#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jun 20 13:05:23 2022

@author: tanu
"""
import re
#all_featuresN   = X_evolFN + X_structural_FN + X_genomicFN
#    X_structural_FN =  X_stability_FN + X_affinityFN + X_resprop_FN
#    X_resprop_FN    = X_aaindex_Fnum + X_str_Fnum + X_aap_Fcat
    
   
score_type_ordermapD = { 'mcc'           : 1
                   , 'fscore'       : 2
                   , 'jcc'          : 3
                   , 'precision'    : 4
                   , 'recall'       : 5      
                   , 'accuracy'     : 6  
                   , 'roc_auc'      : 7
                   , 'TN'           : 8
                   , 'FP'           : 9
                   , 'FN'           : 10
                   , 'TP'           : 11  
                   , 'trainingY_neg': 12  
                   , 'trainingY_pos': 13    
                   , 'blindY_neg'   : 14
                   , 'blindY_pos'   : 15
                   , 'fit_time'     : 16
                   , 'score_time'   : 17
                   }
     
     
#==================
# Baseline models 
#==================
# cm_di2 = MultModelsCl_dissected(input_df = X
#                     , target = y
#                     , var_type = 'mixed'
#                     , skf_cv = skf_cv
#                     , blind_test_input_df = X_bts
#                     , blind_test_target = y_bts
#                     , add_cm = True 
#                     , add_yn = True)

# baseline_all2 = pd.DataFrame(cm_di2)
# baseline_all2T = baseline_all2.T
# baseline_CTBT2 = baseline_all2T.filter(regex = 'test_.*|bts_.*|TN|FP|FN|TP|.*_neg|.*_pos' , axis = 1)

#================
# Stability cols
#================


#================
# Affinity cols
#================


#================
# Residue level
#================


#================
# Genomics
# X_genomicFN
#================
feature_gp_name = 'genomics'

scores_mm_gn = MultModelsCl_dissected(input_df = X[X_genomicFN]
                    , target = y
                    , var_type = 'mixed'
                    , skf_cv = skf_cv
                    , blind_test_input_df = X_bts[X_genomicFN]
                    , blind_test_target = y_bts
                    , add_cm = True 
                    , add_yn = True)

baseline_all_gn = pd.DataFrame(scores_mm_gn)

baseline_GN = baseline_all_gn.filter(regex = 'bts_.*|test_.*|.*_time|TN|FP|FN|TP|.*_neg|.*_pos', axis = 0)
baseline_GN = baseline_GN.reset_index()
baseline_GN.rename(columns = {'index': 'original_names'}, inplace = True)

# Indicate whether BT or CT
bt_pattern = re.compile(r'bts_.*')
baseline_GN['data_source'] = baseline_GN.apply(lambda row: 'BT' if bt_pattern.search(row.original_names) else 'CV' , axis = 1)

baseline_GN['score_type'] = baseline_GN['original_names'].str.replace('bts_|test_', '', regex = True)

score_type_uniqueN = set(baseline_GN['score_type'])
cL1 = list(score_type_ordermapD.keys())
cL2 = list(score_type_uniqueN)

if set(cL1).issubset(cL2):
    print('\nPASS: sorting df by score that is mapped onto the order I want')
    baseline_GN['score_order'] = baseline_GN['score_type'].map(score_type_ordermapD)
    baseline_GN.sort_values(by = ['data_source', 'score_order'], ascending = [True, True], inplace = True)
else:
    sys.exit('\nFAIL: could not sort df as score mapping for ordering failed')
    
baseline_GN['feature_group'] = feature_gp_name
    
#-------------
# Blind test
#-------------
baseline_BT = baseline_all_gn.filter(regex = 'bts_', axis = 0)
baseline_BT = baseline_BT.reset_index()
baseline_BT.rename(columns = {'index': 'original_names'}, inplace = True)
baseline_BT['score_type'] = baseline_BT['original_names'] 
baseline_BT['score_type'] = baseline_BT['score_type'].str.replace('bts_*', '', regex = True)
baseline_BT['data_source'] = 'BT_score'

#--------
# CV
#--------
baseline_CT = baseline_all_gn.filter(regex = '.*_time|test_.*|TN|FP|FN|TP|.*_neg|.*_pos', axis = 0)
baseline_CT = baseline_CT.reset_index()
baseline_CT.rename(columns = {'index': 'original_names'}, inplace = True)
baseline_CT['score_type'] = baseline_CT['original_names'] 
baseline_CT['score_type'] = baseline_CT['score_type'].str.replace('test_*', '', regex = True)
baseline_CT['data_source'] = 'CT_score'

#----------------------
# rpow bind: CT and BT
#----------------------
if all(baseline_BT.columns == baseline_CT.columns):
    print('\nPASS: Colnames match, proceeding to row bind for data:', feature_gp_name
          , '\nDim of df1 (BT):', baseline_BT.shape
          , '\nDim of df2 (CT):', baseline_CT.shape)
    comb_df_gn = pd.concat([baseline_BT, baseline_CT], axis = 0, ignore_index = True) 
    comb_df_gn['feature_group'] = feature_gp_name
    print('\nDim of combined df:', comb_df_gn.shape)
else:
    print('\nFAIL: colnames mismatch, cannot combine')
    
# good way but I don't like to  have to rearrange the columns later
#frames_tocombine = [baseline_BT, baseline_CT]
#common_cols = list(set.intersection(*(set(df.columns) for df in frames_tocombine)))
#a = pd.concat([df[common_cols] for df in frames_tocombine], ignore_index=True)
###############################################################################
#================
# Evolution
#================