#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu May 26 05:19:25 2022

@author: tanu
"""
#%% https://www.kite.com/blog/python/smote-python-imbalanced-learn-for-oversampling/
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.svm import SVC
from imblearn.over_sampling import SMOTE
#%%############################################################################

def train_SVM(df):
   # select the feature columns
   X = df.loc[:, df.columns != 'label']
   # select the label column
   y = df.label

   # train an SVM with linear kernel
   clf = SVC(kernel='linear')
   clf.fit(X, y)

   return clf


def plot_svm_boundary(clf, df, title):
   fig, ax = plt.subplots()
   X0, X1 = df.iloc[:, 0], df.iloc[:, 1]

   x_min, x_max = X0.min() - 1, X0.max() + 1
   y_min, y_max = X1.min() - 1, X1.max() + 1
   xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02), np.arange(y_min, y_max, 0.02))

   Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
   Z = Z.reshape(xx.shape)
   out = ax.contourf(xx, yy, Z, cmap=plt.cm.coolwarm, alpha=0.8)

   ax.scatter(X0, X1, c=df.label, cmap=plt.cm.coolwarm, s=20, edgecolors='k')
   ax.set_ylabel('y')
   ax.set_xlabel('x')
   ax.set_title(title)
   plt.show()
#%% SIMPLE RESAMPLING
############################################################################
#                               RESAMPLING
###############################################################################
#------------------------------
# Simple Random oversampling
# [Numerical + catgeorical]
#------------------------------
oversample = RandomOverSampler(sampling_strategy='minority')
X_ros, y_ros = oversample.fit_resample(X, y)
print(X_ros.shape) #228

#------------------------------
# Simple Random oversampling
# [Numerical + catgeorical]
#------------------------------
undersample = RandomUnderSampler(sampling_strategy='majority')
X_rus, y_rus = undersample.fit_resample(X, y)
print(X_rus.shape) #142

#------------------------------
# Simple combine ROS and RUS
# [Numerical + catgeorical]
#------------------------------
oversample = RandomOverSampler(sampling_strategy='minority')
X_ros, y_ros = oversample.fit_resample(X, y)
undersample = RandomUnderSampler(sampling_strategy='majority')
X_rouC, y_rouC = undersample.fit_resample(X_ros, y_ros)
print(X_rouC.shape) #228
###############################################################################
#%% SMOETE RESAMPLING
#------------------------------
# SMOTE: Oversampling
# [Numerical ONLY]
#------------------------------
k_sm = 1
sm = SMOTE(sampling_strategy = 'auto', k_neighbors = k_sm, **rs)
X_sm, y_sm = sm.fit_resample(X, y)
print(len(X_sm)) #228
#print(Counter(y))
y_sm_df = y_sm.to_frame()
y_sm_df.value_counts().plot(kind = 'bar')

#------------------------------
# SMOTE: Over + Undersampling COMBINED
# [Numerical ONLY]
#-----------------------------
sm_enn = SMOTEENN(enn=EditedNearestNeighbours(sampling_strategy='all', **rs, **njobs ))
X_enn, y_enn = sm_enn.fit_resample(X, y)
print(len(X_enn)) #53

#TO TRY
# sm_etomek = SMOTETomek(*, sampling_strategy='all',tomek = None), **njobs )
# X_etomek , y_etomek = sm_etomek.fit_resample(X, y)
# print(len(X_etomek)) 

# k_renn = 3 # deafult======= BAAADDD
# sm_renn = RepeatedEditedNearestNeighbours(sampling_strategy='all'
#                                                          , n_neighbors = k_renn
#                                                          , max_iter =100
#                                                          , **njobs )
# X_renn, y_renn = sm_renn.fit_resample(X, y)
# print(len(X_renn)) #22

# check:
# SMOTEhttps://imbalanced-learn.org/dev/references/generated/imblearn.under_sampling.AllKNN.html#imblearn.under_sampling.AllKNN
###############################################################################
#------------------------------
# SMOTE_NC: oversampling 
# [numerical + categorical]
#https://stackoverflow.com/questions/47655813/oversampling-smote-for-binary-and-categorical-data-in-python

#------------------------------
# Determine categorical and numerical features
numerical_ix = input_df.select_dtypes(include=['int64', 'float64']).columns
numerical_ix
num_featuresL = list(numerical_ix)
numerical_colind = input_df.columns.get_indexer(list(numerical_ix) )
numerical_colind

categorical_ix = input_df.select_dtypes(include=['object', 'bool']).columns
categorical_ix    
categorical_colind = input_df.columns.get_indexer(list(categorical_ix))
categorical_colind

k_sm = 5 # 5 is deafult
sm_nc = SMOTENC(categorical_features=categorical_colind, k_neighbors = k_sm, **rs, **njobs)
X_smnc, y_smnc = sm_nc.fit_resample(X, y)
print(len(X_smnc)) #228

#%%############################################################################
# FIXME : if that!
# SMOTE: Over+undersampling + [num+categorical]
# THIS WILL ONLY work if you handle the ohe separately for categ and find a way
# of inverse transforming it!
# t  = [ ('cat', OneHotEncoder(), categorical_ix) ]
        
# col_transform = ColumnTransformer(transformers = t
#                                       , remainder='passthrough')
# Xm = col_transform.fit_transform(X)
# Xm_colnames = col_transform.get_feature_names_out()
# Xmcolnames = pd.Index(Xm_colnames)
# Xmcolnames

# sm_ennC = SMOTEENN(enn=EditedNearestNeighbours(sampling_strategy='all'), **rs, **njobs )
# X_ennC, y_ennC = sm_ennC.fit_resample(Xm, y)
# print(X_ennC.shape)

# ohe = OneHotEncoder()
# ohe.fit(X)

# Xm = ohe.fit_transform(X[categorical_ix])
# print(Xm.shape)

# XmDF = pd.DataFrame(Xm.toarray())
#%%############################################################################
# TODO: Find over and undersampling JUST for categorical data