#!/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

mcc_score_fn = {'mcc': make_scorer(matthews_corrcoef)}

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

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


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(num_df_wtgt[numerical_FN]
          , num_df_wtgt['mutation_class'])

gs_dt_fit = gs_dt.fit(num_df_wtgt[numerical_FN]
          , num_df_wtgt['mutation_class'])

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_)
print('Check best models score:\n', mean(gs_dt_fit_res['mean_test_mcc']))

#%% 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(num_df_wtgt[numerical_FN]
                                                               , num_df_wtgt['mutation_class'])])
gs_dt_fit.cv_results_
#%%
gs_rf = GridSearchCV(estimator=RandomForestClassifier(n_jobs=-1, oob_score = True
                                                      ,class_weight = {1: 10/11, 0: 1/11})
                     , param_grid=[{'max_depth': [4, 6, 8, 10, 12, 16, 20, None]
                                 , 'max_features': ['auto', 'sqrt']
                                 , 'min_samples_leaf': [2, 4, 8]
                                 , 'min_samples_split': [10, 20]}]
                    , **cv
                    , **njobs
                    #, **scoring_refit
                    , scoring = scoring_fn
                    #, refit  = T
                    )
gs_rf.fit(X_train, y_train)
#gs_rf_fit = gs_rf.fit(X_train, y_train)
#res_rf = res_dt_fit.cv_results_

print('Best model:\n', gs_rf.best_params_)
print('Best models score:\n', gs_rf.best_score_)
print('Check best models score:\n', mean(res_rf['mean_test_score']))

#%%
gs_lr = GridSearchCV(estimator=LogisticRegression(multi_class='ovr',random_state=42,class_weight={1:10, 0:1}),
              param_grid=[{'C': [0.000001, 0.00001, 0.0001, 0.001, 0.01, 0.1 ,1],
                          'penalty':['l1','l2']}],
              scoring=scoring,
              cv=10,
              refit='recall')

gs_gb = GridSearchCV(estimator=XGBClassifier(n_jobs=-1),
                    param_grid=[{'learning_rate': [0.01, 0.05, 0.1, 0.2],
                                 'max_depth': [4, 6, 8, 10, 12, 16, 20],
                                 'min_samples_leaf': [4, 8, 12, 16, 20], 
                                 'max_features': ['auto', 'sqrt']}],
                    scoring=scoring,
                    cv=10,
                    n_jobs=4,
                    refit='recall')
#%%



#%%
gs_mlp = MLPClassifier(max_iter=600)
clf = GridSearchCV(gs_mlp, parameter_space, n_jobs= 10, cv = 10)

#clf.fit(X_train, y_train.values.ravel())

parameter_space = {
    'hidden_layer_sizes': [(1), (2), (3)],
}

print('Best parameters found:\n', clf.best_params_)

means = clf.cv_results_['mean_test_score']
stds = clf.cv_results_['std_test_score']
for mean, std, params in zip(means, stds, clf.cv_results_['params']):
    print("%0.3f (+/-%0.03f) for %r" % (mean, std * 2, params))