#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon May 23 23:25:26 2022

@author: tanu
"""

#####################################
def fsgs(input_df
         , target
         , blind_test_df = pd.DataFrame()
         , blind_test_target = pd.Series(dtype = 'int64')
         #, y_trueS = pd.Series()
         , estimator = LogisticRegression(**rs)
         , param_gridLd = {}
         , cv_method =  StratifiedKFold(n_splits = 10
                                    , shuffle = True,**rs)
         , var_type = ['numerical'
                     , 'categorical'
                     , 'mixed']
         #, fs_estimator = [LogisticRegression(**rs)]
         , fs = RFECV(DecisionTreeClassifier(**rs) 
                      , cv =  StratifiedKFold(n_splits = 10
                                    , shuffle = True,**rs)
                      , scoring = 'matthews_corrcoef')
         ):
    '''
    returns
    Dict containing results from FS and hyperparam tuning for a given estiamtor
    >>> ADD MORE <<<
    optimised/selected based on mcc
    '''
    # Determine categorical and numerical features
    numerical_ix = input_df.select_dtypes(include=['int64', 'float64']).columns
    numerical_ix
    categorical_ix = input_df.select_dtypes(include=['object', 'bool']).columns
    categorical_ix    
    
    # Determine preprocessing steps ~ var_type
    if var_type == 'numerical':
        t = [('num', MinMaxScaler(), numerical_ix)]
    
    if var_type == 'categorical':
        t = [('cat', OneHotEncoder(), categorical_ix)]
    
    if var_type == 'mixed':
        t = [('cat', OneHotEncoder(), categorical_ix)
              , ('num', MinMaxScaler(), numerical_ix)]
        
    col_transform = ColumnTransformer(transformers = t
                                        , remainder='passthrough')
    
    ###########################################################################
    #=================
    # Create var_type ~ column names
    # using one hot encoder with RFECV means the names internally are lost
    # Hence fit col_transformeer to my input_df and get all the column names 
    # out and stored in a var to allow the 'selected features' to be subsetted
    # from the numpy boolean array
    #=================
    col_transform.fit(input_df)
    col_transform.get_feature_names_out()
    
    var_type_colnames = col_transform.get_feature_names_out()
    var_type_colnames = pd.Index(var_type_colnames)
    
    if var_type == 'mixed':
        print('\nVariable type is:', var_type
              , '\nNo. of columns in input_df:', len(input_df.columns)
              , '\nNo. of columns post one hot encoder:', len(var_type_colnames))
    else:
        print('\nNo. of columns in input_df:', len(input_df.columns))
    
    ############################################################################   
    # Create Pipeline object
    pipe = Pipeline([
        ('pre', col_transform),
        ('fs', fs),
        #('clf',  LogisticRegression(**rs))])
        ('clf', estimator)])
    ############################################################################   
    # Define GridSearchCV
    gscv_fs = GridSearchCV(pipe
                           , param_gridLd
                           , cv = cv_method
                           , scoring = mcc_score_fn
                           , refit = 'mcc'
                           , verbose = 1
                           , return_train_score = True
                           , **njobs)
    
    gscv_fs.fit(input_df, target)
    
    ###########################################################################
    # Get best param and scores out
    gscv_fs.best_params_
    gscv_fs.best_score_
    
    # Training best score corresponds to the max of the mean_test<score>
    train_bscore = round(gscv_fs.best_score_, 2); train_bscore
    print('\nTraining best score (MCC):', train_bscore)
    gscv_fs.cv_results_['mean_test_mcc']
    round(gscv_fs.cv_results_['mean_test_mcc'].max(),2)
    round(np.nanmax(gscv_fs.cv_results_['mean_test_mcc']),2)
    
    check_train_score = [round(gscv_fs.cv_results_['mean_test_mcc'].max(),2)
                        , round(np.nanmax(gscv_fs.cv_results_['mean_test_mcc']),2)]
    
    check_train_score = np.nanmax(check_train_score)
    
    # Training results
    gscv_tr_resD = gscv_fs.cv_results_
    mod_refit_param =  gscv_fs.refit
    
    # sanity check
    if train_bscore == check_train_score:
        print('\nVerified training score (MCC):', train_bscore )
    else:
        print('\nTraining score could not be internatlly verified. Please check training results dict')
    
    #-------------------------
    # Blind test: REAL check!
    #-------------------------
    #tp = gscv_fs.predict(X_bts)
    tp = gscv_fs.predict(blind_test_df)

    print('\nMCC on Blind test:'     , round(matthews_corrcoef(blind_test_target, tp),2))
    print('\nAccuracy on Blind test:', round(accuracy_score(blind_test_target, tp),2))
    
    #=================
    # info extraction
    #=================
    # gives input vals??
    gscv_fs._check_n_features
    
