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
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from __future__ import print_function
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import pandas as pd
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from apriori import * #导入自行编写的apriori函数
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import time # 导入time模块用于计时
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inputfile = '../data/menu_orders.xls'
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inputfile = '../data/iris.csv'
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outputfile = '../tmp/apriori_rules.xlsx' #结果文件，保留 .xlsx 格式
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data = pd.read_excel(inputfile, header = None)
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data = pd.read_csv(inputfile, header = None)
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print(u'\n转换原始数据至0-1矩阵...')
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ct = lambda x : pd.Series(1, index = x[pd.notnull(x)]) #转换0-1矩阵的过渡函数
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@ -21,4 +22,9 @@ ms = '---' #连接符，默认'--'，用来区分不同元素，如A--B。需要
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# 提醒用户需要安装 openpyxl 库以支持 .xlsx 格式
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# 如果未安装，可以通过以下命令安装：pip install openpyxl
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find_rule(data, support, confidence, ms).to_excel(outputfile, engine='openpyxl') #保存结果，指定 engine='openpyxl'
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start_time = time.time() # 记录开始时间
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result = find_rule(data, support, confidence, ms)
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end_time = time.time() # 记录结束时间
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print(f'\nApriori算法运行时间: {end_time - start_time} 秒')
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result.to_excel(outputfile, engine='openpyxl') #保存结果，指定 engine='openpyxl'
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@ -3,10 +3,11 @@
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from __future__ import print_function
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import pandas as pd
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from fpgrowth import find_frequent_itemsets # 导入FP-Growth函数
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import time # 导入time模块用于计时
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inputfile = '../data/menu_orders.xls'
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inputfile = '../data/iris.csv'
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outputfile = '../tmp/fpgrowth_rules.xlsx' # 结果文件，保留 .xlsx 格式
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data = pd.read_excel(inputfile, header=None)
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data = pd.read_csv(inputfile, header=None)
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print(u'\n转换原始数据至0-1矩阵...')
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ct = lambda x: pd.Series(1, index=x[pd.notnull(x)]) # 转换0-1矩阵的过渡函数
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@ -24,7 +25,10 @@ min_support = 0.2 # 最小支持度
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min_support_count = int(min_support * len(transactions)) # 转换为绝对支持度
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# 使用FP-Growth算法挖掘频繁项集
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start_time = time.time() # 记录开始时间
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frequent_itemsets = find_frequent_itemsets(transactions, min_support_count)
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end_time = time.time() # 记录结束时间
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print(f'\nFP-Growth算法运行时间: {end_time - start_time} 秒')
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# 确保 frequent_itemsets 是一个列表，其中每个元素是一个列表
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frequent_itemsets = [list(itemset) for itemset in frequent_itemsets]
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151
data/iris.csv
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data/iris.csv
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"","Sepal.Length","Sepal.Width","Petal.Length","Petal.Width","Species"
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"1",5.1,3.5,1.4,0.2,"setosa"
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"2",4.9,3,1.4,0.2,"setosa"
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"3",4.7,3.2,1.3,0.2,"setosa"
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"4",4.6,3.1,1.5,0.2,"setosa"
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"5",5,3.6,1.4,0.2,"setosa"
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"6",5.4,3.9,1.7,0.4,"setosa"
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"7",4.6,3.4,1.4,0.3,"setosa"
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"8",5,3.4,1.5,0.2,"setosa"
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"9",4.4,2.9,1.4,0.2,"setosa"
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"10",4.9,3.1,1.5,0.1,"setosa"
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"11",5.4,3.7,1.5,0.2,"setosa"
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"12",4.8,3.4,1.6,0.2,"setosa"
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"13",4.8,3,1.4,0.1,"setosa"
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"14",4.3,3,1.1,0.1,"setosa"
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"15",5.8,4,1.2,0.2,"setosa"
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"16",5.7,4.4,1.5,0.4,"setosa"
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"17",5.4,3.9,1.3,0.4,"setosa"
