#!/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
"""
parameters = [
    {
        'clf__estimator': [LogisticRegression(**rs)],
        #'clf__estimator__C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
        'clf__estimator__C': np.logspace(0, 4, 10),
        'clf__estimator__penalty': ['none', 'l1', 'l2', 'elasticnet'],
        'clf__estimator__max_iter': list(range(100,800,100)),
        'clf__estimator__solver': ['saga']
    },
    {
        'clf__estimator': [LogisticRegression(**rs)],
        #'clf__estimator__C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
        'clf__estimator__C': np.logspace(0, 4, 10),
        'clf__estimator__penalty': ['l2', 'none'],
        'clf__estimator__max_iter': list(range(100,800,100)),
        'clf__estimator__solver': ['newton-cg', 'lbfgs', 'sag']
    }, 
    {
        'clf__estimator': [LogisticRegression(**rs)],
        #'clf__estimator__C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
        'clf__estimator__C': np.logspace(0, 4, 10),
        'clf__estimator__penalty': ['l1', 'l2'],
        'clf__estimator__max_iter': list(range(100,800,100)),
        'clf__estimator__solver': ['liblinear']
    }

]    

# Create pipeline
pipeline = Pipeline([
    ('pre', MinMaxScaler()),
    ('clf', ClfSwitcher()),
])

# Grid search i.e hyperparameter tuning and refitting on mcc
gscv_lr = GridSearchCV(pipeline
                    , parameters
                    #, scoring = 'f1', refit = 'f1'
                    , scoring = mcc_score_fn, refit = 'mcc'
                    , cv = skf_cv
                    , **njobs
                    , return_train_score = False
                    , verbose = 3)

# Fit 
gscv_lr_fit = gscv_lr.fit(X, y)
gscv_lr_fit_be_mod = gscv_lr_fit.best_params_
gscv_lr_fit_be_res = gscv_lr_fit.cv_results_

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

#print('\nMean test score from fit results:', round(mean(gscv_lr_fit_be_res['mean_test_mcc']),2))
print('\nMean test score from fit results:', round(np.nanmean(gscv_lr_fit_be_res['mean_test_mcc']),2))


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

test_predict = gscv_lr_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(gscv_lr_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

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