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

@author: tanu
"""
#%%
del(t3_res)
t3_res = MultClassPipeSKF(input_df = numerical_features_df
                          , y_targetF = target1
                          , var_type = 'numerical'
                          , skf_splits = 10)
pp.pprint(t3_res)
#print(t3_res)

#%% Manually: mean for each model, each metric
model_name = 'Logistic Regression'
model_name = 'Naive Bayes'
model_name = 'K-Nearest Neighbors'
model_name = 'SVM'
#%%
model_metric = 'F1_score'

log_reg_f1 = []
for key in t3_res[model_name]:
    log_reg_f1.append(t3_res[model_name][key][model_metric])
    log_reg_f1M = mean(log_reg_f1)
    print('key:', key, model_metric, ':', log_reg_f1)
print(log_reg_f1M)

log_reg_f1df = pd.DataFrame({model_name: [log_reg_f1M]}, index = [model_metric])
log_reg_f1df

#%%
model_metric = 'MCC'
log_reg_mcc = []
for key in t3_res[model_name]:
    log_reg_mcc.append(t3_res[model_name][key][model_metric])
    log_reg_mccM = mean(log_reg_mcc)
    print('key:', key, model_metric, ':', log_reg_mcc)
print(log_reg_mccM)

log_reg_mccdf = pd.DataFrame({model_name: [log_reg_mccM]}, index = [model_metric])
log_reg_mccdf
#%%
################################################################
# extract items from wwithin a nested dict
#%% Classification Metrics we need to mean()
classification_metrics = {
    'F1_score': []
    ,'MCC': []
    ,'Precision': []
    ,'Recall': []
    ,'Accuracy': []
    }
# "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': []}
    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': []}:
            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': []}:
        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)