#!/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()
#%%############################################################################
# SMOTE number of neighbors
#k = 1 (pnca, extra trees baseline is 0.49,numerical only)
k = 1

sm = SMOTE(sampling_strategy = 'auto', k_neighbors = k, **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')

oversample = RandomOverSampler(sampling_strategy='minority')
X_ros, y_ros = oversample.fit_resample(X, y)
print(len(X_ros)) #228

undersample = RandomUnderSampler(sampling_strategy='majority')
X_rus, y_rus = undersample.fit_resample(X, y)
print(len(X_rus)) #142

sm_enn = SMOTEENN(enn=EditedNearestNeighbours(sampling_strategy='all'))
X_enn, y_enn = sm_enn.fit_resample(X, y)
print(len(X_enn)) #53