#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed May 18 06:03:24 2022

@author: tanu
"""
#%% RandomForest + hyperparam: BaseEstimator: ClfSwitcher()
class ClfSwitcher(BaseEstimator):
    def __init__(
        self, 
        estimator = SGDClassifier(),
    ):
        """
        A Custom BaseEstimator that can switch between classifiers.
        :param estimator: sklearn object - The classifier
        """ 
        self.estimator = estimator
    
    def fit(self, X, y=None, **kwargs):
        self.estimator.fit(X, y)
        return self

    def predict(self, X, y=None):
        return self.estimator.predict(X)
    
    def predict_proba(self, X):
        return self.estimator.predict_proba(X)
    
    def score(self, X, y):
        return self.estimator.score(X, y)
    
parameters = [
    {
        'clf__estimator': [RandomForestClassifier(**rs
                                                  , **njobs
                                                  , bootstrap = True
                                                  , oob_score = True)],
        'clf__estimator__max_depth': [4, 6, 8, 10, 12, 16, 20, None]
        , 'clf__estimator__class_weight':['balanced','balanced_subsample']
        , 'clf__estimator__n_estimators': [10, 25, 50, 100]
        , 'clf__estimator__criterion': ['gini', 'entropy', 'log_loss']
        , 'clf__estimator__max_features': ['sqrt', 'log2', None] #deafult is sqrt
        , 'clf__estimator__min_samples_leaf': [1, 2, 3, 4, 5, 10]
        , 'clf__estimator__min_samples_split': [2, 5, 15, 20]
        }
]

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

# Grid search i.e hyperparameter tuning and refitting on mcc
gscv_rf = 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_rf_fit  = gscv_rf.fit(X, y)

gscv_rf_fit_be_mod = gscv_rf_fit.best_params_
gscv_rf_fit_be_res = gscv_rf_fit.cv_results_

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

print('\nMean test score from fit results:', round(mean(gscv_rf_fit_be_re['mean_test_mcc']),2))
print('\nMean test score from fit results:', round(np.nanmean(gscv_rf_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_rf_fit.predict(X_bts)
print(test_predict)
print(np.array(y_bts))
y_btsf = np.array(y_bts)

print(accuracy_score(y_btsf, test_predict))
print(matthews_corrcoef(y_btsf, test_predict))

# create a dict with all scores
rf_bts_dict = {#'best_model': list(gscv_rf_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 }
rf_bts_dict
rf_bts_dict['bts_fscore']    = round(f1_score(y_bts, test_predict),2)
rf_bts_dict['bts_mcc']       = round(matthews_corrcoef(y_bts, test_predict),2)
rf_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
rf_bts_dict['bts_recall']    = round(recall_score(y_bts, test_predict),2)
rf_bts_dict['bts_accuracy']  = round(accuracy_score(y_bts, test_predict),2)
rf_bts_dict['bts_roc_auc']   = round(roc_auc_score(y_bts, test_predict),2)
rf_bts_dict['bts_jaccard']   = round(jaccard_score(y_bts, test_predict),2)
rf_bts_dict

# Create a df from dict with all scores
pd.DataFrame.from_dict(rf_bts_dict, orient = 'index', columns = 'best_model')

rf_bts_df = pd.DataFrame.from_dict(rf_bts_dict,orient = 'index')
rf_bts_df.columns = ['Logistic_Regression']
print(rf_bts_df)

# Create df with best model params
model_params = pd.Series(['best_model_params',  list(gscv_rf_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
rf_bts_df.columns
rf_output = pd.concat([model_params_df, rf_bts_df], axis = 0)
rf_output

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

#FIXME: tidy the index of the formatted df

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