def _get_frequent_features(self):
"""Frequent Features are found using apriori algorithm"""
feature_terms = [sub_items for items in self.df['noun_and_np'].values for sub_items in items]
C1 = apriori.createC1(feature_terms)
D = map(set, feature_terms)
L1, support_data = apriori.scanD(D,C1,0.01) # minimum support 0.01
self.frequent_features = map(lambda x: "".join(list(x)), L1)
class Test:
if __name__ == "__main__":
#fza=frozenset(['a','bc'])
#adict={fza:1,'b':2}
#print(adict)
# print (isinstance('36521dyht', str)) ##可以判断变量 x 是否是字符串；
#cc= loadDataSet()
#createC1(cc)
#c=[6,5,4,9,8,3,5,6,8,1]
#c.sort()
#print(c)
dataSet = apriori.loadDataSet()
print(dataSet)
C1 = apriori.createC1(dataSet)
C1
D = map(set, dataSet)
L1 = []
supportData = []
(L1, supportData) = apriori.scanD(D, C1, 0.5)
print(L1)
print(supportData)
# coding:utf-8 import apriori # 发现频繁项集和发现关联规则 dataSet = apriori.loadDataSet() print(dataSet) C1 = apriori.createC1(dataSet) print(C1) D = map(set, dataSet) print(D) L1, suppData0 = apriori.scanD(D, C1, 0.5) print(L1) L, suppData = apriori.apriori(dataSet) print(L) L, suppData = apriori.apriori(dataSet, minSupport=0.5) rules = apriori.generateRules(L, suppData, minConf=0.7) print rules rules = apriori.generateRules(L, suppData, minConf=0.5) print rules
#!/usr/bin/env python
import sys
from apriori import createC1
from apriori import scanD
from apriori import aprioriGen
a = []
for i in sys.stdin:
i = i.strip()
v = i.split()
a.append(v)
c1 = createC1(a)
d = map(set, a)
K = 2
ps = 0.3
for i in range(K):
L1, sp = scanD(d, c1, ps)
c1 = aprioriGen(L1, i + 1)
for i in L1:
x, y = i
x = int(x)
y = int(y)
print "%d %d" % (x, y)
Created on Tue May 16 10:26:18 2017
@author: 凯风
"""
from imp import reload
import apriori
reload(apriori)
dataSet = apriori.loadDataSet() # 获取数据
dataSet
C1 = apriori.creadteC1(dataSet) # 获取数据集的C1-候选项集合
C1
D = list(map(set, dataSet)) # 把数据转换成集合的形式存放在列表中
D
L1, supportData0 = apriori.scanD(
D, C1, 0.5) # 以0.5支持度为要求，计算候选集的每一个项的支持度，并返回大于支持度的集合L1
L1
supportData0
# 根据支持度生成频繁集
reload(apriori)
L, supportData = apriori.apriori(dataSet)
L # 获得支持度大于0.5的频繁集合
L[0] # 包含一个元素的
L[1] # 包含两个元素的
L[2] # 包含三个元素的
L[3]
apriori.aprioriGen(L[0], 2) # 看一下如何生成的未和支持度比较的‘L[1]’
L, supportData = apriori.apriori(dataSet, minSupport=0.7) # 更大的支持度，获得少的结果了
# 根据可信度生成关联规则
def aprioriGen(Lk, k): #creates Ck
retList = []
lenLk = len(Lk)
for i in range(lenLk):
for j in range(i + 1, lenLk):
L1 = list(Lk[i])[:k - 2]
L2 = list(Lk[j])[:k - 2]
L1.sort()
L2.sort()
if L1 == L2: #if first k-2 elements are equal
retList.append(Lk[i] | Lk[j]) #set union
return retList
dataSet = loadDataSet()
minSupport = 0.5
'''def apriori(dataSet, minSupport = 0.5):'''
C1 = createC1(dataSet)
D = list(map(set, dataSet))
L1, supportData = scanD(D, C1, minSupport)
L = [L1]
k = 2
while (len(L[k - 2]) > 0):
Ck = aprioriGen(L[k - 2], k)
Lk, supK = scanD(D, Ck, minSupport) #scan DB to get Lk
supportData.update(supK)
L.append(Lk)
k += 1
#return L, supportData
Experiments with apriori
'''
import apriori
import random
import loadText
support = 0.4
loadText.importFromFile('spanish_db.txt')
dataset = loadText.rawPriori
#print dataset
