import pandas as pd
import numpy as np
import sys
from pandas.api.types import is_numeric_dtype
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle

def encode(df):
    res = pd.DataFrame()
    for col in df.columns.values:
        if not is_numeric_dtype(df[col]):
            tmp = pd.get_dummies(df[col],prefix=col)
        else:
            tmp = df[col]
        res = pd.concat([res,tmp],axis=1)
    return res


def load(device_id):
    fn_train = "data/bank_train_" + str(device_id) + ".csv"
    fn_test = "data/bank_test_" + str(device_id) + ".csv"
    
    train = pd.read_csv(fn_train, sep=',')
    test = pd.read_csv(fn_test, sep=',')

    train_x = train.drop(['y'], axis=1)
    train_y = train['y']
    val_x = test.drop(['y'], axis=1)
    val_y = test['y']
    
    train_x = np.array((train_x),dtype=np.float32)
    val_x = np.array((val_x),dtype=np.float32)
    train_y = np.array((train_y),dtype=np.int32)
    val_y = np.array((val_y),dtype=np.int32)
    
    train_x, val_x = normalize(train_x, val_x) 
    num_classes = 2

    return train_x, train_y, val_x, val_y, num_classes

def normalize(X_train, X_test):
    from sklearn.preprocessing import MinMaxScaler
    scaler = MinMaxScaler()
    X_train_scaled = scaler.fit_transform(X_train)
    X_test_scaled = scaler.transform(X_test)
    return X_train_scaled, X_test_scaled

def split(num):
    filepath = "data/bank-additional-full.csv"
    df = pd.read_csv(filepath, sep=';')
    df['y'] = (df['y']=='yes').astype(int)
    data = encode(df)
    data = shuffle(data)
    train, test = train_test_split(data, test_size=0.2)
    
    train.to_csv("data/bank_train_.csv",index=False)
    test.to_csv("data/bank_test_.csv",index=False)
    
    train_per_client = len(train)//num
    test_per_client = len(test)//num
    
    print("train_per_client:",train_per_client)
    print("test_per_client:",test_per_client)
    for i in range(num):
        sub_train = train[i*train_per_client:(i+1)*train_per_client]
        sub_test = test[i*test_per_client:(i+1)*test_per_client]
        sub_train.to_csv("data/bank_train_"+str(i)+".csv",index=False)
        sub_test.to_csv("data/bank_test_"+str(i)+".csv",index=False)

if __name__ == "__main__":
     split(int(sys.argv[1]))

