The tool is designed to mine behavior patterns
Project description
BahaviorTool是一个Python包,提供了CombinePattern、ContinuePattern和SequencePattern三个类的实现行为模式挖掘。
安装
你可以使用pip安装BahaviorPattern:
pip install BahaviorPattern
使用
在你的Python代码中引入类,例如:
from BahaviorPattern import CombinePattern, ContinuePattern, SequencePattern
#--------------------------------- 组合行为模式挖掘 ---------------------------------#
use_behavior = []
del_behavior = []
# 创建实例
behavior = CombinePattern(data=data,
use_behavior=use_behavior,
del_behavior=del_behavior,
min_support=0.1,
min_confidence=0.5,
min_length=3,
max_length=7,
sep='@')
# 运行模型,返回pattern结果和使用的行为列表
combine, combine_use_behavior = behavior.run()
# 筛选lift符合要求的pattern
combine_result = combine[combine['lift'] > 6]
#--------------------------------- 连续行为模式挖掘 ---------------------------------#
use_behavior = []
del_behavior = []
# 创建实例
behavior = ContinuePattern(data=data,
use_behavior=use_behavior,
del_behavior=del_behavior,
min_support=0.1,
min_length=3,
max_length=6,
sep='@')
# 运行模型,返回pattern结果和使用的行为列表
continues, continue_use_behavior = behavior.run()
# 筛选lift符合要求的pattern
continues_result = continues[continues['lift'] > 6]
#--------------------------------- 序列行为模式挖掘 ---------------------------------#
use_behavior = []
del_behavior = []
# 创建实例
behavior = SequencePattern(data=data,
use_behavior=use_behavior,
del_behavior=del_behavior,
min_support=0.1,
min_length=3,
max_length=7,
sep='@')
# 运行模型,返回pattern结果和使用的行为列表
sequence, seq_use_behavior = behavior.run()
# 筛选lift符合要求的pattern
sequence_result = sequence[sequence['lift'] > 6]
依赖
BahaviorPattern依赖以下Python库:
- numpy
- pandas
- efficient_apriori
- tqdm
- prefixspan
完整的依赖列表可以在setup.py中找到。
贡献
如果你发现任何bugs,请提交Issue或Pull Request进行更正
作者
MyPackage由Chen Chen编写和维护。
致谢
感谢以下Python包的开发者:
- numpy
- pandas
- efficient_apriori
- tqdm
- prefixspan
这些开发者对该项目发展做出了重要贡献。
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