remove old sklearn_interface

examples/sklearn_interface/ABOD_skitest.py

@@ -1,14 +0,0 @@
-import numpy as np
-from tods.tods_skinterface.primitiveSKI.detection_algorithm.ABOD_skinterface import ABODSKI
-
-X_train = np.array([[3., 4., 8., 16, 18, 13., 22., 36., 59., 128, 62, 67, 78, 100]])
-X_test = np.array([[3., 4., 8.6, 13.4, 22.5, 17, 19.2, 36.1, 127, -23, 59.2]])
-
-transformer = ABODSKI()
-transformer.fit(X_train)
-prediction_labels = transformer.predict(X_test)
-prediction_score = transformer.predict_score(X_test)
-
-print("Primitive: ", transformer.primitive)
-print("Prediction Labels\n", prediction_labels)
-print("Prediction Score\n", prediction_score)

examples/sklearn_interface/system_KNN.py

@@ -1,51 +0,0 @@
-import numpy as np
-
-from tods.sk_interface.feature_analysis.StatisticalMaximum_skinterface import StatisticalMaximumSKI
-from tods.sk_interface.detection_algorithm.KNN_skinterface import KNNSKI
-from tods.sk_interface.data_ensemble.Ensemble_skinterface import EnsembleSKI
-from tods.sk_interface.utils.data import generate_3D_data, load_sys_data, generate_sys_feature
-
-# Generate 3D data (n, T, d), n: system number, T: time, d: dimension
-
-# n_sys = 5
-# X_train, y_train, X_test, y_test = generate_3D_data(n_sys=n_sys,
-#                                                     n_train=1000,
-#                                                     n_test=1000,
-#                                                     n_features=3,
-#                                                     contamination=0.1)
-
-X_train, y_train, sys_info_train = load_sys_data('../../datasets/anomaly/system_wise/sample/train.csv',
-                                 '../../datasets/anomaly/system_wise/sample/systems')
-X_test, y_test, sys_info_test = load_sys_data('../../datasets/anomaly/system_wise/sample/train.csv',
-                                 '../../datasets/anomaly/system_wise/sample/systems')
-n_sys = sys_info_train['sys_num']
-
-# feature analysis algorithms
-stmax = StatisticalMaximumSKI(system_num=n_sys)
-
-# OD algorithms
-detection_module = KNNSKI(contamination=0.1, system_num=n_sys)
-
-# ensemble model
-ensemble_module = EnsembleSKI()
-
-# Fit the feature analysis algorithms
-X_train = stmax.produce(X_train)
-X_test = stmax.produce(X_test)
-
-# Fit the detector
-detection_module.fit(X_train)
-sys_ts_score = detection_module.predict_score(X_test) # shape (n, T, 1)
-
-# generate sys_feature based on the time-series anomaly score
-sys_feature = generate_sys_feature(sys_ts_score) # shape (T, n)
-
-print(sys_feature.shape)
-print(sys_feature.ndim)
-
-# Ensemble the time series outlier socre for each system
-ensemble_module.fit(sys_feature)
-sys_score = ensemble_module.predict(sys_feature)
-
-print(sys_score)
-