perf(code): 添加算法运行时间计时并更换数据集

- 在 Apriori 和 FP-Growth算法运行前后添加时间计时代码
- 将输入数据集从 menu_orders.xls 更改为 iris.csv
- 更新数据读取方式以适应 CSV 文件格式
-保留算法结果并输出运行时间

code/5-6_cal_apriori.py

@@ -3,10 +3,11 @@
 from __future__ import print_function
 import pandas as pd
 from apriori import * #导入自行编写的apriori函数
+import time  # 导入time模块用于计时
 
-inputfile = '../data/menu_orders.xls'
+inputfile = '../data/iris.csv'
 outputfile = '../tmp/apriori_rules.xlsx' #结果文件，保留 .xlsx 格式
-data = pd.read_excel(inputfile, header = None)
+data = pd.read_csv(inputfile, header = None)
 
 print(u'\n转换原始数据至0-1矩阵...')
 ct = lambda x : pd.Series(1, index = x[pd.notnull(x)]) #转换0-1矩阵的过渡函数
@@ -21,4 +22,9 @@ ms = '---' #连接符，默认'--'，用来区分不同元素，如A--B。需要
 
 # 提醒用户需要安装 openpyxl 库以支持 .xlsx 格式
 # 如果未安装，可以通过以下命令安装：pip install openpyxl
-find_rule(data, support, confidence, ms).to_excel(outputfile, engine='openpyxl') #保存结果，指定 engine='openpyxl'
+start_time = time.time()  # 记录开始时间
+result = find_rule(data, support, confidence, ms)
+end_time = time.time()  # 记录结束时间
+print(f'\nApriori算法运行时间: {end_time - start_time} 秒')
+
+result.to_excel(outputfile, engine='openpyxl') #保存结果，指定 engine='openpyxl'

code/5-6_cal_fpgrowth.py

@@ -3,10 +3,11 @@
 from __future__ import print_function
 import pandas as pd
 from fpgrowth import find_frequent_itemsets  # 导入FP-Growth函数
+import time  # 导入time模块用于计时
 
-inputfile = '../data/menu_orders.xls'
+inputfile = '../data/iris.csv'
 outputfile = '../tmp/fpgrowth_rules.xlsx'  # 结果文件，保留 .xlsx 格式
-data = pd.read_excel(inputfile, header=None)
+data = pd.read_csv(inputfile, header=None)
 
 print(u'\n转换原始数据至0-1矩阵...')
 ct = lambda x: pd.Series(1, index=x[pd.notnull(x)])  # 转换0-1矩阵的过渡函数
@@ -24,7 +25,10 @@ min_support = 0.2  # 最小支持度
 min_support_count = int(min_support * len(transactions))  # 转换为绝对支持度
 
 # 使用FP-Growth算法挖掘频繁项集
+start_time = time.time()  # 记录开始时间
 frequent_itemsets = find_frequent_itemsets(transactions, min_support_count)
+end_time = time.time()  # 记录结束时间
+print(f'\nFP-Growth算法运行时间: {end_time - start_time} 秒')
 
