def rulesthread(self): start = time.time() if (self.suanfa=='Aprioi'): if(self.minconfig!=0): self.rules=apriori.generateRules(self.L,self.support,self.minconfig) else: self.rules=apriori.generateRules(self.L,self.support) else: self.rules = [] if (self.minconfig!=0): Rules.rulesGenerator(self.L, self.rules, self.minconfig) else: Rules.rulesGenerator(self.L, self.rules) # self.rules = [] # if (self.minconfig!=0): # Rules.rulesGenerator(self.L, self.rules, self.minconfig) # else: # Rules.rulesGenerator(self.L, self.rules) self.result_data_Text.insert(INSERT,'关联规则\n') for i in self.rules: self.result_data_Text.insert(INSERT,list(i)) self.result_data_Text.insert(INSERT,'\n') end = time.time() self.result_data_Text.insert(INSERT,str(len(self.rules))+'\n') self.log_data_Text.insert(INSERT,'关联规则生成完毕! 共'+str(len(self.rules))+'项 耗时：'+str(round(end-start,2))+'s\n')
def test_generate_associations(self):
L, supp_data = apriori.apriori(self.dataset, min_support=0.5)
print 'L:', L
print '-'*20
print 'supp_data: ', supp_data
print '-'*20
rules = apriori.generateRules(L, supp_data, min_confidence=0.95)
print '-'*20
print 'rules: ', rules
print '-'*20
assert False
def test1():
dataSet = apriori.loadDataSet()
print(dataSet) #[[1, 3, 4], [2, 3, 5], [1, 2, 3, 5], [2, 5]]
#C1=apriori.createC1(dataSet)
#print(set(C1)) #{frozenset({4}), frozenset({5}), frozenset({2}), frozenset({3}), frozenset({1})}
#print(list(C1)) #[frozenset({1}), frozenset({2}), frozenset({3}), frozenset({4}), frozenset({5})]
#D=map(set,dataSet)
#print(list(D)) #[{1, 3, 4}, {2, 3, 5}, {1, 2, 3, 5}, {2, 5}] 注意！！被list(map1)之后，map1的内容就空了。。。好像set(.)也会清空人家
#L1,suppData0 = apriori.scanD(D, C1, 0.5) #不能直接用了，要把D和C1先变成list
#print(L1) #[frozenset({1}), frozenset({3}), frozenset({2}), frozenset({5})]
#print(suppData0) #{frozenset({4}): 0.25, frozenset({5}): 0.75, frozenset({2}): 0.75, frozenset({3}): 0.75, frozenset({1}): 0.5}
L, suppData = apriori.apriori(dataSet, 0.5)
print(L)
print(suppData)
rules = apriori.generateRules(L, suppData, minConf=0.5)
print(rules)
#print 'rules:\n', rules
with open("xss-train.txt") as f:
for line in f:
#/discuz?q1=0&q3=0&q2=0%3Ciframe%20src=http://xxooxxoo.js%3E
index=line.find("?")
if index>0:
line=line[index+1:len(line)]
#print line
tokens=re.split('\=|&|\?|\%3e|\%3c|\%3E|\%3C|\%20|\%22|<|>|\\n|\(|\)|\'|\"|;|:|,|\%28|\%29',line)
#print "token:"
#print tokens
myDat.append(tokens)
f.close()
L, suppData = apriori(myDat, 0.15)
rules = generateRules(L, suppData, minConf=0.6)
#print 'rules:\n', rules# -*- coding:utf-8 -*-
import sys
import urllib
import urlparse
import re
from hmmlearn import hmm
import numpy as np
from sklearn.externals import joblib
import HTMLParser
import nltk
#处理参数值的最小长度
MIN_LEN=6
import sys
sys.path.append('../chapter4')
import pandas as pd
from graphviz import Digraph
import apriori
# 定义数据文件
fileName = 'association.txt'
# 通过调用自定义的apriori做关联分析
minS = 0.1 # 定义最小支持度阀值
minC = 0.38 # 定义最小置信度阀值
dataSet = apriori.createData(fileName) # 获取格式化的数据集
L, suppData = apriori.apriori(dataSet, minSupport=minS) # 计算得到满足最小支持度的规则
rules = apriori.generateRules(fileName, L, suppData,
minConf=minC) # 计算满足最小置信度的规则
# 关联结果报表评估
model_summary = 'data record: {1} \nassociation rules count: {0}' # 展示数据集记录数和满足阀值定义的规则数量
print(model_summary.format(len(rules), len(dataSet))) # 使用str.format做格式化输出
df = pd.DataFrame(
rules,
columns=['item1', 'itme2', 'instance', 'support', 'confidence',
'lift']) # 创建频繁规则数据框
df_lift = df[df['lift'] > 1.0] # 只选择提升度>1的规则