C1 = apriori.createC1(dataset)
#print 'C1', C1
D = map(set,dataset)
#print 'D', D
L1, support_data = apriori.scanD(D,C1,support)
#print 'L1', L1
#print 'support_data', support_data
k_length = 2
transactions = apriori.aprioriGen(L1, k_length)
#print 'transactions', transactions
#print '\n*** *** ***'
L,support_data = apriori.apriori(dataset, support)
#print 'L', L
#print 'support_data', support_data
rules = apriori.generateRules(L, support_data, min_confidence=0.7)
#print 'rules', rules
ruleDict = apriori.generateRuleDict(rules)
'''
Experiments with apriori
'''
import apriori
import random
import loadText
support = 0.1
loadText.importFromFile('snowflakes_db.txt')
dataset = loadText.rawPriori
#print dataset
C1 = apriori.createC1(dataset)
#print 'C1', C1
D = map(set, dataset)
#print 'D', D
L1, support_data = apriori.scanD(D, C1, support)
#print 'L1', L1
#print 'support_data', support_data
print 'support_data'
for k, v in support_data.iteritems():
print k, v
k_length = 2
transactions = apriori.aprioriGen(L1, k_length)
#print 'transactions', transactions
#print '\n*** *** ***'
L, support_data = apriori.apriori(dataset, support)
#print 'L', L
#print 'support_data', support_data
rules = apriori.generateRules(L, support_data, min_confidence=0.7)
#print 'rules', rules
import apriori dataSet = apriori.loadDataSet() print "dataSet" print dataSet C1 = apriori.createC1(dataSet) print "C1" print C1 D=map(set, dataSet) print "D" print D L1, suppData0 = apriori.scanD(D, C1, 0.5) print "L1" print L1 print "suppData0" print suppData0 L,suppData = apriori.apriori(dataSet, minSupport=0.5) print "L" print L print "suppData" print suppData rules = apriori.generateRules(L, suppData, minConf=0.7) print "rules" print rules rules = apriori.generateRules(L, suppData, minConf=0.5)
'holocaust-history', 'dominican-republic-environmental-citizenship'
]
df = pd.DataFrame(et_tours, columns=tour_names)
#print(df.shape)
#print(df.head)
for index in range(len(tour_names)):
# tour_to_name = {'0' : np.nan, '1' : tour_names[index],'2' : tour_names[index],'1' : tour_names[index],'3' : tour_names[index],'4' : tour_names[index],'5' : tour_names[index],'6' : tour_names[index],'7' : tour_names[index]}
tour_to_name = {0: np.nan, 1: tour_names[index]}
df[tour_names[index]] = df[tour_names[index]].map(tour_to_name)
print(df.head)
C1 = createC1(df) #new stuff
D = map(set, df) #new stuff
L1, support_data = scanD(D, C1, 0.0000005) #new stuff
my_data = list()
for index in range(df.shape[0]):
basket = list(df.ix[index].dropna())
my_data.append(basket)
L, suppData = apriori(my_data)
print('Identified rules with support = ', alpha, 'and confidence= ', beta)
rules = generateRules(L, suppData, minConf=beta)
n_other_items = 1
while n_other_items <= max_other_items:
print('\nRules with ', n_other_items, 'other item(s)')
for item in L[n_other_items]:
import apriori as ap dataSet = ap.loadDataSet() #print dataSet C1 = ap.createC1(dataSet) #print C1 D = map(set, dataSet) #print D L1, suppData0 = ap.scanD(D, C1, 0.5) #print suppData0 L, S = ap.apriori(D, 0.5) #print L print L List = ap.generateRules(L, S, minConf=0.4) print List
