python编写KMeans算法调用pandas和sklearn库

zhangqinghao

import pandas as pd
from sklearn.cluster import KMeans
if __name__=='__main__':
    inputfile = 'data.xlsx'
    outputfile = 'data1.xlsx'
    k = 3
    iteration = 500
    data = pd.read_excel(inputfile,index_col='ID')
    data_zs = 1.0*(data-data.mean())/data.std()
    model = KMeans(n_clusters=k, n_jobs=4, max_iter=iteration)
    model.fit(data_zs)
    r1 = pd.Series(model.labels_).value_counts()
    r2 = pd.DataFrame(model.cluster_centers_)
    r = pd.concat([r2, r1], axis=1)
    print(r)
    r = pd.concat([data, pd.Series(model.labels_, index=data.index)], axis=1)
    r.columns = list(data.columns) + [u'聚类类别']
    r.to_excel(outputfile)
一开始少打了这个命令
model.fit(data_zs)
就会报错，显示KMeans里无labels标签
第二次显示：raise ImportError('[joblib] Attempting to do parallel computing '
ImportError: [joblib] Attempting to do parallel computing without protecting your import on a system that does not support forking. To use parallel-computing in a script, you must protect your main loop using "if __name__ == '__main__'". Please see the joblib documentation on Parallel for more information
Traceback (most recent call last):
于是加上了
if __name__=='__main__':
一定注意name和mian前后都是双下划线，如果仅有单一下划线会提示name未定义。
在Ipython notebook里面没有出现这种保护loop main 问题
但由于ipython notebook不太方便打包，所以用pycharm.