#!/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_7030 import *
setvars(gene,drug)
from ml_data_7030 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_7030    = '70_30'
OutFile_suffix  = '7030'

#==================
# Specify outdir 
#==================
outdir_ml = outdir + 'ml/tts_7030/'
print('\nOutput directory:', outdir_ml)

outFile_wf = outdir_ml + gene.lower() + '_baselineC_' + OutFile_suffix + '.csv'
#outFile_lf = outdir_ml + gene.lower() + '_baselineC_ext_' + 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'}
        }

# Initial run to get the dict containing CV, BT and metadata DFs 
mmD = {}
for k, v in paramD.items():
#    print(mmD[k])
    scores_7030D = MultModelsCl(**paramD[k]
                        , tts_split_type = tts_split_7030
                        , skf_cv = skf_cv
                        , blind_test_df = X_bts
                        , blind_test_target = y_bts
                        , add_cm = True 
                        , add_yn = True
                        , return_formatted_output = True)
    mmD[k] = scores_7030D

# Extracting the dfs from within the dict and concatenating to output as one df
for k, v in mmD.items():
    out_wf_7030 = pd.concat(mmD, ignore_index = True)
    
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_7030.shape
      , '\n######################################################################')
###############################################################################
#====================
# Write output file
#====================
#out_wf_7030.to_csv(outFile_wf, index = False)
print('\nFile successfully written:', outFile_wf)
###############################################################################