#!/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
import pprint as pp
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.ensemble import AdaBoostClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.neural_network import MLPClassifier
from xgboost import XGBClassifier
from sklearn.naive_bayes import MultinomialNB
from sklearn.linear_model import SGDClassifier
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, jaccard_score
from sklearn.metrics import jaccard_score

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

#from imblearn.over_sampling import RandomOverSampler
#from imblearn.over_sampling import SMOTE
#from imblearn.pipeline import Pipeline
#from sklearn.datasets import make_classification
from sklearn.model_selection import cross_validate, cross_val_score
from sklearn.model_selection import RepeatedStratifiedKFold
from sklearn.ensemble import AdaBoostClassifier
#from imblearn.combine import SMOTEENN
#from imblearn.under_sampling import EditedNearestNeighbours

from sklearn.model_selection import GridSearchCV
from sklearn.base import BaseEstimator

scoring_fn =  ({'accuracy'      : make_scorer(accuracy_score)
                 , 'fscore'     : make_scorer(f1_score)
                 , 'mcc'        : make_scorer(matthews_corrcoef)
                 ,  'precision' : make_scorer(precision_score)
                 ,  'recall'    : make_scorer(recall_score)
                 ,  'roc_auc'   : make_scorer(roc_auc_score)
                 , 'jcc'        : make_scorer(jaccard_score)
            }) 
  
rs = {'random_state': 42}
njobs = {'n_jobs': 10}
skf_cv = StratifiedKFold(n_splits = 10
                          #, shuffle = False, random_state= None)
                           , shuffle = True,**rs)

rskf_cv = RepeatedStratifiedKFold(n_splits = 10
                                  , n_repeats=3
                                 #, shuffle = False, random_state= None)
                                 #, shuffle = True
                                 ,**rs)
#my_mcc = make_scorer({'mcc':make_scorer(matthews_corrcoef})
mcc_score_fn = {'mcc': make_scorer(matthews_corrcoef)}

#%%
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 MultClassPipeSKFLoop
from MultClassPipe3 import MultClassPipeSKFCV

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, index_col = 0)
my_df.dtypes
my_df_cols = my_df.columns

geneL_basic = ['pnca']

# -- CHECK script -- imports.py
#%% get cols
mycols = my_df.columns
mycols

# change from numberic to 
num_type = ['int64', 'float64']
cat_type = ['object', 'bool']

# TODO:
# Treat active site aa pos as category and not numerical: This needs to be part of merged_df3!
#if my_df['active_aa_pos'].dtype in num_type:
#    my_df['active_aa_pos'] = my_df['active_aa_pos'].astype(object)
#    my_df['active_aa_pos'].dtype

# -- CHECK script -- imports.py
#%%============================================================================
#%% IMPUTE values for OR

#%% Combine mmCSM_lig Data

#%% Combine PROVEAN data

#%% Combine ED logo data

#%% Masking columns (mCSM-lig, mCSM-NA, mCSM-ppi2) values for lig_dist >10

# get logic from upstream!
my_df_ml = my_df.copy()

my_df_ml['mutationinformation'][my_df['ligand_distance']>10].value_counts()
my_df_ml.groupby('mutationinformation')['ligand_distance'].apply(lambda x: (x>10)).value_counts()
my_df_ml.groupby(['mutationinformation'])['ligand_distance'].apply(lambda x: (x>10)).value_counts()

my_df_ml.loc[(my_df_ml['ligand_distance'] > 10), 'ligand_affinity_change'] = 0
(my_df_ml['ligand_affinity_change'] == 0).sum()

#%%============================================================================
# Separate blind test set
my_df_ml[drug].isna().sum()

blind_test_df = my_df_ml[my_df_ml[drug].isna()]
blind_test_df.shape

training_df =  my_df_ml[my_df_ml[drug].notna()]
training_df.shape

# Target1: dst
training_df[drug].value_counts()
training_df['dst_mode'].value_counts()

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

foldX_cols = ['contacts'
#, 'electro_rr', 'electro_mm', 'electro_sm', 'electro_ss'
#, 'disulfide_rr', 'disulfide_mm', 'disulfide_sm', 'disulfide_ss'
#, 'hbonds_rr', 'hbonds_mm', 'hbonds_sm', 'hbonds_ss'
#, 'partcov_rr', 'partcov_mm', 'partcov_sm', 'partcov_ss'
#, 'vdwclashes_rr', 'vdwclashes_mm', 'vdwclashes_sm', 'vdwclashes_ss'
#, 'volumetric_rr', 'volumetric_mm', 'volumetric_ss'
]

X_strFN =  ['rsa'
           #, 'asa'
           , 'kd_values'
           , 'rd_values']    

X_evolFN =  ['consurf_score'
           , 'snap2_score']

# quick inspection which lineage to use:
#foo = my_df_ml[['lineage', 'lineage_count_all', 'lineage_count_unique']]
    
X_genomicFN =  ['maf'
#            , 'or_mychisq'
#            , 'or_logistic'
#            , 'or_fisher'
#            , 'pval_fisher'
             #, 'lineage'
             #, 'lineage_count_all'
             #, 'lineage_count_unique'
]

#%% Construct numerical and categorical column names

# numerical feature names
numerical_FN = common_cols_stabiltyN + foldX_cols + X_strFN + X_evolFN + X_genomicFN 

#categorical feature names
categorical_FN = ['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'
             ]

#%% extracting dfs based on numerical, categorical column names
#----------------------------------
# WITHOUT the target var included
#----------------------------------
num_df = training_df[numerical_FN]
num_df.shape

cat_df = training_df[categorical_FN]
cat_df.shape

all_df = training_df[numerical_FN + categorical_FN]
all_df.shape

#------------------------------
# WITH the target var included:
    #'wtgt': with target
#------------------------------
# drug and dst_mode should be the same thing
num_df_wtgt = training_df[numerical_FN + ['dst_mode']]
num_df_wtgt.shape

cat_df_wtgt = training_df[categorical_FN + ['dst_mode']]
cat_df_wtgt.shape

all_df_wtgt = training_df[numerical_FN + categorical_FN + ['dst_mode']]
all_df_wtgt.shape
#%%================================================================
#%% Apply ML
#TODO: A

#%% Data
#X = all_df_wtgt[numerical_FN+categorical_FN]
X = all_df_wtgt[numerical_FN]
y = all_df_wtgt['dst_mode']

#Blind test data {same format}
X_bts = blind_test_df[numerical_FN]
y_bts = blind_test_df['dst_mode']

X_bts_wt = blind_test_df[numerical_FN + ['dst_mode']] 

# Quick check
(X['ligand_affinity_change']==0).sum() == (X['ligand_distance']>10).sum()
#%% MultClassPipeSKFCV: function call()
# mm_skf_scoresD = MultClassPipeSKFCV(input_df = X
#                                         , target = y
#                                         , var_type = 'numerical'
#                                         , skf_cv = skf_cv)


# mm_skf_scores_df_all = pd.DataFrame(mm_skf_scoresD)
# mm_skf_scores_df_all
# mm_skf_scores_df_test = mm_skf_scores_df_all.filter(like='test_', axis=0)
# mm_skf_scores_df_train = mm_skf_scores_df_all.filter(like='train_', axis=0) # helps to see if you trust the results
# print(mm_skf_scores_df_train)
# print(mm_skf_scores_df_test)