#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jun 29 19:44:06 2022

@author: tanu
"""
import sys, os
import pandas as pd
import numpy as np
import re
from copy import deepcopy
from sklearn import linear_model
from sklearn import datasets
from collections import Counter

from sklearn.linear_model import LogisticRegression, LogisticRegressionCV
from sklearn.linear_model import RidgeClassifier, RidgeClassifierCV, SGDClassifier, PassiveAggressiveClassifier

from sklearn.naive_bayes import BernoulliNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier, ExtraTreeClassifier
from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, AdaBoostClassifier, GradientBoostingClassifier, BaggingClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.gaussian_process import GaussianProcessClassifier, kernels
from sklearn.gaussian_process.kernels import RBF, DotProduct, Matern, RationalQuadratic, WhiteKernel

from sklearn.discriminant_analysis import LinearDiscriminantAnalysis, QuadraticDiscriminantAnalysis
from sklearn.neural_network import MLPClassifier

from sklearn.svm import SVC
from xgboost import XGBClassifier
from sklearn.naive_bayes import MultinomialNB
from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder

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

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

# added
from sklearn.model_selection import train_test_split, cross_validate, cross_val_score, LeaveOneOut, KFold, RepeatedKFold, cross_val_predict

from sklearn.model_selection import train_test_split, cross_validate, cross_val_score
from sklearn.model_selection import StratifiedKFold,RepeatedStratifiedKFold, RepeatedKFold

from sklearn.pipeline import Pipeline, make_pipeline

from sklearn.feature_selection import RFE, RFECV

import itertools
import seaborn as sns
import matplotlib.pyplot as plt

from statistics import mean, stdev, median, mode

from imblearn.over_sampling import RandomOverSampler
from imblearn.under_sampling import RandomUnderSampler
from imblearn.over_sampling import SMOTE
from sklearn.datasets import make_classification
from imblearn.combine import SMOTEENN
from imblearn.combine import SMOTETomek

from imblearn.over_sampling import SMOTENC
from imblearn.under_sampling import EditedNearestNeighbours
from imblearn.under_sampling import RepeatedEditedNearestNeighbours

from sklearn.model_selection import GridSearchCV
from sklearn.base import BaseEstimator
from sklearn.impute import KNNImputer as KNN
import json
import argparse
import re
import itertools
from sklearn.model_selection import LeaveOneGroupOut
###############################################################################
homedir = os.path.expanduser("~")
sys.path.append(homedir + '/git/LSHTM_analysis/scripts/ml/ml_functions')
sys.path
###############################################################################
#outdir = homedir + '/git/LSHTM_ML/output/combined/'
outdir = homedir + '/git/LSHTM_ML/output/test/'

#====================
# Import ML functions 
#====================
from ml_data_combined import *
from MultClfs import *

#from GetMLData import *
#from SplitTTS import *

skf_cv = StratifiedKFold(n_splits = 10 , shuffle = True, random_state = 42)
#logo = LeaveOneGroupOut()
#random_state = 42
#njobs = os.cpu_count() 
sampling_names = {"": "none", "_ros": "Oversampling", "_rus": "Undersampling", "_rouC": "Over+Under", "_smnc": "SMOTE"}
########################################################################
# COMPLETE data: No tts_split
########################################################################
#%%
def CMLogoSkf(cm_input_df
        , all_genes =  ["embb", "katg", "rpob", "pnca", "gid", "alr"]
        , bts_genes = ["embb", "katg", "rpob", "pnca", "gid"]
        #, bts_genes = ["embb"]
        , cols_to_drop = ['dst', 'dst_mode', 'gene_name']
        , target_var = 'dst_mode'
        , gene_group = 'gene_name'
        , std_gene_omit = []
        , output_dir = outdir
        , file_suffix = ""
        , random_state = 42
        , k_smote = 5
        , njobs = os.cpu_count()
        ):

