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Multi_algo_HP_dict = {
'IForest': {
'n_estimators': [25, 50, 100, 150, 200],
'max_features': [0.2, 0.4, 0.6, 0.8, 1.0]
},
'LOF': {
'n_neighbors': [10, 20, 30, 40, 50],
'metric': ['minkowski', 'manhattan', 'euclidean']
},
'PCA': {
'n_components': [0.25, 0.5, 0.75, None]
},
'HBOS': {
'n_bins': [5, 10, 20, 30, 40],
'tol': [0.1, 0.3, 0.5, 0.7]
},
'OCSVM': {
'kernel': ['linear', 'poly', 'rbf', 'sigmoid'],
'nu': [0.1, 0.3, 0.5, 0.7]
},
'MCD': {
'support_fraction': [0.2, 0.4, 0.6, 0.8, None]
},
'KNN': {
'n_neighbors': [10, 20, 30, 40, 50],
'method': ['largest', 'mean', 'median']
},
'KMeansAD': {
'n_clusters': [10, 20, 30, 40],
'window_size': [10, 20, 30, 40]
},
'COPOD': {
'HP': [None]
},
'CBLOF': {
'n_clusters': [4, 8, 16, 32],
'alpha': [0.6, 0.7, 0.8, 0.9]
},
'EIF': {
'n_trees': [25, 50, 100, 200]
},
'RobustPCA': {
'max_iter': [500, 1000, 1500]
},
'AutoEncoder': {
'hidden_neurons': [[64, 32], [32, 16], [128, 64]]
},
'CNN': {
'window_size': [50, 100, 150],
'num_channel': [[32, 32, 40], [16, 32, 64]]
},
'LSTMAD': {
'window_size': [50, 100, 150],
'lr': [0.0004, 0.0008]
},
'TranAD': {
'win_size': [5, 10, 50],
'lr': [1e-3, 1e-4]
},
'AnomalyTransformer': {
'win_size': [50, 100, 150],
'lr': [1e-3, 1e-4, 1e-5]
},
'OmniAnomaly': {
'win_size': [5, 50, 100],
'lr': [0.002, 0.0002]
},
'USAD': {
'win_size': [5, 50, 100],
'lr': [1e-3, 1e-4, 1e-5]
},
'Donut': {
'win_size': [60, 90, 120],
'lr': [1e-3, 1e-4, 1e-5]
},
'TimesNet': {
'win_size': [32, 96, 192],
'lr': [1e-3, 1e-4, 1e-5]
},
'FITS': {
'win_size': [100, 200],
'lr': [1e-3, 1e-4, 1e-5]
},
'OFA': {
'win_size': [50, 100, 150]
},
'Time_RCD': {
'win_size': 7000
},
'TSPulse': {
'win_size': [64, 128, 256],
'batch_size': [32, 64, 128],
'aggregation_length': [32, 64, 128],
'aggr_function': ['max', 'mean'],
'smoothing_length': [4, 8, 16]
}
}
Optimal_Multi_algo_HP_dict = {
'IForest': {'n_estimators': 25, 'max_features': 0.8},
'LOF': {'n_neighbors': 50, 'metric': 'euclidean'},
'PCA': {'n_components': 0.25},
'HBOS': {'n_bins': 30, 'tol': 0.5},
'OCSVM': {'kernel': 'rbf', 'nu': 0.1},
'MCD': {'support_fraction': 0.8},
'KNN': {'n_neighbors': 50, 'method': 'mean'},
'KMeansAD': {'n_clusters': 10, 'window_size': 40},
'KShapeAD': {'n_clusters': 20, 'window_size': 40},
'COPOD': {'n_jobs':1},
'CBLOF': {'n_clusters': 4, 'alpha': 0.6},
'EIF': {'n_trees': 50},
'RobustPCA': {'max_iter': 1000},
'AutoEncoder': {'hidden_neurons': [128, 64]},
'CNN': {'window_size': 50, 'num_channel': [32, 32, 40]},
'LSTMAD': {'window_size': 150, 'lr': 0.0008},
'TranAD': {'win_size': 10, 'lr': 0.001},
'AnomalyTransformer': {'win_size': 50, 'lr': 0.001},
'OmniAnomaly': {'win_size': 100, 'lr': 0.002},
'USAD': {'win_size': 100, 'lr': 0.001},
'Donut': {'win_size': 60, 'lr': 0.001},
'TimesNet': {'win_size': 96, 'lr': 0.0001},
'FITS': {'win_size': 100, 'lr': 0.001},
'OFA': {'win_size': 50},
'Time_RCD': {'win_size':5000, 'batch_size': 1},
'DADA': {'win_size': 100, 'batch_size': 64},
'TSPulse': {'win_size': 96 , 'batch_size': 64, 'aggregation_length': 64, 'aggr_function': 'max', 'smoothing_length': 8}
}
Uni_algo_HP_dict = {
'Sub_IForest': {
'periodicity': [1, 2, 3],
'n_estimators': [25, 50, 100, 150, 200]
},
'IForest': {
'n_estimators': [25, 50, 100, 150, 200]
},
'Sub_LOF': {
'periodicity': [1, 2, 3],
'n_neighbors': [10, 20, 30, 40, 50]
},
'LOF': {
'n_neighbors': [10, 20, 30, 40, 50]
},
'POLY': {
'periodicity': [1, 2, 3],
'power': [1, 2, 3, 4]
},
'MatrixProfile': {
'periodicity': [1, 2, 3]
},