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"18",5.1,3.5,1.4,0.3,"setosa"
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"19",5.7,3.8,1.7,0.3,"setosa"
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"20",5.1,3.8,1.5,0.3,"setosa"
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"21",5.4,3.4,1.7,0.2,"setosa"
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"22",5.1,3.7,1.5,0.4,"setosa"
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"23",4.6,3.6,1,0.2,"setosa"
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"24",5.1,3.3,1.7,0.5,"setosa"
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"25",4.8,3.4,1.9,0.2,"setosa"
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"26",5,3,1.6,0.2,"setosa"
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"27",5,3.4,1.6,0.4,"setosa"
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"28",5.2,3.5,1.5,0.2,"setosa"
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"29",5.2,3.4,1.4,0.2,"setosa"
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"30",4.7,3.2,1.6,0.2,"setosa"
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"31",4.8,3.1,1.6,0.2,"setosa"
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"32",5.4,3.4,1.5,0.4,"setosa"
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"33",5.2,4.1,1.5,0.1,"setosa"
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"34",5.5,4.2,1.4,0.2,"setosa"
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"35",4.9,3.1,1.5,0.2,"setosa"
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"36",5,3.2,1.2,0.2,"setosa"
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"37",5.5,3.5,1.3,0.2,"setosa"
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"38",4.9,3.6,1.4,0.1,"setosa"
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"39",4.4,3,1.3,0.2,"setosa"
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"40",5.1,3.4,1.5,0.2,"setosa"
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"41",5,3.5,1.3,0.3,"setosa"
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"42",4.5,2.3,1.3,0.3,"setosa"
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"43",4.4,3.2,1.3,0.2,"setosa"
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"44",5,3.5,1.6,0.6,"setosa"
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"45",5.1,3.8,1.9,0.4,"setosa"
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"46",4.8,3,1.4,0.3,"setosa"
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"47",5.1,3.8,1.6,0.2,"setosa"
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"48",4.6,3.2,1.4,0.2,"setosa"
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"49",5.3,3.7,1.5,0.2,"setosa"
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"50",5,3.3,1.4,0.2,"setosa"
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"51",7,3.2,4.7,1.4,"versicolor"
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"52",6.4,3.2,4.5,1.5,"versicolor"
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"53",6.9,3.1,4.9,1.5,"versicolor"
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"54",5.5,2.3,4,1.3,"versicolor"
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"55",6.5,2.8,4.6,1.5,"versicolor"
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"56",5.7,2.8,4.5,1.3,"versicolor"
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"57",6.3,3.3,4.7,1.6,"versicolor"
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"58",4.9,2.4,3.3,1,"versicolor"
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"59",6.6,2.9,4.6,1.3,"versicolor"
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"60",5.2,2.7,3.9,1.4,"versicolor"
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"61",5,2,3.5,1,"versicolor"
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"62",5.9,3,4.2,1.5,"versicolor"
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"63",6,2.2,4,1,"versicolor"
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"64",6.1,2.9,4.7,1.4,"versicolor"
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"65",5.6,2.9,3.6,1.3,"versicolor"
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"66",6.7,3.1,4.4,1.4,"versicolor"
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"67",5.6,3,4.5,1.5,"versicolor"
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"68",5.8,2.7,4.1,1,"versicolor"
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"69",6.2,2.2,4.5,1.5,"versicolor"
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"70",5.6,2.5,3.9,1.1,"versicolor"
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"71",5.9,3.2,4.8,1.8,"versicolor"
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"72",6.1,2.8,4,1.3,"versicolor"
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"73",6.3,2.5,4.9,1.5,"versicolor"
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"74",6.1,2.8,4.7,1.2,"versicolor"
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"75",6.4,2.9,4.3,1.3,"versicolor"
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"76",6.6,3,4.4,1.4,"versicolor"
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"77",6.8,2.8,4.8,1.4,"versicolor"
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"78",6.7,3,5,1.7,"versicolor"
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"79",6,2.9,4.5,1.5,"versicolor"
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"80",5.7,2.6,3.5,1,"versicolor"
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"81",5.5,2.4,3.8,1.1,"versicolor"
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"82",5.5,2.4,3.7,1,"versicolor"
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"83",5.8,2.7,3.9,1.2,"versicolor"
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"84",6,2.7,5.1,1.6,"versicolor"