 # 确保 frequent_itemsets 是一个列表，其中每个元素是一个列表
 frequent_itemsets = [list(itemset) for itemset in frequent_itemsets]

data/iris.csv

@@ -0,0 +1,151 @@
+"","Sepal.Length","Sepal.Width","Petal.Length","Petal.Width","Species"
+"1",5.1,3.5,1.4,0.2,"setosa"
+"2",4.9,3,1.4,0.2,"setosa"
+"3",4.7,3.2,1.3,0.2,"setosa"
+"4",4.6,3.1,1.5,0.2,"setosa"
+"5",5,3.6,1.4,0.2,"setosa"
+"6",5.4,3.9,1.7,0.4,"setosa"
+"7",4.6,3.4,1.4,0.3,"setosa"
+"8",5,3.4,1.5,0.2,"setosa"
+"9",4.4,2.9,1.4,0.2,"setosa"
+"10",4.9,3.1,1.5,0.1,"setosa"
+"11",5.4,3.7,1.5,0.2,"setosa"
+"12",4.8,3.4,1.6,0.2,"setosa"
+"13",4.8,3,1.4,0.1,"setosa"
+"14",4.3,3,1.1,0.1,"setosa"
+"15",5.8,4,1.2,0.2,"setosa"
+"16",5.7,4.4,1.5,0.4,"setosa"
+"17",5.4,3.9,1.3,0.4,"setosa"
+"18",5.1,3.5,1.4,0.3,"setosa"
+"19",5.7,3.8,1.7,0.3,"setosa"
+"20",5.1,3.8,1.5,0.3,"setosa"
+"21",5.4,3.4,1.7,0.2,"setosa"
+"22",5.1,3.7,1.5,0.4,"setosa"
+"23",4.6,3.6,1,0.2,"setosa"
+"24",5.1,3.3,1.7,0.5,"setosa"
+"25",4.8,3.4,1.9,0.2,"setosa"
+"26",5,3,1.6,0.2,"setosa"
+"27",5,3.4,1.6,0.4,"setosa"
+"28",5.2,3.5,1.5,0.2,"setosa"
+"29",5.2,3.4,1.4,0.2,"setosa"
+"30",4.7,3.2,1.6,0.2,"setosa"
+"31",4.8,3.1,1.6,0.2,"setosa"
+"32",5.4,3.4,1.5,0.4,"setosa"
+"33",5.2,4.1,1.5,0.1,"setosa"
+"34",5.5,4.2,1.4,0.2,"setosa"
+"35",4.9,3.1,1.5,0.2,"setosa"
+"36",5,3.2,1.2,0.2,"setosa"
+"37",5.5,3.5,1.3,0.2,"setosa"
+"38",4.9,3.6,1.4,0.1,"setosa"
+"39",4.4,3,1.3,0.2,"setosa"
+"40",5.1,3.4,1.5,0.2,"setosa"
+"41",5,3.5,1.3,0.3,"setosa"
+"42",4.5,2.3,1.3,0.3,"setosa"
+"43",4.4,3.2,1.3,0.2,"setosa"
+"44",5,3.5,1.6,0.6,"setosa"
+"45",5.1,3.8,1.9,0.4,"setosa"
+"46",4.8,3,1.4,0.3,"setosa"
+"47",5.1,3.8,1.6,0.2,"setosa"
+"48",4.6,3.2,1.4,0.2,"setosa"
+"49",5.3,3.7,1.5,0.2,"setosa"
+"50",5,3.3,1.4,0.2,"setosa"
+"51",7,3.2,4.7,1.4,"versicolor"
+"52",6.4,3.2,4.5,1.5,"versicolor"
+"53",6.9,3.1,4.9,1.5,"versicolor"
+"54",5.5,2.3,4,1.3,"versicolor"
+"55",6.5,2.8,4.6,1.5,"versicolor"
+"56",5.7,2.8,4.5,1.3,"versicolor"
+"57",6.3,3.3,4.7,1.6,"versicolor"
+"58",4.9,2.4,3.3,1,"versicolor"
+"59",6.6,2.9,4.6,1.3,"versicolor"
+"60",5.2,2.7,3.9,1.4,"versicolor"
+"61",5,2,3.5,1,"versicolor"
+"62",5.9,3,4.2,1.5,"versicolor"
+"63",6,2.2,4,1,"versicolor"
+"64",6.1,2.9,4.7,1.4,"versicolor"
+"65",5.6,2.9,3.6,1.3,"versicolor"
+"66",6.7,3.1,4.4,1.4,"versicolor"