print(df_lift.sort('instance', ascending=False)) # 打印排序后的数据框
# 关联结果图形展示
dot = Digraph() # 创建有向图
graph_data = df_lift[['item1', 'itme2', 'instance']] # 切分画图用的前项、后项和实例数数据
for each_data in graph_data.values: # 循环读出每条规则
import apriori dataSet = apriori.loadDataSet() L, suppData = apriori.apriori(dataSet, minSupport=0.5) rules = apriori.generateRules(L, suppData, minConf=0.5) from sklearn import svm x = [[2, 0], [1, 1], [2, 3]] y = [0, 0, 1] clf = svm.SVC(kernel='linear') clf.fit(x, y) print(clf) print(clf.predict([[2, 0]])) #predict[2,0] belong to which class import numpy as np import pylab as pl from sklearn import svm np.random.seed(0) x = np.r_[np.random.randn(20, 2) - [2, 2], np.random.randn(20, 2) + [2, 2]] y = [0] * 20 + [1] * 20 #[0,0...1,1] clf = svm.SVC(kernel='linear') clf.fit(x, y) w = clf._get_coef()[0] a = -w[0] / w[1] xx = np.linspace(-5, 5) yy = a * xx - (clf._intercept_[0]) / w[1] b = clf.support_vectors_[0] yy_down = a * xx + (b[1] - a * b[0]) b = clf.support_vectors_[-1] yy_up = a * xx + (b[1] - a * b[0])
#dataSet = apriori.loadDataSet()
#C1 = apriori.createC1(dataSet)
#D = map(set, dataSet)
#print('D', D)
#L1, suppData0 = apriori.scanD(D, C1, 0.5)
#频繁项集L
#所有候选项集的支持度信息suppData
L, suppData = apriori.apriori(dataSet, 0.3)
print('L:', L)
#print('suppData:', suppData)
rules = apriori.generateRules(L, suppData, minConf = 0.3)
#print('rules', len(rules))
rules_remove_redundancy = []
def remove_redundancy():
redundancy_indices = []
for i in range(0, len(rules) - 1):
for j in range(i + 1, len(rules)):
if rules[i][0] < rules[j][0] and rules[i][1] < rules[j][1] and rules[i][4] <= rules[j][4]:
if i not in redundancy_indices:
redundancy_indices.append(i)
if rules[j][0] < rules[i][0] and rules[j][1] < rules[i][1] and rules[j][4] <= rules[i][4]:
if j not in redundancy_indices:
import apriori dataMat = apriori.loadDataSet() print(dataMat) dataSet = apriori.createC1(dataMat) print(dataSet) L, supportData = apriori.apriori(dataMat) print(L) print(supportData) apriori.generateRules(L, supportData, 0.5)
#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)
'''
print 'ruleDict', ruleDict
print '*** *** ***'
'''
print 'keys', ruleDict.keys()
print '*** *** ***'
## testing
if __name__ == '__main__':
#print '\n\n***\n'
import ex1
import apriori
data = ex1.loadDataSet()
c1 = ex1.createcl(data)
print(c1)
print(data)
# d = map(set,data)
l1, supportdata = ex1.scand(data, c1, 0.5)
print(l1)
print(supportdata)
k = 2
l = [l1]
# ck = ex1.apriorigen(l,k)
# print(ck)
while (len(l[k - 2]) > 0):
ck = ex1.apriorigen(l[k - 2], k)
lk, supk = ex1.scand(data, ck, 0.5)
supportdata.update(supk)
l.append(lk)
k += 1
print(l, k)
rules = apriori.generateRules(l, supportdata, minConf=0.7)
print(rules)
D = map(set, datSet)
#D[set([1, 3, 4]), set([2, 3, 5]), set([1, 2, 3, 5]), set([2, 5])]
L1, suppData0 = apriori.scanD(D, Cl, 0.5)
#retList, supportData
print("retList-L1")
print(L1)
print("supportData-suppData0")
print(suppData0)
# apriori.aprioriGen()
L2, suppData0 = apriori.apriori(datSet)
print("L2")
print(L2)
print("suppData0")
print(suppData0)
#
rules = apriori.generateRules(L2, suppData0, minConf=0.6)
print("rules")
print(rules)
# actionIdList, billTitleList=recentAprioriTest.getActionIds()
# print("actionIdList")
# print(actionIdList)
# print("billTitleList")
# print(billTitleList)
#
# transDict, itemMeaning=recentAprioriTest.getTransList(actionIdList[:2],billTitleList[:2])
# print("transDict")
# print(transDict)
# print("itemMeaning")
# print(itemMeaning)
# the current data-set isn't in transactional format. To convert it into a transactional data-set, we use the following snippet of code:
basket_str=""
