#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Mar  6 13:41:54 2022

@author: tanu
"""
import os, sys
import pandas as pd
import numpy as np
#from copy import deepcopy
from sklearn import linear_model
from sklearn.linear_model import LogisticRegression, LinearRegression
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 xgboost import XGBClassifier
from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder

from sklearn.compose import ColumnTransformer
from sklearn.compose import make_column_transformer

from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score 
from sklearn.metrics import roc_auc_score, roc_curve, f1_score, matthews_corrcoef
from sklearn.metrics import make_scorer
from sklearn.metrics import classification_report


from sklearn.metrics import average_precision_score

from sklearn.model_selection import cross_validate
from sklearn.model_selection import train_test_split
from sklearn.model_selection import StratifiedKFold

from sklearn.pipeline import Pipeline
from sklearn.pipeline import make_pipeline

from sklearn.feature_selection import RFE
from sklearn.feature_selection import RFECV

import itertools
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
print(np.__version__)
print(pd.__version__)
from statistics import mean, stdev, median, mode

#%%
homedir = os.path.expanduser("~")
os.chdir(homedir + "/git/ML_AI_training/")

# my function
from MultClassPipe import MultClassPipeline
from MultClassPipe2 import MultClassPipeline2
from loopity_loop import MultClassPipeSKF

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

my_df['active_aa_pos'].dtype
my_df['active_aa_pos'] = my_df['active_aa_pos'].astype(object)

if gene.lower() in geneL_na_ppi2:
    #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)

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

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

# 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 [Binary]
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 = 'drtype' [Multinomial]
target3 = my_df[drtype_labels]

# target4
drtype_labels2 = 'drtype_labels2'
my_df[drtype_labels2] = my_df['drtype'].map({'Sensitive'     : 0
                                                 , 'Other'   : 0
                                                 , 'Pre-MDR' : 1
                                                 , 'MDR'     : 1
                                                 , 'Pre-XDR' : 2
                                                 , 'XDR'     : 2})
target4 = my_df[drtype_labels2]

# 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()
target4.value_counts()
my_df['drtype'].value_counts()

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

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

if gene.lower() in geneL_na_ppi2:
    x_stabilityN = common_cols_stabiltyN + ['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_strFN =  ['asa'
           , 'rsa'
           , 'kd_values'
           , 'rd_values']    

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

# TODO: ADD ED values
# Problematic due to NA: filling NA with unknown or string will make it categorical
# OPTIONS
# 1. Imputing: KNN or MICE or from distribution
# 2. Fill na with median or mode
# 3. Separate datset without including genomic features AT ALL for ML, then using this as a 'blind test set'
    # this means the size of the training data gets reduced!
# 4. Remove genomic features from ML COMPLETELEY!

# X_genomicFN =  ['af'
#            , 'or_mychisq'
#            , 'or_logistic'
#            , 'or_fisher'
#            , 'pval_fisher']

#%% try combinations
X_vars1 = my_df[x_stabilityN] 
X_vars2 = my_df[X_strFN] 
X_vars3 = my_df[X_evolFN] 

X_vars5  = my_df[x_stabilityN + X_strFN]
X_vars6  = my_df[x_stabilityN + X_evolFN]
#X_vars7  = my_df[x_stabilityN + X_genomicFN]
X_vars8  = my_df[X_strFN + X_evolFN]
#X_vars9  = my_df[X_strFN + X_genomicFN]
#X_vars10 = my_df[X_evolFN + X_genomicFN]
X_vars11 = my_df[x_stabilityN + X_strFN + X_evolFN]
#X_vars12 = my_df[x_stabilityN + X_strFN + X_evolFN + X_genomicFN]

numerical_features_names = x_stabilityN + X_strFN + X_evolFN

# separate ones for foldx?
categorical_features_names = ['ss_class'
                           , 'wt_prop_water'
                          # , 'lineage_labels' # misleading if using merged_df3
                           , 'mut_prop_water'
                           , 'wt_prop_polarity'
                           , 'mut_prop_polarity'
                           , 'wt_calcprop'
                           , 'mut_calcprop'
                           , 'active_aa_pos']

numerical_features_df = my_df[numerical_features_names]
numerical_features_df.shape

categorical_features_df = my_df[categorical_features_names]
categorical_features_df.shape

all_features_df = my_df[numerical_features_names + categorical_features_names]
all_features_df.shape