#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Mar  4 14:54:30 2022

@author: tanu
"""
import os, sys
import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import BernoulliNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.pipeline import Pipeline
from xgboost import XGBClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score, roc_auc_score, roc_curve, f1_score
#%%
homedir = os.path.expanduser("~")
os.chdir(homedir + "/git/ML_AI_training/")

# my function
from MultClassPipe import MultClassPipeline 

#gene = 'pncA'
#drug = 'pyrazinamide'

#==============
# directories
#==============
datadir = homedir + '/git/Data/'
indir   = datadir + drug + '/input/'
outdir  = datadir + drug + '/output/'

#=======
# input
#=======
infile_ml1 = outdir + gene.lower() + '_merged_df3.csv' 
#infile_ml2 = outdir + gene.lower() + '_merged_df2.csv'

my_df = pd.read_csv(infile_ml1)
my_df.dtypes
my_df_cols = my_df.columns

geneL_basic = ['pnca']
geneL_na   = ['gid']
geneL_na_ppi2   = ['rpob']
geneL_ppi2 = ['alr', 'embb', 'katg']
#%% get cols
mycols = my_df.columns

#%%============================================================================
# GET Y

# Target1: mutation_info_labels
dm_om_map = {'DM': 1, 'OM': 0}
target1 = my_df['mutation_info_labels'].map(dm_om_map)

# Target2: drug
drug_labels = drug + '_labels'
drug_labels
my_df[drug_labels] = my_df[drug].map({1: 'resistant', 0: 'sensitive'})
my_df[drug_labels].value_counts()
my_df[drug_labels] = my_df[drug_labels].fillna('unknown')
my_df[drug_labels].value_counts()
target2 = my_df[drug_labels]

# Target3: drtype
drtype_labels = 'drtype_labels'
my_df[drtype_labels] = my_df['drtype'].map({'Sensitive' : 0
                                                 , 'Other'   : 0
                                                 , 'Pre-MDR' : 1
                                                 , 'MDR'     : 1
                                                 , 'Pre-XDR' : 1
                                                 , 'XDR'     : 1})
# target3 = my_df['drtype']
target3 = my_df[drtype_labels]

# sanity checks
target1.value_counts()
my_df['mutation_info_labels'].value_counts()

target2.value_counts()
my_df[drug_labels].value_counts()

target3.value_counts()
my_df['drtype'].value_counts()

#%%
# GET X
common_cols_stabilty = ['ligand_distance'
           , 'ligand_affinity_change'
           , 'duet_stability_change'
           , 'ddg_foldx'
           , 'deepddg'
           , 'ddg_dynamut2']

# Build stability columns ~ gene
if gene.lower() in geneL_basic:
    x_stability_cols = common_cols_stabilty
    
if gene.lower() in geneL_ppi2:
    x_stability_cols = common_cols_stabilty + ['mcsm_ppi2_affinity'
                                               , 'interface_dist'] 
if gene.lower() in geneL_na:
    x_stability_cols = common_cols_stabilty + ['mcsm_na_affinity'] 

if gene.lower() in geneL_na_ppi2:
    x_stability_cols = common_cols_stabilty + ['mcsm_na_affinity'] + ['mcsm_ppi2_affinity', 'interface_dist'] 
    #D1148 get rid of
    na_index = my_df['mutationinformation'].index[my_df['mcsm_na_affinity'].apply(np.isnan)]
    my_df = my_df.drop(index=na_index)
    
X_strF =  ['asa'
           , 'rsa'
           , 'kd_values'
           , 'rd_values']    

X_evolF =  ['consurf_score'
           , 'snap2_score'
           , 'snap2_accuracy_pc']

# TODO: ADD ED values
# Problematic due to NA
# X_genomicF =  ['af'
#            , 'or_mychisq'
#            , 'or_logistic'
#            , 'or_fisher'
#            , 'pval_fisher']

#%% try combinations
X_vars1 = my_df[x_stability_cols] 
X_vars2 = my_df[X_strF] 
X_vars3 = my_df[X_evolF] 
#X_vars4 = my_df[X_genomicF] 
#X_vars4 = X_vars4.fillna('unknown') # need one hot encoder!

X_vars5  = my_df[x_stability_cols + X_strF]
X_vars6  = my_df[x_stability_cols + X_evolF]
#X_vars7  = my_df[x_stability_cols + X_genomicF]
X_vars8  = my_df[X_strF + X_evolF]
#X_vars9  = my_df[X_strF + X_genomicF]
#X_vars10 = my_df[X_evolF + X_genomicF]
X_vars11 = my_df[x_stability_cols + X_strF + X_evolF ]
#X_vars12 = my_df[x_stability_cols + X_strF + X_evolF + X_genomicF]

#%%
X_vars1.shape[1]

# TODO: stratified cross validate
# Train-test Split

# TARGET1
X_train, X_test, y_train, y_test = train_test_split(X_vars1, 
                                                    target1, 
                                                    test_size = 0.33, 
                                                    random_state = 42)
MultClassPipeline(X_train, X_test, y_train, y_test)

# TARGET3
X_train3, X_test3, y_train3, y_test3 = train_test_split(X_vars5, 
                                                    target3, 
                                                    test_size = 0.33, 
                                                    random_state = 42)
MultClassPipeline(X_train3, X_test3, y_train3, y_test3)