#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jun 29 20:29:36 2022

@author: tanu
"""
import sys, os
import pandas as pd
import numpy as np
import re
    
###############################################################################
homedir = os.path.expanduser("~")
sys.path.append(homedir + '/git/LSHTM_analysis/scripts/ml/ml_functions')
sys.path
###############################################################################
outdir = homedir + '/git/LSHTM_ML/output/combined/'

#====================
# Import ML functions 
#====================
#from MultClfs import *
#from MultClfs_logo_skf import *
from MultClfs_logo_skf_split import *

from GetMLData import *
from SplitTTS import *

# Input data
from ml_data_combined import *

###############################################################################
print('\nUsing data with 5 genes:', len(cm_input_df5))

###############################################################################

split_types = ['70_30', '80_20', 'sl']
split_data_types = ['actual', 'complete']

for split_type in split_types:
    for data_type in split_data_types:

        out_filename = outdir + 'cm_' + split_type + '_' + data_type + '.csv'
        print(out_filename)
        tempD = split_tts(cm_input_df5
                  , data_type = data_type
                  , split_type = split_type
                  , oversampling = True
                  , dst_colname = 'dst'
                  , target_colname = 'dst_mode'
                  , include_gene_name = True
              )
        paramD = {
                'baseline_paramD': { 'input_df'         : tempD['X']
                                    , 'target'          : tempD['y']
                                    , 'var_type'        : 'mixed'
                                    , 'resampling_type' : 'none'}
                , 'smnc_paramD'  : { 'input_df'         : tempD['X_smnc']
                                    , 'target'          : tempD['y_smnc']
                                    , 'var_type'        : 'mixed'
                                    , 'resampling_type' : 'smnc'}
                , 'ros_paramD'   : { 'input_df'         : tempD['X_ros']
                                    , 'target'          : tempD['y_ros']
                                    , 'var_type'        : 'mixed'
                                    , 'resampling_type' : 'ros'}
                , 'rus_paramD'   : { 'input_df'         : tempD['X_rus']
                                    , 'target'          : tempD['y_rus']
                                    , 'var_type'        : 'mixed'
                                    , 'resampling_type' : 'rus'}
                , 'rouC_paramD'  : { 'input_df'         : tempD['X_rouC']
                                    , 'target'          : tempD['y_rouC']
                                    , 'var_type'        : 'mixed'
                                    , 'resampling_type' : 'rouC'}
                }
        
        mmDD = {}
        for k, v in paramD.items():
            scoresD = MultModelsCl_logo_skf(**paramD[k]
            XXXXXXXXXXXXXXXXXXXXXXX
            mmDD[k] = scoresD

        # Extracting the dfs from within the dict and concatenating to output as one df
        for k, v in mmDD.items():
            out_wf= pd.concat(mmDD, ignore_index = True)
        
        out_wf_f = out_wf.sort_values(by = ['resampling', 'source_data', 'MCC'], ascending = [True, True, False], inplace = False)
        out_wf_f.to_csv(('/home/tanu/git/Data/ml_combined/genes/'+out_filename), index = False)