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Commit efb66678 authored by Desiree Wyrzykala's avatar Desiree Wyrzykala
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Merge branch 'dev_Ahmad' into 'main' 

evaluation_PCA3 and PCA6

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docs/Dataset info.txt → docs/data/Dataset info.txt
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docs/Datensatz.md → docs/data/Datensatz.md
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docs/evaluation/run_Sub_PCA3.csv 0 → 100644
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params,file_name,duration,group,point anomaly,seq anomaly,AUC-PR,AUC-ROC,VUS-PR,VUS-ROC,Standard-F1,PA-F1,Event-based-F1,R-based-F1,Affiliation-F,Recall,Precision
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"{'periodicity': 2, 'n_components': 0.75}",233_MGAB_id_9_Synthetic_tr_25000_1st_41300.csv,24.39292287826538,MGAB,False,True,0.03962073592960159,0.46820856713426856,0.009074853694442138,0.612897608048629,0.11718408213093782,0.17937219730941703,0.1666666666666664,0.116805038648726,0.6666609381931233,0.065,0.34210526315789475
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docs/evaluation/run_Sub_PCA6.csv 0 → 100644
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params,file_name,duration,group,point anomaly,seq anomaly,AUC-PR,AUC-ROC,VUS-PR,VUS-ROC,Standard-F1,PA-F1,Event-based-F1,R-based-F1,Affiliation-F,Recall,Precision
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"{'periodicity': 1, 'n_components': 0.5}",526_UCR_id_224_Sensor_tr_2827_1st_4711.csv,5.808629274368286,UCR,False,False,0.026514811408072672,0.4089779533579712,0.037787553463207994,0.5293123393703363,0.12458580267887291,0.6901408450704225,0.1783439490445858,0.32854179500048597,0.8165896799933247,0.0,0.0
"{'periodicity': 1, 'n_components': 0.25}",545_SMAP_id_15_Sensor_tr_1173_1st_2750.csv,2.3520584106445312,SMAP,False,False,0.35907013709938385,0.9914844007400461,0.7387481553967095,0.9971610914948849,0.6891846722355942,0.7555555555555555,0.6853146853146848,0.6845637583892618,0.9950712506535755,0.0,0.0
"{'periodicity': 2, 'n_components': None}",532_SMAP_id_2_Sensor_tr_2075_1st_5550.csv,3.4671173095703125,SMAP,False,False,0.21492635811559677,0.9294130968134239,0.22548894212153012,0.9305320216408012,0.44285338575636435,0.6971201588877854,0.45641838351822467,0.4520283322601417,0.9313520919999877,0.0,0.0
src/models/ahmad/PCA.ipynb
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− 443
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src/run_model_wrapper.py
+ 1
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......@@ -50,7 +50,7 @@ Semisupervise_AD_Pool = ['SAND','OCSVM','AutoEncoder', 'CNN', 'LSTMAD', 'USAD',
def hyperparameter_optimization(run_model, data,label,train_data, hyperparams,slidingWindow, model_type = 'unsupervised'):
#ignore warnings for hyperparameter tuning
warnings.simplefilter("ignore", category=UndefinedMetricWarning)
warnings.simplefilter("ignore", category=np.RankWarning)
#warnings.simplefilter("ignore", category=np.RankWarning)
best_params = None
best_score = float("-inf")
......
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