'NORMA': {
'periodicity': [1, 2, 3],
'clustering': ['hierarchical', 'kshape']
},
'SAND': {
'periodicity': [1, 2, 3]
},
'Series2Graph': {
'periodicity': [1, 2, 3]
},
'Sub_PCA': {
'periodicity': [1, 2, 3],
'n_components': [0.25, 0.5, 0.75, None]
},
'Sub_HBOS': {
'periodicity': [1, 2, 3],
'n_bins': [5, 10, 20, 30, 40]
},
'Sub_OCSVM': {
'periodicity': [1, 2, 3],
'kernel': ['linear', 'poly', 'rbf', 'sigmoid']
},
'Sub_MCD': {
'periodicity': [1, 2, 3],
'support_fraction': [0.2, 0.4, 0.6, 0.8, None]
},
'Sub_KNN': {
'periodicity': [1, 2, 3],
'n_neighbors': [10, 20, 30, 40, 50],
},
'KMeansAD_U': {
'periodicity': [1, 2, 3],
'n_clusters': [10, 20, 30, 40],
},
'KShapeAD': {
'periodicity': [1, 2, 3]
},
'AutoEncoder': {
'window_size': [50, 100, 150],
'hidden_neurons': [[64, 32], [32, 16], [128, 64]]
},
'CNN': {
'window_size': [50, 100, 150],
'num_channel': [[32, 32, 40], [16, 32, 64]]
},
'LSTMAD': {
'window_size': [50, 100, 150],
'lr': [0.0004, 0.0008]
},
'TranAD': {
'win_size': [5, 10, 50],
'lr': [1e-3, 1e-4]
},
'AnomalyTransformer': {
'win_size': [50, 100, 150],
'lr': [1e-3, 1e-4, 1e-5]
},
'OmniAnomaly': {
'win_size': [5, 50, 100],
'lr': [0.002, 0.0002]
},
'USAD': {
'win_size': [5, 50, 100],
'lr': [1e-3, 1e-4, 1e-5]
},
'Donut': {
'win_size': [60, 90, 120],
'lr': [1e-3, 1e-4, 1e-5]
},
'TimesNet': {
'win_size': [32, 96, 192],
'lr': [1e-3, 1e-4, 1e-5]
},
'FITS': {
'win_size': [100, 200],
'lr': [1e-3, 1e-4, 1e-5]
},
'OFA': {
'win_size': [50, 100, 150]
},
# 'Time_RCD': {
# 'win_size': [1000, 2000, 3000, 4000, 5000, 6000, 8000, 10000],
# 'batch_size': [32, 64, 128]
# }
}
Optimal_Uni_algo_HP_dict = {
'Sub_IForest': {'periodicity': 1, 'n_estimators': 150},
'IForest': {'n_estimators': 200},
'Sub_LOF': {'periodicity': 2, 'n_neighbors': 30},
'LOF': {'n_neighbors': 50},
'POLY': {'periodicity': 1, 'power': 4},
'MatrixProfile': {'periodicity': 1},
'NORMA': {'periodicity': 1, 'clustering': 'kshape'},
'SAND': {'periodicity': 1},
'Series2Graph': {'periodicity': 1},
'SR': {'periodicity': 1},
'Sub_PCA': {'periodicity': 1, 'n_components': None},
'Sub_HBOS': {'periodicity': 1, 'n_bins': 10},
'Sub_OCSVM': {'periodicity': 2, 'kernel': 'rbf'},
'Sub_MCD': {'periodicity': 3, 'support_fraction': None},
'Sub_KNN': {'periodicity': 2, 'n_neighbors': 50},
'KMeansAD_U': {'periodicity': 2, 'n_clusters': 10},
'KShapeAD': {'periodicity': 1},
'FFT': {},
'Left_STAMPi': {},
'AutoEncoder': {'window_size': 100, 'hidden_neurons': [128, 64]},
'CNN': {'window_size': 50, 'num_channel': [32, 32, 40]},
'LSTMAD': {'window_size': 100, 'lr': 0.0008},
'TranAD': {'win_size': 10, 'lr': 0.0001},
'AnomalyTransformer': {'win_size': 50, 'lr': 0.001},
'OmniAnomaly': {'win_size': 5, 'lr': 0.002},
'USAD': {'win_size': 100, 'lr': 0.001},
'Donut': {'win_size': 60, 'lr': 0.0001},
'TimesNet': {'win_size': 32, 'lr': 0.0001},
'FITS': {'win_size': 100, 'lr': 0.0001},
'OFA': {'win_size': 50},
'Lag_Llama': {'win_size': 96},
'Chronos': {'win_size': 100},
'TimesFM': {'win_size': 96},
'MOMENT_ZS': {'win_size': 64},
'MOMENT_FT': {'win_size': 64},
'M2N2': {},
'DADA': {'win_size': 100},
'Time_MOE': {'win_size':96},
'Time_RCD': {'win_size':5000, 'batch_size': 64},
'Time_RCD_Reconstruction': {'win_size':5000, 'batch_size': 128},
'Time_RCD_Reconstruction_Anomaly_Head': {'win_size':5000, 'batch_size': 128},
'Time_RCD_Reconstruction_Random_Mask_Anomaly_Head': {'win_size':5000, 'batch_size': 128},
'TSPulse': {'win_size':96, 'batch_size': 64, 'aggregation_length': 64, 'aggr_function': 'max', 'smoothing_length': 8}
} |