From 6809fd18dc4f79d59133b4f994782e65498473e5 Mon Sep 17 00:00:00 2001 From: lhenry15 Date: Sun, 30 May 2021 11:38:45 -0500 Subject: [PATCH] remove old sklearn_interface --- examples/sklearn_interface/ABOD_skitest.py | 14 -------- examples/sklearn_interface/system_KNN.py | 51 ------------------------------ 2 files changed, 65 deletions(-) delete mode 100644 examples/sklearn_interface/ABOD_skitest.py delete mode 100644 examples/sklearn_interface/system_KNN.py diff --git a/examples/sklearn_interface/ABOD_skitest.py b/examples/sklearn_interface/ABOD_skitest.py deleted file mode 100644 index fe33576..0000000 --- a/examples/sklearn_interface/ABOD_skitest.py +++ /dev/null @@ -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) diff --git a/examples/sklearn_interface/system_KNN.py b/examples/sklearn_interface/system_KNN.py deleted file mode 100644 index 46a3b98..0000000 --- a/examples/sklearn_interface/system_KNN.py +++ /dev/null @@ -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) -