for rowNum, row in accident_data.iterrows():
#Break lines
if (rowNum != 0):
basket_str = basket_str + "\n"
#Add the rowid as the first column
basket_str = basket_str + str(rowNum)
#Add columns
for colName, col in row.iteritems():
if ( colName != 'Accident_Index'):
basket_str = basket_str + "," + colName + "=" + str(col)
#print basket_str
basket_file=open("accidents_basket.csv","w")
basket_file.write(basket_str)
basket_file.close()
import csv
with open("accidents_basket.csv","rb") as f:
reader=csv.reader(f)
my_list=list(reader)
#my_list
L,supportData=apriori.apriori(my_list,0.6)
f_rules= apriori.generateRules(L,supportData,0.6)
for row in f_rules:
print list(row[0]), " => ", list(row[1]), row[2]
for line in f:
#/discuz?q1=0&q3=0&q2=0%3Ciframe%20src=http://xxooxxoo.js%3E
index = line.find("?")
if index > 0:
line = line[index + 1:len(line)]
#print line
tokens = re.split(
'\=|&|\?|\%3e|\%3c|\%3E|\%3C|\%20|\%22|<|>|\\n|\(|\)|\'|\"|;|:|,|\%28|\%29',
line)
#print "token:"
#print tokens
myDat.append(tokens)
f.close()
L, suppData = apriori(myDat, 0.15)
rules = generateRules(L, suppData, minConf=0.6)
#print 'rules:\n', rules# -*- coding:utf-8 -*-
import sys
import urllib
import urlparse
import re
from hmmlearn import hmm
import numpy as np
from sklearn.externals import joblib
import HTMLParser
import nltk
#处理参数值的最小长度
MIN_LEN = 6
arrAll.append(arr4.strip().split(','))
arr5 = arr[5]
if arr5:
arrAll.append(arr5.strip().split(','))
dataSet = arrAll
print 'dataSet: ', dataSet
# Apriori 算法生成频繁项集以及它们的支持度
# 这个
L1, supportData1 = apriori.apriori(dataSet, minSupport=0.2)
print 'L(0.7): ', L1
print 'supportData(0.7): ', supportData1
# 生成关联规则
dic = dict()
rules = apriori.generateRules(L1, supportData1, minConf=0.8)
print 'rules: ', rules[:10]
print type(rules)
#for i in range(0,len(rules)):
# if rules[i][0]==frozenset(['58691ed3d87f49b489feb40de28a92f9']):
# print map(str,rules[i][1])
# dic[map(str,rules[i][0])].append(map(str,rules[i][1]))
# print dic
"""
for line in sys.stdin:
arr =line.strip().split()
userID = arr[0]
downloadRes =arr[1]
if downloadRes:
arrAll.append(downloadRes.strip().split(','))
#!usr/bin/env python3 # -*- coding:utf-8 -*- """ #@author:Benny.Chen #@file: main.py #@time: 2020/6/10 11:06 #@email:[email protected] """ from pprint import pprint import apriori if __name__ == '__main__': dataSet = apriori.loadDataSet() L ,supportData = apriori.apriori(dataSet, minSupport=0.5) rules = apriori.generateRules(L, supportData) pprint(rules)
# coding:utf-8
import apriori
import time
import numpy as np
# 读取训练集
with open("./data/agaricus_train.csv", "rb") as f:
dataSet = [line[:-1].split(',') for line in f.readlines()]
# L中的每一个元素都至少在25%的样本中出现过
L, suppData = apriori.apriori(dataSet, 0.25) # 阈值越小，越慢
# 生成规则，每个规则的置信度至少是0.6
bigRuleList = apriori.generateRules(L, suppData, 0.6)
# P→H，根据P集合的大小排序
bigRuleList = sorted(bigRuleList, key=lambda x:len(x[0]), reverse=True)
# 读取测试集
with open("./data/agaricus_test.csv", "rb") as f:
dataSet = [line[:-1].split(',') for line in f.readlines()]
labels = np.array([int(x[0]) for x in dataSet])
scores = []
for line in dataSet:
tmp = []
for item in bigRuleList:
if item[0].issubset(set(line)):
if "1" in item[1]:
tmp.append(float(item[2]))
# 因为是预测“为1的概率”，所以要用1减
# 在循环中出现的前置路径，均为频繁项，因为在构建条件树的过程中已经过滤了频繁项
frequentItems.append(prefixPath)
