#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon May 16 05:59:12 2022

@author: tanu
"""
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 15 11:09:50 2022

@author: tanu
"""
#%% Logistic Regression + hyperparam + FS: BaseEstimator: ClfSwitcher()
model_lr = LogisticRegression(**rs)
model_rfecv = RFECV(estimator = model_lr
                    , cv = rskf_cv
                    #, cv = 10
                    , scoring = 'matthews_corrcoef'
                    )

param_grid2 = [
    {
        #'clf__estimator': [LogisticRegression(**rs)],
        #'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
        'C': np.logspace(0, 4, 10),
        'penalty': ['none', 'l1', 'l2', 'elasticnet'],
        'max_iter': list(range(100,800,100)),
        'solver': ['saga']
    },
    {
        #'clf__estimator': [LogisticRegression(**rs)],
        #'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
        'C': np.logspace(0, 4, 10),
        'penalty': ['l2', 'none'],
        'max_iter': list(range(100,800,100)),
        'solver': ['newton-cg', 'lbfgs', 'sag']
    }, 
    {
        #'clf__estimator': [LogisticRegression(**rs)],
        #'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
        'C': np.logspace(0, 4, 10),
        'penalty': ['l1', 'l2'],
        'max_iter': list(range(100,800,100)),
        'solver': ['liblinear']
    }

]    

#-------------------------------------------------------------------------------
# Grid search CV + FS
gscv_lr = GridSearchCV(model_lr
                    , param_grid2
                    , scoring = mcc_score_fn, refit = 'mcc'
                    , cv = skf_cv
                    , return_train_score = False
                    , verbose = 3
                    , **njobs)

#------------------------------------------------------------------------------
# Create pipeline
pipeline = Pipeline([('pre', MinMaxScaler())
                     #, ('feature_selection', sfs_selector)
                     , ('feature_selection', model_rfecv )
                     , ('clf', gscv_lr)])
  
# Fit
lr_fs_fit = pipeline.fit(X,y)
#lr_fs_fit_be_mod = lr_fs_fit.best_params_
#lr_fs_fit_be_res = lr_fs_fit.cv_results_
dir(lr_fs_fit)

print('Best model:\n', lr_fs_fit_be_mod)
print('Best models score:\n', lr_fs_fit.best_score_, ':' , round(lr_fs_fit.best_score_, 2))

pipeline.predict(X_bts)
lr_fs_fit.predict(X_bts)

test_predict = pipeline.predict(X_bts)
print(test_predict)
print(np.array(y_bts))
#y_btsf = np.array(y_bts)

print(accuracy_score(y_bts, test_predict))
print(matthews_corrcoef(y_bts, test_predict))


######################################
# Blind test
######################################
# See how it does on the BLIND test
#print('\nBlind test score, mcc:', )) 

test_predict = lr_fs_fit.predict(X_bts)
print(test_predict)
print(np.array(y_bts))
y_btsf = np.array(y_bts)

print(accuracy_score(y_bts, test_predict))
print(matthews_corrcoef(y_bts, test_predict))

# create a dict with all scores
lr_bts_dict = {#'best_model': list(gscv_lr_fit_be_mod.items())
               'bts_fscore':None
               , 'bts_mcc':None
               , 'bts_precision':None
               , 'bts_recall':None
               , 'bts_accuracy':None
               , 'bts_roc_auc':None
               , 'bts_jaccard':None }
lr_bts_dict
lr_bts_dict['bts_fscore']    = round(f1_score(y_bts, test_predict),2)
lr_bts_dict['bts_mcc']       = round(matthews_corrcoef(y_bts, test_predict),2)
lr_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
lr_bts_dict['bts_recall']    = round(recall_score(y_bts, test_predict),2)
lr_bts_dict['bts_accuracy']  = round(accuracy_score(y_bts, test_predict),2)
lr_bts_dict['bts_roc_auc']   = round(roc_auc_score(y_bts, test_predict),2)
lr_bts_dict['bts_jaccard']   = round(jaccard_score(y_bts, test_predict),2)
lr_bts_dict

# Create a df from dict with all scores
lr_bts_df = pd.DataFrame.from_dict(lr_bts_dict,orient = 'index')
lr_bts_df.columns = ['Logistic_Regression']
print(lr_bts_df)

# d2 = {'best_model_params': lis(gscv_lr_fit_be_mod.items() )}
# d2
# def Merge(dict1, dict2):
#     res = {**dict1, **dict2}
#     return res
# d3 = Merge(d2, lr_bts_dict)
# d3

# Create df with best model params
model_params = pd.Series(['best_model_params',  list(lr_fs_fit_be_mod.items() )])
model_params_df = model_params.to_frame()
model_params_df
model_params_df.columns = ['Logistic_Regression']
model_params_df.columns

# Combine the df of scores and the best model params
lr_bts_df.columns
lr_output = pd.concat([model_params_df, lr_bts_df], axis = 0)
lr_output

# Format the combined df
# Drop the best_model_params row from lr_output
lr_df = lr_output.drop([0], axis = 0)
lr_df

#FIXME: tidy the index of the formatted df

###############################################################################