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        "author": "RAISE_LAB",
        "author_email": "smajumd3@ncsu.edu",
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        "description": "# h_anomaly\nA small example package Anomaly detection using hierarchical clustering, anomaly detector, classifiers and fast model rebuilding\n\n## Required Packages to run - \n\n```\n1) pandas\n2) numpy\n3) pickle\n4) scipy\n5) freediscovery\n6) sklearn\n7) matplotlib\n\n```\n\n## Install the Package\n\n```\npip install -U h_anomaly\n\n```\n\n## To Use the Package:\n\n### Import h_anomaly\n```\nimport h_anomaly - 'from h_anomaly import driver'\n\n```\n### Building Tree for the 1st time\n```\ncluster,cluster_tree,max_depth = driver.cluster_driver(file_path,target_class,default_class)\n\n```\n### Loading New data for testing\n```\ndf,train_X,train_y = driver.get_data(file_path,target_class,default_class)\n\n```\n### Storing Test Data for future uses\n```\ntest_df,test_X,test_y = driver.get_data(file_path,target_class,default_class)\ncluster.set_test(test_X,test_y)\n\n```\n### Certify Model for performance monitoring:\n```\ncluster.certify_model(cluster_tree,test_y)\n\n```\n### Check Cluster model for retraining\n```\ncluster.check_model(cluster_tree,threshold)\n\n```\n\n## Available Functions:\n```\n1) fit - Fit the Data into the Birch algorithm to create the clusters\n  def fit(self,data,y)\n2) set_test - Store the test data for future uses\n  set_test(self,data,y)\n3) get_cluster_tree - For each cluster at every level creates the bcluster objects\n  get_cluster_tree(self)\n4) model_adder - Classification model added to each cluster by this function (Change this function to add different model)\n  def model_adder(self,cluster_tree)\n5) update_model - Classification model is updated with new data\n   update_model(self,cluster_tree,cluster_id)\n6) outlier_model_adder - Outlier detection model is added to each cluster (Change this function to add different model)\n  outlier_model_adder(self,cluster_tree)\n7) certify_model - Scores are calculated in this function\n  certify_model(self,cluster_tree,test_y)\n\n```\n\n\n",
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