# 获取条件基
conditionPatterns = findPrefixPath(basePattern, myTableHeader[basePattern][1])
# 根据条件基，得到条件树
conditionTree, headerTable = createTree(conditionPatterns, minSupport)
if conditionTree != None:
print('condition: ', prefixPath)
conditionTree.display(1)
mineTree(conditionTree, headerTable, minSupport, prefixPath, frequentItems)
return frequentItems
# simpleDat = loadSimpDat()
# initSet = createInitSet(simpleDat)
# myFPTree, myHeaderTab = createTree(initSet, 3)
# frequentItems = []
# mineTree(myFPTree, myHeaderTab, 3, set([]), frequentItems)
parsedData = [line.split() for line in open('./machinelearninginaction/Ch12/kosarak.dat').readlines()]
initSet = createInitSet(parsedData)
timeStart = time.time()
myFPTree, myHeaderTab = createTree(initSet, 100000)
frequentItems = []
mineTree(myFPTree, myHeaderTab, 100000, set([]), frequentItems)
print(frequentItems)
print('timeEnd:', time.time() - timeStart)
timeStart = time.time()
L, supportData = apriori.apriori(parsedData, 0.1)
apriori.generateRules(L, supportData, 0.7)
print('timeEnd:', time.time() - timeStart)
# Extract transactions and meanings
transactions = map_transactions(RAW_DATA[0])
meaning = map_meaning(RAW_MEANING[0])
for threshold in np.arange(0.5, 0.25, -0.05):
itemsets, support = apriori.apriori(transactions.values(), minSupport=threshold)
print "THRESHOLD: ", threshold
print len(itemsets), "itemsets of length:"
print [len(i) for i in itemsets]
print "\n"
itemset, support = apriori.apriori(transactions.values(), minSupport=0.3)
for threshold in np.arange(0.7, 0.99, 0.05):
print "THRESHOLD: ", threshold
rules = apriori.generateRules(itemset, support, minConf=threshold)
print "\n"
def get_meaning (rule, meaning):
condition, result = [], []
for c in rule[0]:
condition.append(meaning[c])
for r in rule[1]:
result.append(meaning[r])
print "IF:", " AND ".join(condition)
print "THEN:", " AND ".join(result)
print "CONFIDENCE: ", rule[2], "\n\n"
for i in range(6):
get_meaning(random.choice(rules), meaning)
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
ruleDict = apriori.generateRuleDict(rules)
print 'ruleDict'
for k, v in ruleDict.iteritems():
print '\t', k, "".join(
' '
for i in range(30 - len(''.join(item
for item in list(k))) - len(k) * 4)), v
print '*** *** ***'
#print 'keys', ruleDict.keys()
#print '*** *** ***'
import data
import apriori
import matplotlib.pyplot as plt
import numpy
dataSet = data.loadDataSet()
print ('size of dataSet: ', len(dataSet))
# 1)
plt.figure()
minSupports = [0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4]
cntOfRules = []
for minSupport in minSupports:
frequentItemSet, supportData = apriori.apriori(dataSet, minSupport)
rules = apriori.generateRules(frequentItemSet, supportData, 0.1 )
cntOfRules.append(len(rules))
x = numpy.array(minSupports)
y = numpy.array(cntOfRules)
plt.plot(x, y)
plt.xlabel('minimal support with 0.3 confidence')
plt.ylabel('cntOfRules')
# 2)
plt.figure()
minConfidences = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]
cntOfRules = []
frequentItemSet, supportData = apriori.apriori(dataSet, 0.1)
for minConfidence in minConfidences:
rules = apriori.generateRules(frequentItemSet, supportData, minConfidence)
cntOfRules.append(len(rules))
x = numpy.array(minConfidences)
#apriori只支持python2.7版本
from apriori import apriori
from apriori import generateRules
import re
if __name__ == "__main__":
myDat = []
with open("./data/ibook/data/xss-2000.txt") as f:
for line in f:
index = line.find("?")