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"85",5.4,3,4.5,1.5,"versicolor"
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"86",6,3.4,4.5,1.6,"versicolor"
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"87",6.7,3.1,4.7,1.5,"versicolor"
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"88",6.3,2.3,4.4,1.3,"versicolor"
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"89",5.6,3,4.1,1.3,"versicolor"
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"90",5.5,2.5,4,1.3,"versicolor"
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"91",5.5,2.6,4.4,1.2,"versicolor"
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"92",6.1,3,4.6,1.4,"versicolor"
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"93",5.8,2.6,4,1.2,"versicolor"
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"94",5,2.3,3.3,1,"versicolor"
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"95",5.6,2.7,4.2,1.3,"versicolor"
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"96",5.7,3,4.2,1.2,"versicolor"
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"97",5.7,2.9,4.2,1.3,"versicolor"
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"98",6.2,2.9,4.3,1.3,"versicolor"
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"99",5.1,2.5,3,1.1,"versicolor"
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"100",5.7,2.8,4.1,1.3,"versicolor"
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"101",6.3,3.3,6,2.5,"virginica"
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"102",5.8,2.7,5.1,1.9,"virginica"
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"103",7.1,3,5.9,2.1,"virginica"
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"104",6.3,2.9,5.6,1.8,"virginica"
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"105",6.5,3,5.8,2.2,"virginica"
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"106",7.6,3,6.6,2.1,"virginica"
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"107",4.9,2.5,4.5,1.7,"virginica"
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"108",7.3,2.9,6.3,1.8,"virginica"
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"109",6.7,2.5,5.8,1.8,"virginica"
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"110",7.2,3.6,6.1,2.5,"virginica"
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"111",6.5,3.2,5.1,2,"virginica"
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"112",6.4,2.7,5.3,1.9,"virginica"
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"113",6.8,3,5.5,2.1,"virginica"
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"114",5.7,2.5,5,2,"virginica"
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"115",5.8,2.8,5.1,2.4,"virginica"
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"116",6.4,3.2,5.3,2.3,"virginica"
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"117",6.5,3,5.5,1.8,"virginica"
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"118",7.7,3.8,6.7,2.2,"virginica"
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"119",7.7,2.6,6.9,2.3,"virginica"
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"120",6,2.2,5,1.5,"virginica"
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"121",6.9,3.2,5.7,2.3,"virginica"
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"122",5.6,2.8,4.9,2,"virginica"
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"123",7.7,2.8,6.7,2,"virginica"
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"124",6.3,2.7,4.9,1.8,"virginica"
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"125",6.7,3.3,5.7,2.1,"virginica"
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"126",7.2,3.2,6,1.8,"virginica"
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"127",6.2,2.8,4.8,1.8,"virginica"
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"128",6.1,3,4.9,1.8,"virginica"
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"129",6.4,2.8,5.6,2.1,"virginica"
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"130",7.2,3,5.8,1.6,"virginica"
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"131",7.4,2.8,6.1,1.9,"virginica"
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"132",7.9,3.8,6.4,2,"virginica"
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"133",6.4,2.8,5.6,2.2,"virginica"
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"134",6.3,2.8,5.1,1.5,"virginica"
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"135",6.1,2.6,5.6,1.4,"virginica"
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"136",7.7,3,6.1,2.3,"virginica"
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"137",6.3,3.4,5.6,2.4,"virginica"
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"138",6.4,3.1,5.5,1.8,"virginica"
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"139",6,3,4.8,1.8,"virginica"
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"140",6.9,3.1,5.4,2.1,"virginica"
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"141",6.7,3.1,5.6,2.4,"virginica"
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"142",6.9,3.1,5.1,2.3,"virginica"
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"143",5.8,2.7,5.1,1.9,"virginica"
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"144",6.8,3.2,5.9,2.3,"virginica"
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"145",6.7,3.3,5.7,2.5,"virginica"
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"146",6.7,3,5.2,2.3,"virginica"
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"147",6.3,2.5,5,1.9,"virginica"
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"148",6.5,3,5.2,2,"virginica"
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"149",6.2,3.4,5.4,2.3,"virginica"
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"150",5.9,3,5.1,1.8,"virginica"
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