Training Dataset and Trained Model for Supervised Anomaly Diagnosis

train_data.hdf: This is the training dataset used to train the Random Forest Classifier model. train_label.hdf: These are the anomaly labels corresponding to each application run in the training dataset. parameters.json: This JSON file helps to construct all possible features (feature extraction version). normal_graph_search_dictionary.json: This file contains a dictionary that helps find the healthy node based on the specific application type users select. search_dataframe.csv: This CSV file contains the raw data of the healthy nodes provided in the normal_graph_search_dictionary.json file. sample_data_2.csv: This is a sample dataset used for the sample data option. node_anoms_sample_2.csv: This file contains the prediction results for the dataset in the sample_data_2.csv. test_model.sav: This is a trained Random Forest Classifier model that was trained offline.