tokens = re.split(
'\=|&|\?|\%3e|\%3c|\%3E|\%3C|\%20|\%22|<|>|\\n|\(|\)|\'|\"|;|:|,|\%28|\%29',
line)
myDat.append(tokens)
L, suppData = apriori(myDat, 0.15)
rules = generateRules(L, suppData, miniConf=0.5)
print(rules)
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
reload(apriori)
L, supportData = apriori.apriori(dataSet, minSupport=0.5)
rules = apriori.generateRules(L, supportData, minConf=0.7) # 0.7的可信度生成的规则
rules = apriori.generateRules(L, supportData, minConf=0.5) # 0.5的可信度生成的规则
# 在毒蘑菇的数据集上测试下效果如何
mushDataSet = [line.split() for line in open('mushroom.dat').readlines()]
# 这个数据集第一列是标签是否有毒
L, supportData = apriori.apriori(mushDataSet, minSupport=0.3)
for item in L[3]:
if item.intersection('2'): # 看下包含特征有毒为2的频繁项集
print(item)
##replace the processed data into the raw data
def mumerizeData(data):
data_birth_year = dataDiscretizeBirthyear(data)
data_weight = dataDiscretizeWeight(data)
data_height = dataDiscretizeHeight(data)
data.drop("birth_year",inplace = True ,axis = 1)
data.drop("weight",inplace = True ,axis = 1)
data.drop("height",inplace = True ,axis = 1)
data = pd.concat([data,data_birth_year,data_weight,data_height],axis=1)
return data
if __name__ == '__main__':
myDat = mumerizeData(data)
#%%
print myDat['weight']
print myDat['birth_year']
print myDat['height']
#%%
#频繁项集与支持度
sets,sp = apriori.apriori(myDat.values,4,0.6)
rules = apriori.generateRules(sets,sp,0.8)
print ("层级:频繁项集:支持度")
for Lk in sets:
for freq_set in Lk:
print(str(len(list(Lk)[0])),':',freq_set,' : ',sp[freq_set])
print("强关联规则:置信度")
for item in rules:
print (item[0], "-->>", item[1], " : ", item[2])
#%%
def test():
dataSet = apriori.loadDataSet()
print "DataSet:", dataSet
L,suppData = apriori.apriori(dataSet)
rules = apriori.generateRules(L, suppData, minConf=0.5)
print rules
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
def test1():
dataSet = apriori.loadDataSet()
C1 = apriori.createC1(dataSet)
L, supportData = apriori.apriori(dataSet, minSupport=0.5)
# print(L)
rules = apriori.generateRules(L, supportData, minConf=0.5)
data = []
for i in range(len(values)):
temp = []
for j in range(len(values[0])):
if values[i][j] == 1:
temp.append(j)
data.append(temp)
counts = []
for index in columns:
line = df[index]
count = 0
for i in range(len(line)):
if line[i] == 1:
count += 1
counts.append((float)(count) / 10000)
counts.sort()
minSupport = counts[len(counts) * 1 / 5]
#use apriori
L, supportData = ap.apriori(data, minSupport)
rules = ap.generateRules(L, supportData, minConf=0.4)
#use fpGrowth
minSup = minSupport * 10000
simpDat = data
initSet = fp.createInitSet(simpDat)
myFPtree, myHeaderTab = fp.createTree(initSet, minSup)
myFreqList = []
fp.mineTree(myFPtree, myHeaderTab, minSup, set([]), myFreqList)
print myFreqList
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]:
if item.intersection(item_of_interest): print(item)
n_other_items = n_other_items + 1
#apriori原理：可以减少可能感兴趣的项集。apriori原理是说，如果某个项集是频繁的，那么它的所有子集也是频繁的。
#反过来说，如果一个项集是非频繁集，那么它的所有超集也是非频繁集。
#问题1：为什么关联规则中，如果项集中有三个元素，为什么只计算1个 -> 2个
#而不计算2个 --->1个？？？？？？？？？？？？？？？
import apriori
from votesmart import votesmart
dataSet = apriori.loadDataSet()
#C1 = apriori.createC1(dataSet)
#print ("C1 is %s" % C1)
#D = map(set,dataSet)
#print ( "%r" % D)
#L1,suppData0 = apriori.scanD(list(D), list(C1), 0.5)
#print (L1)
#print (suppData0)
L,suppData = apriori.apriori(dataSet, 0.5)
print ("L is" , L)
print ("suppData is" , suppData)
