python 离群点检测，, 1 import

python 离群点检测，, 1 import

 1 import numpy as np 2 import pandas as pd 3 from sklearn.cluster import KMeans 4 import matplotlib.pyplot as mp 5  6  7 def get_data_zs(inputfile): 8     data = pd.read_excel(inputfile, index_col=‘Id‘, encoding=‘gb18030‘) 9     data_zs = 1.0 * (data - data.mean()) / data.std()10     return data, data_zs11 12 13 def model_data_zs(data, k, b):14     model = KMeans(n_clusters=k, n_jobs=4, max_iter=b)15     model.fit(data_zs)16 17     # 标准化数据及其类别18     r = pd.concat(19         [data_zs, pd.Series(model.labels_, index=data.index)], axis=1)20     # print(r.head())21     # 每个样本对应的类别22     r.columns = list(data.columns) + [u‘聚类类别‘]  # 重命名表头23     return model, r, k24 25 26 def make_norm(model, k):27     norm = []28     for i in range(k):29         norm_tmp = r[[‘R‘, ‘F‘, ‘M‘]][30             r[u‘聚类类别‘] == i] - model.cluster_centers_[i]31         norm_tmp = norm_tmp.apply(np.linalg.norm, axis=1)  # 求出绝对距离32         norm.append(norm_tmp / norm_tmp.median())  # 求相对距离并添加33     norm = pd.concat(norm)34     return norm35 36 37 def draw_discrete_point(threshold):38     mp.rcParams[‘font.sans-serif‘] = [‘SimHei‘]39     mp.rcParams[‘axes.unicode_minus‘] = False40     norm[norm <= threshold].plot(style=‘go‘)  # 正常点41 42     discrete_points = norm[norm > threshold]  # 离散点阈值43     discrete_points.plot(style=‘rs‘)44     # print(discrete_points)45 46     for i in range(len(discrete_points)):  # 离群点做标记47         id = discrete_points.index[i]48         n = discrete_points.iloc[i]49         mp.annotate(‘(%s,%0.2f)‘ % (id, n), xy=(id, n), xytext=(id, n))50     mp.xlabel(r‘编号‘)51     mp.ylabel(r‘相对距离‘)52     mp.show()53 54 if __name__ == ‘__main__‘:55     inputfile = ‘data/consumption_data.xls‘56     threshold = 2 # 离散点阈值57     k = 3 # 聚类类别58     b = 500 # 聚类最大循环次数59     data, data_zs = get_data_zs(inputfile)60     model, r, k = model_data_zs(data, k, b)61     norm = make_norm(model, k)62     draw_discrete_point(threshold)63     print(‘All Done‘)

显示结果：

python 离群点检测