From 6298c14f78fc36f935868054023ccab6e5610648 Mon Sep 17 00:00:00 2001 From: fly6516 Date: Fri, 14 Mar 2025 11:40:29 +0800 Subject: [PATCH] =?UTF-8?q?perf(code):=20=E6=B7=BB=E5=8A=A0=E7=AE=97?= =?UTF-8?q?=E6=B3=95=E8=BF=90=E8=A1=8C=E6=97=B6=E9=97=B4=E8=AE=A1=E6=97=B6?= =?UTF-8?q?=E5=B9=B6=E6=9B=B4=E6=8D=A2=E6=95=B0=E6=8D=AE=E9=9B=86?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - 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 | 12 +++- code/5-6_cal_fpgrowth.py | 10 ++- data/iris.csv | 151 +++++++++++++++++++++++++++++++++++++++ tmp/apriori_rules.xlsx | Bin 5159 -> 5591 bytes tmp/fpgrowth_rules.xlsx | Bin 5061 -> 5692 bytes 5 files changed, 167 insertions(+), 6 deletions(-) create mode 100644 data/iris.csv diff --git a/code/5-6_cal_apriori.py b/code/5-6_cal_apriori.py index 9c01607..56a569a 100644 --- a/code/5-6_cal_apriori.py +++ b/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' \ No newline at end of file +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' \ No newline at end of file diff --git a/code/5-6_cal_fpgrowth.py b/code/5-6_cal_fpgrowth.py index d7bb260..8869bc3 100644 --- a/code/5-6_cal_fpgrowth.py +++ b/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] @@ -38,4 +42,4 @@ for itemset in frequent_itemsets: result = pd.DataFrame(result_data) result.to_excel(outputfile, engine='openpyxl') # 保存结果，指定 engine='openpyxl' -print(u'\nFP-Growth算法运行完毕，结果已保存至:', outputfile) +print(u'\nFP-Growth算法运行完毕，结果已保存至:', outputfile) \ No newline at end of file diff --git a/data/iris.csv b/data/iris.csv new file mode 100644 index 0000000..a7055de --- /dev/null +++ b/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" 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