#L is [[frozenset({1}), frozenset({3}), frozenset({2}), frozenset({5})], [frozenset({3, 5}), frozenset({1, 3}), frozenset({2, 5}), frozenset({2, 3})], [frozenset({2, 3, 5})], []]
#suppData is {frozenset({5}): 0.75, frozenset({3}): 0.75, frozenset({2, 3, 5}): 0.5, frozenset({3, 5}): 0.5, frozenset({2, 3}): 0.5, frozenset({2, 5}): 0.75, frozenset({1}): 0.5, frozenset({1, 3}): 0.5, frozenset({2}): 0.75}
#关联规则挖掘
rules = apriori.generateRules(L, suppData, 0.7)
print ("rules is " ,rules)
#关联规则对其中的3/4的记录都是适用的。
#apriori原理：可以减少可能感兴趣的项集。apriori原理是说，如果某个项集是频繁的，那么它的所有子集也是频繁的。
#反过来说，如果一个项集是非频繁集，那么它的所有超集也是非频繁集。
#问题1：为什么关联规则中，如果项集中有三个元素，为什么只计算1个 -> 2个
#而不计算2个 --->1个？？？？？？？？？？？？？？？
import apriori
from votesmart import votesmart
dataSet = apriori.loadDataSet()
#C1 = apriori.createC1(dataSet)
#print ("C1 is %s" % C1)
#D = map(set,dataSet)
#print ( "%r" % D)
#L1,suppData0 = apriori.scanD(list(D), list(C1), 0.5)
#print (L1)
#print (suppData0)
L, suppData = apriori.apriori(dataSet, 0.5)
print("L is", L)
print("suppData is", suppData)
#L is [[frozenset({1}), frozenset({3}), frozenset({2}), frozenset({5})], [frozenset({3, 5}), frozenset({1, 3}), frozenset({2, 5}), frozenset({2, 3})], [frozenset({2, 3, 5})], []]
#suppData is {frozenset({5}): 0.75, frozenset({3}): 0.75, frozenset({2, 3, 5}): 0.5, frozenset({3, 5}): 0.5, frozenset({2, 3}): 0.5, frozenset({2, 5}): 0.75, frozenset({1}): 0.5, frozenset({1, 3}): 0.5, frozenset({2}): 0.75}
#关联规则挖掘
rules = apriori.generateRules(L, suppData, 0.7)
print("rules is ", rules)
data = [] for i in range(len(values)): temp = [] for j in range(len(values[0])): if values[i][j] == 1: temp.append(j) data.append(temp) counts = [] for index in columns: line = df[index] count = 0 for i in range(len(line)): if line[i]==1: count += 1 counts.append((float)(count)/10000) counts.sort() minSupport = counts[len(counts)*1/5] #use apriori L,supportData = ap.apriori(data,minSupport) rules = ap.generateRules(L,supportData,minConf=0.4) #use fpGrowth minSup = minSupport*10000 simpDat = data initSet = fp.createInitSet(simpDat) myFPtree, myHeaderTab = fp.createTree(initSet, minSup) myFreqList = [] fp.mineTree(myFPtree, myHeaderTab, minSup, set([]), myFreqList) print myFreqList
basket_str = ""
for rowNum, row in accident_data.iterrows():
#Break lines
if (rowNum != 0):
basket_str = basket_str + "\n"
#Add the rowid as the first column
basket_str = basket_str + str(rowNum)
#Add columns
for colName, col in row.iteritems():
if (colName != 'Accident_Index'):
basket_str = basket_str + "," + colName + "=" + str(col)
print basket_str
basket_file = open("accident_basket.csv", "w")
basket_file.write(basket_str)
basket_file.close()
"""
Read the basket file now and compute rules
"""
import csv
with open('accident_basket.csv', 'rb') as f:
reader = csv.reader(f)
your_list = list(reader)
L, supportData = apriori.apriori(your_list, 0.6)
brl = apriori.generateRules(L, supportData, 0.6)
for row in brl:
print list(row[0]), " => ", list(row[1]), row[2]
import apriori dataSet = apriori.loadDataSet() L, supportData = apriori.apriori(dataSet, minSupport=0.1) print "[result]-----------------------------------------" rules = apriori.generateRules(L, supportData, minConf=1.0)
import apriori
mushDatSet = [line.split() for line in open('mushroom.dat').readlines()]
L, supportData = apriori.apriori(mushDatSet, 0.4)
rule = apriori.generateRules(L, supportData, minConf=0.5)
for item in L[3]:
if item.intersection('2'):
print(item)