    
    rs    = {'random_state': random_state}
    njobs = {'n_jobs': njobs }

    
    for bts_gene in bts_genes:
        outDict = {}

        print('\n BTS gene:', bts_gene)
        if not std_gene_omit:
            training_genesL = ['alr']
        else:
            training_genesL = []
            
        tr_gene_omit = std_gene_omit + [bts_gene]
        n_tr_genes = (len(bts_genes) - (len(std_gene_omit)))
        #n_total_genes = (len(bts_genes) - len(std_gene_omit))
        n_total_genes = len(all_genes)
        
        training_genesL = training_genesL + list(set(bts_genes) - set(tr_gene_omit))
        #training_genesL = [element for element in bts_genes if element not in tr_gene_omit]
    
        print('\nTotal genes: ', n_total_genes
              ,'\nTraining on:', n_tr_genes
              ,'\nTraining on genes:', training_genesL
              , '\nOmitted genes:', tr_gene_omit
              , '\nBlind test gene:', bts_gene) 
        
        #print('\nDim of data:', cm_input_df.shape)
            
        tts_split_type = "logo_skf_BT_" + bts_gene
        
        # if len(file_suffix) > 0:
        #     file_suffix = '_' + file_suffix
        # else:
        #     file_suffix = file_suffix

        #outFile = output_dir + str(n_tr_genes+1) + "genes_" + tts_split_type + '_' + file_suffix +  ".csv"
               
        #print(outFile)
    
        #-------
        # training
        #------
        cm_training_df = cm_input_df[~cm_input_df['gene_name'].isin(tr_gene_omit)]
        
        cm_X = cm_training_df.drop(cols_to_drop, axis=1, inplace=False)
        #cm_y = cm_training_df.loc[:,'dst_mode']
        cm_y = cm_training_df.loc[:, target_var]
    
        gene_group = cm_training_df.loc[:,'gene_name']
        
        #print('\nTraining data dim:', cm_X.shape
        #      , '\nTraining Target dim:', cm_y.shape)
        
        if all(cm_X.columns.isin(cols_to_drop) == False):
            print('\nChecked training df does NOT have Target var')
        else:
            sys.exit('\nFAIL: training data contains Target var')
        
        #---------------
        # BTS: genes
        #---------------
        cm_test_df = cm_input_df[cm_input_df['gene_name'].isin([bts_gene])]
        
        cm_bts_X = cm_test_df.drop(cols_to_drop, axis = 1, inplace = False)
        #cm_bts_y = cm_test_df.loc[:, 'dst_mode']
        cm_bts_y = cm_test_df.loc[:, target_var]
    
        #print('\nTEST data dim:'     , cm_bts_X.shape
        #      , '\nTEST Target dim:' , cm_bts_y.shape)
        
        #print("Running Multiple models on LOGO with SKF")
        # NULL, ros, rus, rouc, smnc
        
        outDict.update({'X_cm'       : cm_X
                        , 'y_cm'     : cm_y
                        , 'X_bts_cm' : cm_bts_X
                        , 'y_bts_cm' : cm_bts_y
                        })

        #######################################################################
        #                               RESAMPLING
        #######################################################################
        #------------------------------
        # Simple Random oversampling
        # [Numerical + catgeorical]
        #------------------------------
        oversample = RandomOverSampler(sampling_strategy='minority')
        X_ros, y_ros = oversample.fit_resample(cm_X, cm_y)
        # print('\nSimple Random OverSampling\n', Counter(y_ros))
        # print(X_ros.shape)
        
        #------------------------------
        # Simple Random Undersampling
        # [Numerical + catgeorical]
        #------------------------------
        undersample = RandomUnderSampler(sampling_strategy='majority')
        X_rus, y_rus = undersample.fit_resample(cm_X, cm_y)
        # print('\nSimple Random UnderSampling\n', Counter(y_rus))
        # print(X_rus.shape)
        
