python-LMDI implementation and data examples

def LMDI(*,data_t:object, data_0:object, yt:float, y0:float) -> object:
    """
    Args:
        data_t:t期结果值,example
        data_0:基期结果值
        yt: t期结果值
        y0: 基期结果值
    Returns:
        object['x']['key'] 代表x的key,x为自变量
        object['x']['key']['x0']: 基期值
        object['x']['key']['xt']: t期值
        object['x']['key']['change']: 变动值
        object['x']['key']['changeRate']: 变动率，即同比值
        object['x']['key']['contribute']: LMDI计算得贡献度
        object['x']['key']['contributeRate']: 基期贡献率, LMDI计算得贡献度 / y0
        object['x']['key']['changeContributeRate']: 变动值贡献率, LMDI计算得贡献度 / object['y']['change']
        object['y'] y代表因变量
        object['y']['y0']: 基期值
        object['y']['yt']: 本期值
        object['y']['change']: 变动值
        object['y']['changeRate']: 变动率，即同比值
    Raises:
        ValueError: data_t/data_0的内容相乘不等于yt/y0, data_t和data_0的key名称及数量不相等

    """
    from functools import reduce
    import numpy as np 

    # 对参数进行校验
    data_0_comp = reduce(lambda x,y:x*y,data_0.values())
    data_t_comp = reduce(lambda x,y:x*y,data_t.values())

    if ( data_t_comp - yt > 1 or data_t_comp - yt <-1) :  # 考虑到float的计算精度，这里放了gap值不能大于1
        raise ValueError('data_t的内容相乘不等于tt')
    elif (data_0_comp - y0 > 1 or data_0_comp - y0 <-1):
        raise ValueError('data_0的内容相乘不等于tt')
    elif data_t.keys() != data_0.keys():
        raise ValueError('data_t和data_0的key名称及数量不相等')


    def Delta_XX(*,yt,y0,xt,x0):
        # 计算LMDI中每个参数的Δ值
        def L(yt,y0):
            if yt == y0:
                return 0
            else:
                return (yt-y0)/(np.log(yt) - np.log(y0))
        return L(yt,y0)*np.log(xt/x0)
    
    x = {}
    for key in data_t.keys(): 
        x[key] = {}
        x[key]['x0'] = data_0[key]
        x[key]['xt'] = data_t[key]
        x[key]['change'] = data_t[key]- data_0[key]
        x[key]['changeRate'] = 0 if data_0[key]==0 or data_0[key]==0 or data_0[key]=="" else (data_t[key]- data_0[key]) / data_0[key]
        x[key]['contribute'] = Delta_XX(yt=yt,y0=y0,xt=data_t[key], x0=data_0[key]) 
        x[key]['contributeRate'] = 0 if y0==0 else x[key]['contribute']  / y0
        x[key]['changeContributeRate'] = 0 if yt-y0 == 0 else  x[key]['contribute'] / (yt-y0)

    y = {}
    y['y0'] = y0
    y['yt'] = yt
    y['change'] = yt - y0
    y['changeRate'] = 0 if yt ==0 or y0==0 else (yt-y0)/yt
    
    result = {
        "x":x,
        "y":y
    }
    return result

# 计算结果
y0 = 1078122 # 基期结果值
yt = 1469699  # t期结果值
data_t = {
    "uv":19087,
    "m": 0.25,
    "d": 308
} # t期分解值集合
data_0 = {
    "uv":20032,
    "m": 0.23,
    "d": 234
} # 基期分解值集合
LMDI(data_t=data_t,data_0=data_0,yt=yt,y0=y0)

Reference: Detailed explanation of LMDI theoretical derivation [From theory to Python-MATLAB implementation (programming implementation)]_Chunfeng Zui’s blog-CSDN blog_LMDI model python implementation