+"67",5.6,3,4.5,1.5,"versicolor"
+"68",5.8,2.7,4.1,1,"versicolor"
+"69",6.2,2.2,4.5,1.5,"versicolor"
+"70",5.6,2.5,3.9,1.1,"versicolor"
+"71",5.9,3.2,4.8,1.8,"versicolor"
+"72",6.1,2.8,4,1.3,"versicolor"
+"73",6.3,2.5,4.9,1.5,"versicolor"
+"74",6.1,2.8,4.7,1.2,"versicolor"
+"75",6.4,2.9,4.3,1.3,"versicolor"
+"76",6.6,3,4.4,1.4,"versicolor"
+"77",6.8,2.8,4.8,1.4,"versicolor"
+"78",6.7,3,5,1.7,"versicolor"
+"79",6,2.9,4.5,1.5,"versicolor"
+"80",5.7,2.6,3.5,1,"versicolor"
+"81",5.5,2.4,3.8,1.1,"versicolor"
+"82",5.5,2.4,3.7,1,"versicolor"
+"83",5.8,2.7,3.9,1.2,"versicolor"
+"84",6,2.7,5.1,1.6,"versicolor"
+"85",5.4,3,4.5,1.5,"versicolor"
+"86",6,3.4,4.5,1.6,"versicolor"
+"87",6.7,3.1,4.7,1.5,"versicolor"
+"88",6.3,2.3,4.4,1.3,"versicolor"
+"89",5.6,3,4.1,1.3,"versicolor"
+"90",5.5,2.5,4,1.3,"versicolor"
+"91",5.5,2.6,4.4,1.2,"versicolor"
+"92",6.1,3,4.6,1.4,"versicolor"
+"93",5.8,2.6,4,1.2,"versicolor"
+"94",5,2.3,3.3,1,"versicolor"
+"95",5.6,2.7,4.2,1.3,"versicolor"
+"96",5.7,3,4.2,1.2,"versicolor"
+"97",5.7,2.9,4.2,1.3,"versicolor"
+"98",6.2,2.9,4.3,1.3,"versicolor"
+"99",5.1,2.5,3,1.1,"versicolor"
+"100",5.7,2.8,4.1,1.3,"versicolor"
+"101",6.3,3.3,6,2.5,"virginica"
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+"103",7.1,3,5.9,2.1,"virginica"
+"104",6.3,2.9,5.6,1.8,"virginica"
+"105",6.5,3,5.8,2.2,"virginica"
+"106",7.6,3,6.6,2.1,"virginica"
+"107",4.9,2.5,4.5,1.7,"virginica"
+"108",7.3,2.9,6.3,1.8,"virginica"
+"109",6.7,2.5,5.8,1.8,"virginica"
+"110",7.2,3.6,6.1,2.5,"virginica"
+"111",6.5,3.2,5.1,2,"virginica"
+"112",6.4,2.7,5.3,1.9,"virginica"
+"113",6.8,3,5.5,2.1,"virginica"
+"114",5.7,2.5,5,2,"virginica"
+"115",5.8,2.8,5.1,2.4,"virginica"
+"116",6.4,3.2,5.3,2.3,"virginica"
+"117",6.5,3,5.5,1.8,"virginica"
+"118",7.7,3.8,6.7,2.2,"virginica"
+"119",7.7,2.6,6.9,2.3,"virginica"
+"120",6,2.2,5,1.5,"virginica"
+"121",6.9,3.2,5.7,2.3,"virginica"
+"122",5.6,2.8,4.9,2,"virginica"
+"123",7.7,2.8,6.7,2,"virginica"
+"124",6.3,2.7,4.9,1.8,"virginica"
+"125",6.7,3.3,5.7,2.1,"virginica"
+"126",7.2,3.2,6,1.8,"virginica"
+"127",6.2,2.8,4.8,1.8,"virginica"
+"128",6.1,3,4.9,1.8,"virginica"
+"129",6.4,2.8,5.6,2.1,"virginica"
+"130",7.2,3,5.8,1.6,"virginica"
+"131",7.4,2.8,6.1,1.9,"virginica"
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