python 实现单调性，相关性，鲁棒性

import scipy
import pandas as pd
import math


def monotonicity(df_ori, col):
    df = df_ori.diff().dropna()
    data_x = df[col].values
    values_x = sum([1 if i > 0 else 0 for i in data_x])
    values_x_2 = sum([1 if i < 0 else 0 for i in data_x])
    mon = abs((values_x - values_x_2) / len(data_x))
    print(" 单调性:", mon)


def correlation(df_ori, col):
    data = df_ori[col].values.tolist()
    length = len(data)
    lis_t = [i/length for i in range(length)]
    val_a = len(data) * sum([i*j for i, j in zip(data, lis_t)]) - (sum(data) * sum(lis_t))
    val_b = math.sqrt(length * sum([pow(i, 2) for i in data]) - pow(sum([i for i in data]), 2))
    val_c = math.sqrt(length * sum([pow(i, 2) for i in lis_t]) - pow(sum([i for i in lis_t]), 2))
    corr = val_a / (val_b * val_c)
    print(" 相关性:", corr)


def robustness(df_ori, col):
    data = df_ori[col].values.tolist()
    y_smooth = scipy.signal.savgol_filter(data, 5, 3)
    res = [abs(i-j) for i, j in zip(y_smooth, data)]
    rob = sum([math.exp(-i) for i in res])/len(data)
    print(" 鲁棒性:", rob)


if __name__ == '__main__':
    df_ori = pd.read_excel(r'./HI.xlsx', header=None)
    col = 0
    monotonicity(df_ori, col)
    correlation(df_ori, col)
    robustness(df_ori, col)

之前找了一下只有matlab里面有可供调用的方法，使用python实现了一版

有点误差但影响不大