#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Mar 11 11:15:50 2022

@author: tanu
"""
#%% variables
# rs = {'random_state': 42}

# skf_cv = StratifiedKFold(n_splits = 10
#                           #, shuffle = False, random_state= None)
#                           , shuffle = True,**rs)
#%% MultClassPipeSKFLoop: function call()
t3_res = MultClassPipeSKFLoop(input_df = num_df_wtgt[numerical_FN]
                          , target = num_df_wtgt['mutation_class']
                          , var_type = 'numerical'
                          , sel_cv = skf_cv)
                          #, sel_cv = rskf_cv)
pp.pprint(t3_res)
#print(t3_res)
################################################################
# extract items from wwithin a nested dict
#%% Classification Metrics we need to mean()
# classification_metrics = {
#     'F1_score': []
#     ,'MCC': []
#     ,'Precision': []
#     ,'Recall': []
#     ,'Accuracy': []
#     ,'ROC_AUC':[]
#     }
# "mean() of the current metric across all folds for this model"
# the output containing all the metrics across all folds for this model
out={}
# Just the mean() for each of the above metrics-per-model
out_means={}

# Build up out{} from t3_res, which came from loopity_loop
for model in t3_res:
    # NOTE: can't copy objects in Python!!!
    out[model]={'F1_score': [], 'MCC': [], 'Precision': [], 'Recall': [], 'Accuracy': [], 'ROC_AUC':[]}
    out_means[model]={} # just to make life easier
    print(model)
    for fold in t3_res[model]:
        for metric in {'F1_score': [], 'MCC': [], 'Precision': [], 'Recall': [], 'Accuracy': [], 'ROC_AUC':[]}:
            metric_value = t3_res[model][fold][metric]
            out[model][metric].append(metric_value)
# now that we've built out{}, let's mean() each metric            
for model in out:
    for metric in {'F1_score': [], 'MCC': [], 'Precision': [], 'Recall': [], 'Accuracy': [], 'ROC_AUC':[]}:
        metric_mean = mean(out[model][metric])
        # just some debug output
        # print('model:', model
        #       , 'metric: ', metric
        #       , metric_mean
        #       )
        out[model].update({(metric+'_mean'): metric_mean })
        out_means[model].update({(metric+'_mean'): metric_mean })

out_scores = pd.DataFrame(out_means)
out_scores2 = round(out_scores, 2)