Metadata-Version: 2.1
Name: h-anomaly
Version: 0.0.6
Summary: A small example package Anomaly detection using hierarchical clustering, anomaly detector, classifiers and fast model rebuilding
Home-page: https://github.com/ai-se/h_anomaly
Author: RAISE_LAB
Author-email: smajumd3@ncsu.edu
License: UNKNOWN
Description: # h_anomaly
        A small example package Anomaly detection using hierarchical clustering, anomaly detector, classifiers and fast model rebuilding
        
        ## Required Packages to run - 
        
        ```
        1) pandas
        2) numpy
        3) pickle
        4) scipy
        5) freediscovery
        6) sklearn
        7) matplotlib
        
        ```
        
        ## Install the Package
        
        ```
        pip install -U h_anomaly
        
        ```
        
        ## To Use the Package:
        
        ### Import h_anomaly
        ```
        import h_anomaly - 'from h_anomaly import driver'
        
        ```
        ### Building Tree for the 1st time
        ```
        cluster,cluster_tree,max_depth = driver.cluster_driver(file_path,target_class,default_class)
        
        ```
        ### Loading New data for testing
        ```
        df,train_X,train_y = driver.get_data(file_path,target_class,default_class)
        
        ```
        ### Storing Test Data for future uses
        ```
        test_df,test_X,test_y = driver.get_data(file_path,target_class,default_class)
        cluster.set_test(test_X,test_y)
        
        ```
        ### Certify Model for performance monitoring:
        ```
        cluster.certify_model(cluster_tree,test_y)
        
        ```
        ### Check Cluster model for retraining
        ```
        cluster.check_model(cluster_tree,threshold)
        
        ```
        
        ## Available Functions:
        ```
        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)
        
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.6
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