    # gives gscv params used
    gscv_fs._get_param_names()
    
    # gives ??
    gscv_fs.best_estimator_
    gscv_fs.best_params_ # gives best estimator params as a dict 
    gscv_fs.best_estimator_._final_estimator # similar to above, doesn't contain max_iter
    gscv_fs.best_estimator_.named_steps['fs'].get_support()
    gscv_fs.best_estimator_.named_steps['fs'].ranking_ # array of ranks for the features
    
    gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.mean()
    gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.max()
    #gscv_fs.best_estimator_.named_steps['fs'].grid_scores_
    
    ############################################################################
    #============
    # FS results
    #============
    # Now get the features out
    
    #--------------
    # All features 
    #--------------
    all_features = gscv_fs.feature_names_in_
    n_all_features =  gscv_fs.n_features_in_
    #all_features = gsfit.feature_names_in_
    
    #--------------
    # Selected features by the classifier
    # Important to have var_type_colnames here
    #----------------
    #sel_features = X.columns[gscv_fs.best_estimator_.named_steps['fs'].get_support()] 3 only for numerical df    
    sel_features = var_type_colnames[gscv_fs.best_estimator_.named_steps['fs'].get_support()]
    n_sf = gscv_fs.best_estimator_.named_steps['fs'].n_features_
    
    #--------------
    # Get model name
    #--------------
    model_name     = gscv_fs.best_estimator_.named_steps['clf']
    b_model_params = gscv_fs.best_params_
    
    print('\n========================================'
          , '\nRunning model:'
          , '\nModel name:', model_name
          , '\n==============================================='
          , '\nRunning feature selection with RFECV for model'
          , '\nTotal no. of features in model:', len(all_features)
          , '\nThese are:\n',  all_features, '\n\n'
          , '\nNo of features for best model: ', n_sf
          , '\nThese are:', sel_features, '\n\n'
          , '\nBest Model hyperparams:', b_model_params
          )
    
    ###############################################################################
    ############################## OUTPUT #########################################
    ###############################################################################
    #=========================
    # Blind test: BTS results
    #=========================
    # Build the final results with all scores for a feature selected model
    #bts_predict = gscv_fs.predict(X_bts)
    bts_predict = gscv_fs.predict(blind_test_df)

    print('\nMCC on Blind test:'     , round(matthews_corrcoef(blind_test_target, bts_predict),2))
    print('\nAccuracy on Blind test:', round(accuracy_score(blind_test_target, bts_predict),2))
    bts_mcc_score = round(matthews_corrcoef(blind_test_target, bts_predict),2)
    
    # Diff b/w train and bts test scores
    train_test_diff = train_bscore - bts_mcc_score
    print('\nDiff b/w train and blind test score (MCC):', train_test_diff)
    
    
    # create a dict with all scores
    lr_btsD = {#'best_model': list(gscv_lr_fit_be_mod.items())
                   #'bts_mcc':None
                    'bts_fscore':None
                   , 'bts_precision':None
                   , 'bts_recall':None
                   , 'bts_accuracy':None
                   , 'bts_roc_auc':None
                   , 'bts_jaccard':None}
    
    
    lr_btsD
    #lr_btsD['bts_mcc']       = bts_mcc_score
    lr_btsD['bts_fscore']    = round(f1_score(blind_test_target, bts_predict),2)
    lr_btsD['bts_precision'] = round(precision_score(blind_test_target, bts_predict),2)
    lr_btsD['bts_recall']    = round(recall_score(blind_test_target, bts_predict),2)
    lr_btsD['bts_accuracy']  = round(accuracy_score(blind_test_target, bts_predict),2)
    lr_btsD['bts_roc_auc']   = round(roc_auc_score(blind_test_target, bts_predict),2)
    lr_btsD['bts_jaccard']   = round(jaccard_score(blind_test_target, bts_predict),2)
    lr_btsD
    
    #===========================
    # Add FS related model info
    #===========================
    model_namef = str(model_name)
    # FIXME: doesn't tell you which it has chosen
    fs_methodf = str(gscv_fs.best_estimator_.named_steps['fs'])
    all_featuresL = list(all_features)
    fs_res_arrayf = str(list( gscv_fs.best_estimator_.named_steps['fs'].get_support()))
    fs_res_array_rankf = str(list( gscv_fs.best_estimator_.named_steps['fs'].ranking_))
    sel_featuresf = list(sel_features)
    n_sf = int(n_sf)
    
    output_modelD = {'model_name': model_namef
                     , 'model_refit_param': mod_refit_param
                     , 'Best_model_params': b_model_params 
                     , 'n_all_features': n_all_features
                     , 'fs_method': fs_methodf
                     , 'fs_res_array': fs_res_arrayf
                     , 'fs_res_array_rank': fs_res_array_rankf
                     , 'all_feature_names': all_featuresL
                     , 'n_sel_features': n_sf
                     , 'sel_features_names': sel_featuresf}
    output_modelD
    
    #========================================
    # Update output_modelD with bts_results
    #========================================
    output_modelD.update(lr_btsD)
    output_modelD
    
    output_modelD['train_score (MCC)'] = train_bscore
    output_modelD['bts_mcc'] = bts_mcc_score
    output_modelD['train_bts_diff'] = round(train_test_diff,2)
    print(output_modelD)
    
    return(output_modelD)