#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 16 16:55:06 2022

@author: tanu
"""
# https://stackoverflow.com/questions/57248072/gridsearchcv-gives-different-result
#%% variables
mcc_score_fn = {'mcc': make_scorer(matthews_corrcoef)}

scoring_refit = {'scoring': 'recall'
                 ,'refit': 'recall'}

scoring_refit = {'scoring': mcc_score_fn
                 , 'refit': 'mcc'}

scoring_refit = {'scoring': 'jaccard'
                 ,'refit': 'jaccard'}

njobs = {'n_jobs': 10} # my desktop has 12 cores
skf_cv = StratifiedKFold(n_splits=10,shuffle  = True)
cv = {'cv': 10}

#%% GridSearchCV
gs_dt = GridSearchCV(estimator=DecisionTreeClassifier(**rs
                                                      #,class_weight = {1:10, 0:1}
                                                      ),
                  param_grid=[{'max_depth': [ 2,  4,  6, 8, 10]
                               , 'criterion':['gini','entropy']
                               , "max_features":["auto", None]
                               , "max_leaf_nodes":[10,20,30,40]}]
                  #, **cv
                  , cv = skf_cv
                  , **scoring_refit)
                  #, scoring = mcc_score_fn, refit = 'mcc')

#gs_dt.fit(X_train, y_train)
#gs_dt_fit = gs_dt.fit(X_train, y_train)
gs_dt.fit(X, y)
gs_dt_fit = gs_dt.fit(X, y)
gs_dt_fit_res  = gs_dt_fit.cv_results_

print('Best model:\n', gs_dt.best_params_)
print('Best models score:\n', gs_dt.best_score_)

best_model = gs_dt.best_params_
best_model
best_model_score =  gs_dt.best_score_
best_model_score
#%% Check the scores:
#  https://stackoverflow.com/questions/44947574/what-is-the-meaning-of-mean-test-score-in-cv-result
#print([(len(train), len(test)) for train, test in skf_cv.split(X, y)])
gs_dt_fit.cv_results_
#%%BaseEstimator:Recall
pipeline = Pipeline([
    ('pre', MinMaxScaler()),
    ('clf', ClfSwitcher()),
])


parameters = [
    {
        'clf__estimator': [DecisionTreeClassifier(**rs)]
        , 'clf__estimator__max_depth': [ 2,  4,  6, 8, 10]
        , 'clf__estimator__criterion':['gini','entropy']
        , 'clf__estimator__max_features':["auto", None]
        , 'clf__estimator__max_leaf_nodes':[10,20,30,40]
    }
]

gscv = GridSearchCV(pipeline
                    , parameters
                    , cv=skf_cv
                    , **njobs
                    , **scoring_refit
                    , return_train_score=False
                    , verbose=3)

gscv.fit(X, y)
print('Best model:\n', gscv.best_params_)
print('Best models score:\n', gscv.best_score_)