        #------------------------------
        # Simple combine ROS and RUS
        # [Numerical + catgeorical]
        #------------------------------
        oversample = RandomOverSampler(sampling_strategy='minority')
        X_ros, y_ros = oversample.fit_resample(cm_X, cm_y)
        
        undersample = RandomUnderSampler(sampling_strategy='majority')
        X_rouC, y_rouC = undersample.fit_resample(X_ros, y_ros)
        # print('\nSimple Combined Over and UnderSampling\n',  Counter(y_rouC))
        # print(X_rouC.shape)
        
        #------------------------------
        # SMOTE_NC: oversampling 
        # [numerical + categorical]
        #https://stackoverflow.com/questions/47655813/oversampling-smote-for-binary-and-categorical-data-in-python
        #------------------------------
        # Determine categorical and numerical features
        numerical_ix = cm_X.select_dtypes(include=['int64', 'float64']).columns
        num_featuresL = list(numerical_ix)
        numerical_colind = cm_X.columns.get_indexer(list(numerical_ix) )
        
        categorical_ix = cm_X.select_dtypes(include=['object', 'bool']).columns
        categorical_colind = cm_X.columns.get_indexer(list(categorical_ix))
        
        #k_sm = 5 # default
        k_sm = k_smote
        sm_nc = SMOTENC(categorical_features=categorical_colind, k_neighbors = k_sm
                        , **rs
                        , **njobs)
        
        X_smnc, y_smnc = sm_nc.fit_resample(cm_X, cm_y)

        outDict.update({'X_ros_cm'   : X_ros
                        , 'y_ros_cm' : y_ros
                            
                        , 'X_rus_cm' : X_rus
                        , 'y_rus_cm' : y_rus
                            
                        , 'X_rouC_cm': X_rouC
                        , 'y_rouC_cm': y_rouC
                            
                        , 'X_smnc_cm': X_smnc
                        , 'y_smnc_cm': y_smnc})

        #%%:Running Multiple models on LOGO with SKF
#        cD3_v2 = MultModelsCl_logo_skf(input_df = cm_X # two func were identical excpet for name
        for i in sampling_names.keys():
            print("thing:", "X"+i+"_cm", "y"+i+"_cm")
            
            current_X = "X" + i + "_cm"
            current_y = "y" + i + "_cm"
            
            current_X_df = outDict[current_X]
            current_y_df = outDict[current_y]
            
            
            cD3_v2 = MultModelsCl(input_df = current_X_df       
                            , target = current_y_df
                            , sel_cv = skf_cv
                            , tts_split_type = tts_split_type                     
                            , resampling_type = sampling_names[i] # 'none' # default
                            , add_cm = True 
                            , add_yn = True 
                            , var_type = 'mixed'
                            , scale_numeric = ['min_max'] 
                            , run_blind_test = True
                            , blind_test_df = cm_bts_X
                            , blind_test_target = cm_bts_y
                            , return_formatted_output = True
                            , random_state = 42
                            , n_jobs = os.cpu_count() # the number of jobs should equal the number of CPU cores
                            )
            outFile = output_dir + str(n_tr_genes+1) + "genes_" + tts_split_type + '_' + file_suffix + i + ".csv"

        
            cD3_v2.to_csv(outFile)
        
        
        # outDict.update({'X'       : cm_X
        #                 , 'y'     : cm_y
        #                 , 'X_bts' : cm_bts_X
        #                 , 'y_bts' : cm_bts_y
        #                 })
        
        # return(outDict)
        
#%%  RUN
#===============
# Complete Data
#==============
CMLogoSkf(cm_input_df = combined_df,file_suffix = "complete")
CMLogoSkf(cm_input_df = combined_df, std_gene_omit=['alr'], file_suffix = "complete")

#===============
# Actual Data
#===============
#CMLogoSkf(cm_input_df = combined_df_actual, file_suffix = "actual")
#CMLogoSkf(cm_input_df = combined_df_actual, std_gene_omit=['alr'], file_suffix = "actual")