基于层次聚类、异常检测器、分类器和快速模型重建的小样本包异常检测
h-anomal的Python项目详细描述
h\U异常
一个使用分层聚类、异常检测器、分类器和快速模型重建的小示例包异常检测
运行所需的包-
1) pandas
2) numpy
3) pickle
4) scipy
5) freediscovery
6) sklearn
7) matplotlib
安装程序包
^{pr2}$要使用软件包：
输入h\U异常
import h_anomaly - 'from h_anomaly import driver'
首次建树
cluster,cluster_tree,max_depth = driver.cluster_driver(file_path,target_class,default_class)
加载新数据进行测试
df,train_X,train_y = driver.get_data(file_path,target_class,default_class)
存储测试数据以备将来使用
test_df,test_X,test_y = driver.get_data(file_path,target_class,default_class)
cluster.set_test(test_X,test_y)
性能监控认证模型：
cluster.certify_model(cluster_tree,test_y)
检查重新培训的集群模型
cluster.check_model(cluster_tree,threshold)
可用功能：
1) fit - Fit the Data into the Birch algorithm to create the clusters
def fit(self,data,y)
2) set_test - Store the test data for future uses
set_test(self,data,y)
3) get_cluster_tree - For each cluster at every level creates the bcluster objects
get_cluster_tree(self)
4) model_adder - Classification model added to each cluster by this function (Change this function to add different model)
def model_adder(self,cluster_tree)
5) update_model - Classification model is updated with new data
update_model(self,cluster_tree,cluster_id)
6) outlier_model_adder - Outlier detection model is added to each cluster (Change this function to add different model)
outlier_model_adder(self,cluster_tree)
7) certify_model - Scores are calculated in this function
certify_model(self,cluster_tree,test_y)
- 项目
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