[k]
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, tts_split_type = tts_split_7030
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, skf_cv = skf_cv
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, blind_test_df = X_bts
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, blind_test_target = y_bts
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, add_cm = True
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, add_yn = True
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, return_formatted_output = True)
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mmDD[k] = scores_7030D
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# Extracting the dfs from within the dict and concatenating to output as one df
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for k, v in mmDD.items():
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out_wf_7030 = pd.concat(mmDD, ignore_index = True)
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out_wf_7030f = out_wf_7030.sort_values(by = ['resampling', 'source_data', 'MCC'], ascending = [True, True, False], inplace = False)
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print('\n######################################################################'
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, '\nEnd--> Successfully generated output DF for Multiple classifiers (baseline models)'
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, '\nGene:', gene.lower()
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, '\nDrug:', drug
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, '\noutput file:', outFile_wf
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, '\nDim of output:', out_wf_7030f.shape
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, '\n######################################################################')
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###############################################################################
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#====================
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# Write output file
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#====================
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out_wf_7030f.to_csv(outFile_wf, index = False)
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print('\nFile successfully written:', outFile_wf)
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############################################################################### |