#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jul  7 10:54:09 2022

@author: tanu
"""

import numpy as np
from sklearn.dummy import DummyClassifier

X_eg = np.array([-1, 1, 1, 1])
y_eg = np.array([0, 1, 1, 1])
dummy_clf = DummyClassifier(strategy="most_frequent")
dummy_clf = DummyClassifier(strategy="stratified")
dummy_clf = DummyClassifier(strategy="stratified")

dummy_clf.fit(X_eg, y_eg)
#DummyClassifier(strategy='most_frequent')
dummy_clf.predict(X_eg)
dummy_clf.predict(np.array([1,1,1,1,1,1,1,1,1,1]))
dummy_clf.predict_proba(X_eg)

dummy_clf.score(X_eg, y_eg)
0.75

df2['X']

dummy_clf.fit(df2['X'], df2['y'])
dummy_clf.predict(df2['X'])
dummy_clf.predict_proba(df2['X'])
ypred = dummy_clf.predict(df2['X'])

dummy_clf.score(df2['X'], df2['y'])

confusion_matrix(df2['y'], ypred)
matthews_corrcoef(df2['y'], ypred)
#%%
df['dst_mode']
y_all_tt = df.loc[:,'dst']
y_all_tt.value_counts()
#Counter(y_all_tt)
#0: 71, 1: 114
y_all_tt.value_counts(normalize = True)

df2['y']
y_train_tt = df2['y']
Counter(y_train_tt)
##0: 41, 1: 82
y_train_tt.value_counts(normalize = True)

df2['y_bts']
y_bts_tt = df2['y_bts']
Counter(y_bts_tt)
#0: 21, 1: 41
y_bts_tt.value_counts(normalize = True)

#%%
df_clean = df[df['dst'].notna()]
X = df_clean.iloc[:,0:171]
X.columns

y = df_clean.iloc[:,171] # dst
y.value_counts()
#########################

y2 = df_clean.iloc[:,172] #dst_mode
y2.value_counts()

X_train_tt,X_test_tt, y_train_tt, y_test_tt = train_test_split(X, y, test_size=0.30, random_state=42, stratify = y)

y2.value_counts()
round(y.value_counts(normalize = True),2)

y_train_tt.value_counts()
round(y_train_tt.value_counts(normalize = True),2)

y_test_tt.value_counts()
round(y_test_tt.value_counts(normalize = True),2)

dummy_clf = DummyClassifier(strategy="most_frequent")
dummy_clf.fit(X_train_tt, y_train_tt)
DummyClassifier(strategy='most_frequent')
dummy_clf.predict(X_test_tt)

# pnca: split 0/30
=======================
Total y count in data:
1.0    114 (62%)
0.0     71 (38%)
=======================

=======================
Train y count in data:
1.0    79 (61%)
0.0    50 (39%)
=======================

=======================
Test y count in data:
1.0    35 (62%)
0.0    21 (38%)
=======================


acccuracy: 

TP+TN/TP+TN+FP+FN

114/71

######################################
# try with CV

X_eg = np.array([-1, 1, 1, 1, -2, 9, 4, 4, 1, -1, 3, 0])
y_eg = np.array([0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1])
dummy_clf = DummyClassifier(strategy="most_frequent")
dummy_clf.fit(X_eg, y_eg)
#DummyClassifier(strategy='most_frequent')
dummy_clf.predict(X_eg)
#dummy_clf.predict(np.array([1,1,1,1,1,1,1,1,1,1]))
#dummy_clf.predict_proba(X_eg)

dummy_clf.score(X_eg, y_eg)


cv_DummyD = cross_validate(dummy_clf
                      , X_eg
                      , y_eg
                      , cv = 5
                      #, groups = group
                      , scoring = scoring_fn
                      , return_train_score = True)

cv_dummyD_ALL= {}
cv_dummyD_ALL['DUMMY'] = {}

for key, value in cv_DummyD.items():
    print('\nkey:', key, '\nvalue:', value)
    print('\nmean value:', np.mean(value))
    cv_dummyD_ALL['DUMMY'][key] = round(np.mean(value),2)