def Fp_Growth_Plus(path, min_support, min_conf, name):
#设置存储的log文件名称
save_path=settings.MEDIA_ROOT+"/log/"+name.split("/")[-1].split(".")[0]+"_fp_growth_plus.txt"
filename_rule = name.split("/")[-1].split(".")[0]+"_fp_growth_plus"
data = data_mining.load_data(path)
apriori_plus = data_mining.Fp_growth_plus()
rule_lists = apriori_plus.generate_R(data, min_support, min_conf)
#将结果存储到文本文件里面
if not os.path.exists(settings.MEDIA_ROOT+"/log"):
os.mkdir(settings.MEDIA_ROOT+"/log")
data_mining.save_rule(rule_lists,save_path)
#去掉frozenset
#round(item[2],2)保留小数点后两位
rules=[]
tmp=[]
for item in rule_lists:
tmp = [str(list(item[0])), str(list(item[1])), round(item[2],2)]
AssociationRule.objects.create(antecedent = tmp[0], consequent=tmp[1],conf = tmp[2], filename = filename_rule, min_support=min_support, min_conf=min_conf)
rules.append(tmp)
def Apriori(path, min_support, min_conf, name):
#设置存储的log文件名称
save_path=settings.MEDIA_ROOT+"/log/"+name.split("/")[-1].split(".")[0]+"_apriori.txt"
filename_rule = name.split("/")[-1].split(".")[0]+"_apriori"
data = data_mining.load_data(path)
apriori = data_mining.Apriori()
#算出购买的商品以及购买次数
#C1 = apriori.create_c1(data)
#support_data = apriori.generate_lk_by_ck(data, C1, min_support, support_data)
#txt = os.path.join(settings.MEDIA_ROOT, '/files/groceries_fpgrowth.txt')
rule_lists = apriori.generate_R(data, min_support, min_conf)
#将结果存储到文本文件里面
if not os.path.exists(settings.MEDIA_ROOT+"/log"):
os.mkdir(settings.MEDIA_ROOT+"/log")
data_mining.save_rule(rule_lists,save_path)
#去掉frozenset
#round(item[2],2)保留小数点后两位
rules=[]
tmp=[]
for item in rule_lists:
tmp = [str(list(item[0])), str(list(item[1])), round(item[2],2)]
AssociationRule.objects.create(antecedent = tmp[0], consequent=tmp[1],conf = tmp[2], filename = filename_rule, min_support=min_support, min_conf=min_conf)
rules.append(tmp)
def analysis_detail(request, name):
Tempalte = 'mainpage/analysis_detail.html'
if request.method == 'POST':
path = os.path.join(settings.MEDIA_ROOT, name)
min_support = float(request.POST.get('min_support', '25'))#最小支持度
min_conf = float(request.POST.get('min_conf', '0.7'))#最小置信度
file_name = name.split("/")[-1]
algorithm = ['Apriori','Fp_growth','Apriori_Compress', 'Apriori_Hash','Apriori_Plus','Fp_Growth_Plus']
#------------------------------------------------------------------------------------------#
#检测是否已经生成过该数据的apriori关联规则
filename_apriori_rule = name.split("/")[-1].split(".")[0]+"_apriori"
apriori_rulelists = AssociationRule.objects.filter(filename=filename_apriori_rule, min_support=min_support, min_conf=min_conf)
if not apriori_rulelists:
#计算算法运行时间
apriori_start_time = time.perf_counter()
Apriori(path, min_support,min_conf,name)
apriori_end_time = time.perf_counter()
apriori_time = apriori_end_time - apriori_start_time
AssociationTimeAdd(file_name, algorithm[0], min_support, min_conf, apriori_time)
apriori_rulelists = AssociationRule.objects.filter(filename=filename_apriori_rule, min_support=min_support, min_conf=min_conf)
#------------------------------------------------------------------------------------------#
#算出购买的商品以及购买次数
filename_commodity = name.split("/")[-1].split(".")[0]
file_commodities = Commodity.objects.filter(filename=filename_commodity)
if not file_commodities:
support_data = {}
apriori = data_mining.Apriori()
data = data_mining.load_data(path)
C1 = apriori.create_c1(data)
L1 = apriori.generate_lk_by_ck(data, C1, 0, support_data)
for key,value in support_data.items():
tmp = [str(list(key)[0]), value]
Commodity.objects.create(name=tmp[0],num=tmp[1], filename = filename_commodity)
file_commodities = Commodity.objects.filter(filename=filename_commodity)
#------------------------------------------------------------------------------------------#
#检测是否已经生成过该数据的fp-growth关联规则
filename_fp_rule = name.split("/")[-1].split(".")[0]+"_fpgrowth"
fp_rulelists = AssociationRule.objects.filter(filename=filename_fp_rule, min_support=min_support, min_conf=min_conf)
