141 lines
5.3 KiB
Python
Executable file
141 lines
5.3 KiB
Python
Executable file
#!/usr/bin/env python3
|
|
# -*- coding: utf-8 -*-
|
|
"""
|
|
Created on Mon Jun 20 13:05:23 2022
|
|
|
|
@author: tanu
|
|
"""
|
|
#%%Imports ####################################################################
|
|
import re
|
|
import argparse
|
|
import os, sys
|
|
|
|
# gene = 'pncA'
|
|
# drug = 'pyrazinamide'
|
|
#total_mtblineage_uc = 8
|
|
###############################################################################
|
|
#%% command line args: case sensitive
|
|
arg_parser = argparse.ArgumentParser()
|
|
arg_parser.add_argument('-d', '--drug', help = 'drug name', default = '')
|
|
arg_parser.add_argument('-g', '--gene', help = 'gene name', default = '')
|
|
args = arg_parser.parse_args()
|
|
|
|
drug = args.drug
|
|
gene = args.gene
|
|
|
|
###############################################################################
|
|
homedir = os.path.expanduser("~")
|
|
sys.path.append(homedir + '/git/LSHTM_analysis/scripts/ml')
|
|
|
|
###############################################################################
|
|
#==================
|
|
# Import data
|
|
#==================
|
|
from ml_data_8020 import *
|
|
setvars(gene,drug)
|
|
from ml_data_8020 import *
|
|
|
|
# from YC run_all_ML: run locally
|
|
#from UQ_yc_RunAllClfs import run_all_ML
|
|
|
|
#====================
|
|
# Import ML functions
|
|
#====================
|
|
from MultClfs import *
|
|
|
|
#==================
|
|
# other vars
|
|
#==================
|
|
tts_split_8020 = '80_20'
|
|
OutFile_suffix = '8020'
|
|
|
|
#==================
|
|
# Specify outdir
|
|
#==================
|
|
outdir_ml = outdir + 'ml/tts_8020/'
|
|
print('\nOutput directory:', outdir_ml)
|
|
|
|
#outFile_wf = outdir_ml + gene.lower() + '_baselineC_' + OutFile_suffix + '.csv'
|
|
outFile_wf = outdir_ml + gene.lower() + '_baselineC_noOR' + OutFile_suffix + '.csv'
|
|
#%% Running models ############################################################
|
|
print('\n#####################################################################\n'
|
|
, '\nStarting--> Running ML analysis: Baseline modes (No FS)'
|
|
, '\nGene name:', gene
|
|
, '\nDrug name:', drug
|
|
, '\n#####################################################################\n')
|
|
|
|
paramD = {
|
|
'baseline_paramD': { 'input_df' : X
|
|
, 'target' : y
|
|
, 'var_type' : 'mixed'
|
|
, 'resampling_type': 'none'}
|
|
|
|
, 'smnc_paramD': { 'input_df' : X_smnc
|
|
, 'target' : y_smnc
|
|
, 'var_type' : 'mixed'
|
|
, 'resampling_type' : 'smnc'}
|
|
|
|
, 'ros_paramD': { 'input_df' : X_ros
|
|
, 'target' : y_ros
|
|
, 'var_type' : 'mixed'
|
|
, 'resampling_type' : 'ros'}
|
|
|
|
, 'rus_paramD' : { 'input_df' : X_rus
|
|
, 'target' : y_rus
|
|
, 'var_type' : 'mixed'
|
|
, 'resampling_type' : 'rus'}
|
|
|
|
, 'rouC_paramD' : { 'input_df' : X_rouC
|
|
, 'target' : y_rouC
|
|
, 'var_type' : 'mixed'
|
|
, 'resampling_type' : 'rouC'}
|
|
}
|
|
|
|
##==============================================================================
|
|
## Dict with no CV BT formatted df
|
|
## mmD = {}
|
|
## for k, v in paramD.items():
|
|
## # print(mmD[k])
|
|
## scores_8020D = MultModelsCl(**paramD[k]
|
|
## , tts_split_type = tts_split_8020
|
|
## , skf_cv = skf_cv
|
|
## , blind_test_df = X_bts
|
|
## , blind_test_target = y_bts
|
|
## , add_cm = True
|
|
## , add_yn = True
|
|
## , return_formatted_output = False)
|
|
## mmD[k] = scores_8020D
|
|
##==============================================================================
|
|
## Initial run to get the dict of dicts for each sampling type containing CV, BT and metadata DFs
|
|
mmDD = {}
|
|
for k, v in paramD.items():
|
|
scores_8020D = MultModelsCl(**paramD[k]
|
|
, tts_split_type = tts_split_8020
|
|
, skf_cv = skf_cv
|
|
, blind_test_df = X_bts
|
|
, blind_test_target = y_bts
|
|
, add_cm = True
|
|
, add_yn = True
|
|
, return_formatted_output = True)
|
|
mmDD[k] = scores_8020D
|
|
|
|
# Extracting the dfs from within the dict and concatenating to output as one df
|
|
for k, v in mmDD.items():
|
|
out_wf_8020 = pd.concat(mmDD, ignore_index = True)
|
|
|
|
out_wf_8020f = out_wf_8020.sort_values(by = ['resampling', 'source_data', 'MCC'], ascending = [True, True, False], inplace = False)
|
|
|
|
print('\n######################################################################'
|
|
, '\nEnd--> Successfully generated output DF for Multiple classifiers (baseline models)'
|
|
, '\nGene:', gene.lower()
|
|
, '\nDrug:', drug
|
|
, '\noutput file:', outFile_wf
|
|
, '\nDim of output:', out_wf_8020f.shape
|
|
, '\n######################################################################')
|
|
###############################################################################
|
|
#====================
|
|
# Write output file
|
|
#====================
|
|
out_wf_8020f.to_csv(outFile_wf, index = False)
|
|
print('\nFile successfully written:', outFile_wf)
|
|
###############################################################################
|