用鸢尾花csv文件做CBA（关联规则分类）分类算法，得出准确度，精确度，召回率，F1值。

首先，CBA（Classification Based on Association rule）是一种基于关联规则的分类算法。在使用CBA算法进行分类之前，需要先进行关联规则挖掘。

步骤如下：

1. 导入数据集

import pandas as pd
data = pd.read_csv('iris.csv')

2. 数据预处理

# 将分类变量转换为数值变量
data['species'] = data['species'].map({'setosa': 0, 'versicolor': 1, 'virginica': 2})

# 将数据集划分为训练集和测试集
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(data.iloc[:, :-1], data.iloc[:, -1], test_size=0.2, random_state=42)

3. 关联规则挖掘

!pip install pyfpgrowth
import pyfpgrowth
patterns = pyfpgrowth.find_frequent_patterns(X_train.values.tolist(), 2)
rules = pyfpgrowth.generate_association_rules(patterns, 0.7) # 可调整置信度阈值

4. 基于关联规则的分类

def classify(rules, instance):
    classification = None
    max_confidence = 0
    for rule in rules:
        if set(rule[0]).issubset(set(instance)):
            confidence = rule[1]
            if confidence > max_confidence:
                max_confidence = confidence
                classification = rule[2]
    return classification

y_pred = []
for instance in X_test.values.tolist():
    y_pred.append(classify(rules, instance))

# 计算分类准确度、精确度、召回率、F1值
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
print('Accuracy:', accuracy_score(y_test, y_pred))
print('Precision:', precision_score(y_test, y_pred, average='macro'))
print('Recall:', recall_score(y_test, y_pred, average='macro'))
print('F1 Score:', f1_score(y_test, y_pred, average='macro'))

这里使用了pyfpgrowth库来进行关联规则挖掘，同时计算分类指标使用了sklearn库。