if not fp_rulelists:
fp_growth_start_time = time.perf_counter()
Fp_growth(path, min_support,min_conf,name)
fp_growth_end_time = time.perf_counter()
fp_growth_time = fp_growth_end_time - fp_growth_start_time
AssociationTimeAdd(file_name, algorithm[1], min_support, min_conf, fp_growth_time)
fp_rulelists = AssociationRule.objects.filter(filename=filename_fp_rule,min_support=min_support, min_conf=min_conf)
#------------------------------------------------------------------------------------------#
#检测是否已经生成过该数据的apriori_compress关联规则
filename_apriori_compress_rule = name.split("/")[-1].split(".")[0]+"_apriori_compress"
apriori_compress_rulelists = AssociationRule.objects.filter(filename=filename_apriori_compress_rule, min_support=min_support, min_conf=min_conf)
if not apriori_compress_rulelists:
apriori_compress_start_time = time.perf_counter()
Apriori_Compress(path, min_support,min_conf,name)
apriori_compress_end_time = time.perf_counter()
apriori_compress_time = apriori_compress_end_time - apriori_compress_start_time
AssociationTimeAdd(file_name, algorithm[2], min_support, min_conf, apriori_compress_time)
apriori_compress_rulelists = AssociationRule.objects.filter(filename=filename_apriori_compress_rule, min_support=min_support, min_conf=min_conf)
#------------------------------------------------------------------------------------------#
#检测是否已经生成过该数据的apriori_hash关联规则
filename_apriori_hash_rule = name.split("/")[-1].split(".")[0]+"_apriori_hash"
apriori_hash_rulelists = AssociationRule.objects.filter(filename=filename_apriori_hash_rule, min_support=min_support, min_conf=min_conf)
if not apriori_hash_rulelists:
apriori_hash_start_time = time.perf_counter()
Apriori_Hash(path, min_support,min_conf,name)
apriori_hash_end_time = time.perf_counter()
apriori_hash_time = apriori_hash_end_time - apriori_hash_start_time
AssociationTimeAdd(file_name, algorithm[3], min_support, min_conf, apriori_hash_time)
apriori_hash_rulelists = AssociationRule.objects.filter(filename=filename_apriori_hash_rule, min_support=min_support, min_conf=min_conf)
#------------------------------------------------------------------------------------------#
#检测是否已经生成过该数据的apriori_plus关联规则
filename_apriori_plus_rule = name.split("/")[-1].split(".")[0]+"_apriori_plus"
apriori_plus_rulelists = AssociationRule.objects.filter(filename=filename_apriori_plus_rule, min_support=min_support, min_conf=min_conf)
if not apriori_plus_rulelists:
apriori_plus_start_time = time.perf_counter()
Apriori_Plus(path, min_support,min_conf,name)
apriori_plus_end_time = time.perf_counter()
apriori_plus_time = apriori_plus_end_time - apriori_plus_start_time
AssociationTimeAdd(file_name, algorithm[4], min_support, min_conf, apriori_plus_time)
apriori_plus_rulelists = AssociationRule.objects.filter(filename=filename_apriori_plus_rule, min_support=min_support, min_conf=min_conf)
#------------------------------------------------------------------------------------------#
#检测是否已经生成过该数据的fp_growth_plus关联规则
filename_fp_growth_plus_rule = name.split("/")[-1].split(".")[0]+"_fp_growth_plus"
fp_growth_plus_rulelists = AssociationRule.objects.filter(filename=filename_fp_growth_plus_rule, min_support=min_support, min_conf=min_conf)
if not fp_growth_plus_rulelists:
fp_growth_plus_start_time = time.perf_counter()
Fp_Growth_Plus(path, min_support,min_conf,name)
fp_growth_plus_end_time = time.perf_counter()
fp_growth_plus_time = fp_growth_plus_end_time - fp_growth_plus_start_time
AssociationTimeAdd(file_name, algorithm[5], min_support, min_conf, fp_growth_plus_time)
fp_growth_plus_rulelists = AssociationRule.objects.filter(filename=filename_fp_growth_plus_rule, min_support=min_support, min_conf=min_conf)
#------------------------------------------------------------------------------------------#
#算法时间进行对比
algorithm_time_lists = AssociationTime.objects.filter(filename = file_name, min_support=min_support, min_conf=min_conf)
#
return render(request, Tempalte , locals())