import numpy as np

 import pandas as pd

 from sklearn.cluster import KMeans

 import matplotlib.pyplot as mp

 def get_data_zs(inputfile):

     data = pd.read_excel(inputfile, index_col='Id', encoding='gb18030')

     data_zs = 1.0 * (data - data.mean()) / data.std()

     return data, data_zs

 def model_data_zs(data, k, b):

     model = KMeans(n_clusters=k, n_jobs=4, max_iter=b)

     model.fit(data_zs)

     # 标准化数据及其类别

     r = pd.concat(

         [data_zs, pd.Series(model.labels_, index=data.index)], axis=1)

     # print(r.head())

     # 每个样本对应的类别

     r.columns = list(data.columns) + [u'聚类类别']  # 重命名表头

     return model, r, k

 def make_norm(model, k):

     norm = []

     for i in range(k):

         norm_tmp = r[['R', 'F', 'M']][

             r[u'聚类类别'] == i] - model.cluster_centers_[i]

         norm_tmp = norm_tmp.apply(np.linalg.norm, axis=1)  # 求出绝对距离

         norm.append(norm_tmp / norm_tmp.median())  # 求相对距离并添加

     norm = pd.concat(norm)

     return norm

 def draw_discrete_point(threshold):

     mp.rcParams['font.sans-serif'] = ['SimHei']

     mp.rcParams['axes.unicode_minus'] = False

     norm[norm <= threshold].plot(style='go')  # 正常点

     discrete_points = norm[norm > threshold]  # 离散点阈值

     discrete_points.plot(style='rs')

     # print(discrete_points)

     for i in range(len(discrete_points)):  # 离群点做标记

         id = discrete_points.index[i]

         n = discrete_points.iloc[i]

         mp.annotate('(%s,%0.2f)' % (id, n), xy=(id, n), xytext=(id, n))

     mp.xlabel(r'编号')

     mp.ylabel(r'相对距离')

     mp.show()

 if __name__ == '__main__':

     inputfile = 'data/consumption_data.xls'

     threshold = 2 # 离散点阈值

     k = 3 # 聚类类别

     b = 500 # 聚类最大循环次数

     data, data_zs = get_data_zs(inputfile)

     model, r, k = model_data_zs(data, k, b)

     norm = make_norm(model, k)

     draw_discrete_point(threshold)

     print('All Done')