import pandas as pd
import os, sys
import numpy as np
from sklearn.datasets import load_boston 
from sklearn.ensemble import RandomForestRegressor 
from sklearn.model_selection import train_test_split
from sklearn.feature_selection import RFECV
import matplotlib.pyplot as plt
###############################################################################
homedir = os.path.expanduser("~")
sys.path.append(homedir + '/git/LSHTM_analysis/scripts/ml/ml_functions')
sys.path

# import
from GetMLData import *
from SplitTTS import *
#from MultClfs_fi import *
from MultClfs import *

#%%
# X,y = load_boston(return_X_y=True) 
# features = load_boston()['feature_names']

# X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.33, random_state=42)

# rf = RandomForestRegressor(random_state=0) 
# rf.fit(X_train,y_train)


# f_i = list(zip(features,rf.feature_importances_)) 
# f_i.sort(key = lambda x : x[1])
# plt.barh([x[0] for x in f_i],[x[1] for x in f_i]) 
# plt.show()

#%%

skf_cv = StratifiedKFold(n_splits = 10
                            , shuffle = True,**rs)
#sel_cv = logo
# sel_cv = RepeatedStratifiedKFold(n_splits = 5
#                                   , n_repeats = 3
#                                   , **rs)
# param dict for getmldata()
gene_model_paramD = {'data_combined_model'   : False
                    , 'use_or'                   : False
                    , 'omit_all_genomic_features': False
                    , 'write_maskfile'           : False
                    , 'write_outfile'            : False }

#df = getmldata(gene, drug, **gene_model_paramD)
df = getmldata('pncA', 'pyrazinamide', **gene_model_paramD)
df = getmldata('embB', 'ethambutol'   , **gene_model_paramD)
df = getmldata('katG', 'isoniazid'    , **gene_model_paramD)
df = getmldata('rpoB', 'rifampicin'   , **gene_model_paramD)
df  = getmldata('gid' , 'streptomycin' , **gene_model_paramD)
#df  = getmldata('alr' , 'cycloserine'  , **combined_model_paramD)
all(df.columns.isin(['gene_name'])) # should be False


spl_type = '70_30'
spl_type = '80_20'
spl_type = 'sl'

df2 = split_tts(df
          , data_type = 'actual'
          , split_type = spl_type
          , oversampling = False
          , dst_colname = 'dst'
          , target_colname = 'dst_mode'
          , include_gene_name = True
          , random_state = 42 # default
      )

all(df2['X'].columns.isin(['gene_name'])) # should be False

fooD = MultModelsCl(input_df = df2['X']
                , target = df2['y']
                , sel_cv = skf_cv
                , run_blind_test = True
                , blind_test_df =  df2['X_bts']
                , blind_test_target =  df2['y_bts']
                , tts_split_type  = spl_type
                , resampling_type = 'none' # default
                , var_type = ['mixed']
                , scale_numeric = ['min_max']
                , return_formatted_output = False

                )

for k, v in fooD.items():
    print('\nModel:', k
          , '\nTRAIN MCC:', fooD[k]['test_mcc']
          ,  '\nBTS MCC:' , fooD[k]['bts_mcc']
          , '\nDIFF:',fooD[k]['bts_mcc'] - fooD[k]['test_mcc'] )
    
#%% CHECK SCALING
embb_df = getmldata('embB', 'ethambutol'   , **combined_model_paramD)
all(embb_df.columns.isin(['gene_name'])) # should be False

scaler = MinMaxScaler(feature_range=(-1, 1))
bar = embb_df[['vdwclashes_rr', 'electro_rr']]
bar_df1 = scaler.fit_transform(bar)
bar_df1 = pd.DataFrame(bar_df1)
bar_df1.rename(columns = {0:'vdw_scaled', 1: 'ele_scaled'}, inplace = True)
bar2 = pd.concat([bar, bar_df1], axis = 1)


scaler2 = StandardScaler()
baz = embb_df[['vdwclashes_rr', 'electro_rr']]
baz_df1 = scaler2.fit_transform(baz)
baz_df1 = pd.DataFrame(baz_df1)
baz_df1.rename(columns = {0:'vdw_scaled', 1: 'ele_scaled'}, inplace = True)
baz2 = pd.concat([baz, baz_df1], axis = 1)

a = pd.concat([bar2, baz2], axis = 1)