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1_1dataPreprocess.py
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# coding: utf-8
# 1> 数据预处理
# 1数据清洗
# 2属性规约
# 3数据变换
# （1）属性构造
# （2）数据离散化
# -*- coding:utf-8 -*-
from __future__ import print_function
import pandas as pd
from pandas import DataFrame,Series
from sklearn.cluster import KMeans#导入K均值聚类算法
datafile = 'data.xls'
resultfile = 'data_processed.xlsx'
typelabel = {u'肝气郁结证型系数':'A',u'热毒蕴结证型系数':'B',u'冲任失调证型系数':'C',u'气血两虚证型系数':'D',u'脾胃虚弱证型系数':'E',u'肝肾阴虚证型系数':'F'}
k = 4 #需要进行的聚类类别数
#读取文件进行聚类分析
data = pd.read_excel(datafile)
keys = list(typelabel.keys())
result = DataFrame()
for i in range(len(keys)):
#调用k-means算法 进行聚类
print(u'正在进行%s的聚类' % keys[i])
kmodel = KMeans(n_clusters = k, n_jobs = 4) # n_job是线程数，根据自己电脑本身来调节
kmodel.fit(data[[keys[i]]].as_matrix())# 训练模型
# kmodel.fit(data[[keys[i]]]) # 不转成矩阵形式结果一样
#KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
# n_clusters=4, n_init=10, n_jobs=4, precompute_distances='auto',
# random_state=None, tol=0.0001, verbose=0)
r1 = DataFrame(kmodel.cluster_centers_, columns = [typelabel[keys[i]]]) # 聚类中心
r2 = Series(kmodel.labels_).value_counts() #分类统计
r2 = DataFrame(r2,columns = [typelabel[keys[i]]+'n'])# 转成DataFrame格式，记录各个类别的数目
r = pd.concat([r1,r2], axis=1).sort_values(typelabel[keys[i]])
r.index = range(1,5)
r[typelabel[keys[i]]] = pd.rolling_mean(r[typelabel[keys[i]]],2) # rolling_mean用来计算相邻两列的均值，以此作为边界点
r[typelabel[keys[i]]][1] = 0.0 # 将原来的聚类中心改成边界点
result = result.append(r.T)
result = result.sort_index() # 以index排序，以ABCDEF排序
result.to_excel(resultfile)
print (result)
# '''
# 1 2 3 4
# A 0.0 0.178698 0.257724 0.351843
# An 240.0 356.000000 281.000000 53.000000
# B 0.0 0.150766 0.296631 0.489705
# Bn 325.0 396.000000 180.000000 29.000000
# C 0.0 0.202149 0.289061 0.423537
# Cn 297.0 394.000000 204.000000 35.000000
# D 0.0 0.172049 0.251583 0.359353
# Dn 283.0 375.000000 228.000000 44.000000
# E 0.0 0.152698 0.257762 0.375661
# En 273.0 319.000000 244.000000 94.000000
# F 0.0 0.179143 0.261386 0.354643
# Fn 200.0 237.000000 265.000000 228.000000
# '''
# 2>划分原始数据中的类别
import pandas as pd
data.head()
# 将分类后数据进行处理
data_cut = DataFrame(columns = data.columns[:6])
types = ['A','B','C','D','E','F']
num = ['1','2','3','4']
for i in range(len(data_cut.columns)):
value = list(data.iloc[:,i])
bins = list(result[(2*i):(2*i+1)].values[0])
bins.append(1)
names = [str(x)+str(y) for x in types for y in num]
group_names = names[4*i:4*(i+1)]
cats = pd.cut(value,bins,labels=group_names,right=False)
data_cut.iloc[:,i] = cats
data_cut.to_excel('apriori.xlsx')
data_cut.head()