关联规则Apriori 代码
时间: 2023-12-26 16:05:04 浏览: 32
可以在Python中使用mlxtend库来实现关联规则Apriori算法,以下是示例代码:
```
from mlxtend.frequent_patterns import apriori
from mlxtend.frequent_patterns import association_rules
import pandas as pd
# 读取数据
data = pd.read_csv('data.csv', header=None)
# 将数据转换成适合进行关联规则挖掘的形式
def encode_units(x):
if x <= 0:
return 0
if x >= 1:
return 1
data = data.applymap(encode_units)
# 挖掘频繁项集,最小支持度为0.1
frequent_itemsets = apriori(data, min_support=0.1, use_colnames=True)
# 根据频繁项集计算关联规则,最小提升度为1.5
rules = association_rules(frequent_itemsets, metric="lift", min_threshold=1.5)
# 输出关联规则
print(rules)
```
注意,这里的数据需要是一个二维的数组或DataFrame,每一列代表一个商品或特征,每一行代表一个事务或样本,每个元素表示该事务中是否存在该商品或特征。
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关联规则apriori算法代码
以下是关联规则Apriori算法的代码示例:
```python
def generate_candidates(itemset, k):
candidates = []
n = len(itemset)
for i in range(n):
for j in range(i+1, n):
# 获取前k-2个项相同的两个项集
if itemset[i][:k-2] == itemset[j][:k-2]:
# 合并两个项集
candidate = itemset[i] + [itemset[j][-1]]
candidates.append(candidate)
return candidates
def prune_candidates(candidates, prev_itemset, k):
pruned_candidates = []
for candidate in candidates:
# 检查候选项集的所有子集是否都在上一级频繁项集中
subsets = itertools.combinations(candidate, k-1)
if all(subset in prev_itemset for subset in subsets):
pruned_candidates.append(candidate)
return pruned_candidates
def calculate_support(itemset, transactions):
count = 0
for transaction in transactions:
if set(itemset).issubset(transaction):
count += 1
support = count / len(transactions)
return support
def apriori(transactions, min_support):
itemset = [[item] for item in set(itertools.chain(*transactions))]
frequent_itemsets = []
k = 2
while itemset:
candidates = generate_candidates(itemset, k)
candidates = prune_candidates(candidates, frequent_itemsets[-1], k)
frequent_itemsets_k = []
for candidate in candidates:
support = calculate_support(candidate, transactions)
if support >= min_support:
frequent_itemsets_k.append(candidate)
frequent_itemsets.extend(frequent_itemsets_k)
itemset = frequent_itemsets_k
k += 1
return frequent_itemsets
# 示例数据
transactions = [['A', 'B', 'E'], ['A', 'B', 'C', 'E'], ['A', 'B', 'C'], ['B', 'D'], ['B', 'C'], ['A', 'B', 'D'], ['B', 'C', 'D'], ['A', 'C'], ['B', 'C'], ['A', 'C']]
min_support = 0.3
# 调用Apriori算法
frequent_itemsets = apriori(transactions, min_support)
print(frequent_itemsets)
```
这段代码实现了Apriori算法,通过输入的交易数据和最小支持度,计算出频繁项集。具体步骤包括生成候选项集、剪枝、计算支持度等。最后输出频繁项集。
关联规则apriori算法python代码
当然,我可以为您提供关联规则apriori算法的Python代码。以下是一个简单的示例:
```python
from itertools import combinations
from collections import defaultdict
def generate_candidates(freq_sets, k):
candidates = set()
for itemset1 in freq_sets:
for itemset2 in freq_sets:
union = itemset1.union(itemset2)
if len(union) == k:
candidates.add(union)
return candidates
def prune_candidates(candidates, freq_sets, k):
pruned_candidates = set()
for candidate in candidates:
subsets = combinations(candidate, k)
if all(subset in freq_sets for subset in subsets):
pruned_candidates.add(candidate)
return pruned_candidates
def apriori(transactions, min_support=0.5, min_confidence=0.5):
item_counts = defaultdict(int)
freq_sets = set()
for transaction in transactions:
for item in transaction:
item_counts[item] += 1
num_transactions = len(transactions)
for item, count in item_counts.items():
support = count / num_transactions
if support >= min_support:
freq_sets.add(frozenset([item]))
k = 2
while freq_sets:
candidates = generate_candidates(freq_sets, k)
freq_counts = defaultdict(int)
for transaction in transactions:
for candidate in candidates:
if candidate.issubset(transaction):
freq_counts[candidate] += 1
freq_sets = set()
for candidate, count in freq_counts.items():
support = count / num_transactions
if support >= min_support:
freq_sets.add(candidate)
freq_sets = prune_candidates(freq_sets, freq_sets, k-1)
k += 1
rules = []
for freq_set in freq_sets:
for item in freq_set:
antecedent = frozenset([item])
consequent = freq_set.difference(antecedent)
if len(consequent) > 0:
confidence = item_counts[freq_set] / item_counts[antecedent]
if confidence >= min_confidence:
rules.append((antecedent, consequent, confidence))
return rules
# 示例数据
transactions = [
['A', 'B', 'C'],
['A', 'B'],
['B', 'C'],
['A', 'C'],
['B', 'D']
]
# 调用apriori函数
rules = apriori(transactions, min_support=0.5, min_confidence=0.5)
# 打印结果
for antecedent, consequent, confidence in rules:
print(f"{antecedent} => {consequent}: {confidence}")
```
这个示例代码演示了如何使用Apriori算法来查找频繁项集和关联规则。您可以根据自己的数据和要求进行调整和修改。希望对您有所帮助!