X=data.loc[:,data.columns!='Class'] #loc 通過行標籤索引資料，
# print(X)
y=data.loc[:,data.columns=='Class'] #取label
# print(y)
number_records_fraud=len(data[data.Class==1]) #Class=1的數量
# print(number_records_fraud)
fraud_indices=np.array(data[data.Class==1].index) #取得其索引值
# print(fraud_indices)
normal_indices=np.array(data[data.Class==0
 
].index) # 把class為0的資料索引拿到手
# print(normal_indices)
random_normal_indices=np.random.choice(normal_indices,number_records_fraud,replace=False)  # 隨機取樣，並不對原始dataframe進行替換
random_normal_indices=np.array(random_normal_indices) # 轉換成numpy的array格式轉換成矩陣
# print(random_normal_indices)
under_sample_indices=np.concatenate([fraud_indices,random_normal_indices
 
]) # 將兩組索引資料連線成性的資料索引
# print(under_sample_indices)
under_sample_data = data.iloc[under_sample_indices,:] #定位到真正資料，iloc通過行號索引行資料
# print(under_sample_data)
X_undersample=under_sample_data.loc[:,under_sample_data.columns!='Class']
y_undersample=under_sample_data.loc[:,under_sample_data.columns=='Class'
 
]
print(X_undersample)
print(y_undersample)


print("Percentage of normal transactions: ", len(under_sample_data[under_sample_data.Class == 0])/len(under_sample_data))
print("Percentage of fraud transactions: ", len(under_sample_data[under_sample_data.Class == 1])/len(under_sample_data))
print("Total number of transactions in resampled data: ", len(under_sample_data))