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diff --git a/docs/TO DO LISTE.md b/docs/TO DO LISTE.md
index 8cff74843ee61095fab18ce5020407357efcfa39..e5e7090a9e763e1dd5ae0120adf3a59d65fb7ed5 100644
--- a/docs/TO DO LISTE.md
+++ b/docs/TO DO LISTE.md
@@ -46,4 +46,5 @@
+ Dichtebasiert vs. Distanzbasiert
+ Statistisch vs. Neuronal
+ Ausarbeiten der Algorithmen und Vorbereiten für die Hausarbeit
-+ Histplot / Barplot erstellen mit Ergebnissen von VUS-PR bei TAO von den Autoren gegenüber unseren Ergebnissen für die Modelle, bei denen die Werte stark abweichen
\ No newline at end of file
++ Histplot / Barplot erstellen mit Ergebnissen von VUS-PR bei TAO von den Autoren gegenüber unseren Ergebnissen für die Modelle, bei denen die Werte stark abweichen
++ Heatmaps für Anomalie-Typen erstellen
\ No newline at end of file
diff --git a/docs/evaluation/Vergleich der Ergebnisse.md b/docs/evaluation/Vergleich der Ergebnisse.md
index 1a1a368cee90341fd58f45e100d57f1764bd9811..8f3134873cc00eddaf97125f50a3ba08308f82c2 100644
--- a/docs/evaluation/Vergleich der Ergebnisse.md
+++ b/docs/evaluation/Vergleich der Ergebnisse.md
@@ -3,8 +3,8 @@
| Laufzeiten | POLY | iForest | Sub-iForest | PCA | USAD | CNN | OmAn | LSTM | kShape | kMeans | OCSVM | LOF | Sub-LOF | KNN | KNNV2 |
| ------------------------------ | ---- | ------- | ----------- | ---- | ---- | ---- | ---- | ---- | ------ | ------ | ----- | ---- | ------- | ---- | ----- |
| **Bei Autoren (s)** | 0.09 | 0.14 | | 2 | 2 | 3 | 4 | 8 | 8.5 | 20 | 30 | 70 | 80 | 90 | |
-| **Insgesamt (h)** | 3.42 | 3.44 | | 4.26 | 3.19 | 2.93 | 3.42 | 3.66 | 3.54 | 5.62 | | 2.04 | 5.14 | 6.68 | |
-| **Durchschnitt pro Datei (s)** | 31.0 | 31.2 | | 38.7 | 28.9 | 26.6 | 31 | 33.2 | 32.1 | 50.9 | | 18.5 | 46.6 | 60.5 | |
+| **Insgesamt (h)** | 3.42 | 3.44 | | 4.26 | 3.19 | 2.93 | 3.42 | 3.66 | 3.54 | 5.62 | 11.5 | 2.04 | 5.14 | 6.68 | |
+| **Durchschnitt pro Datei (s)** | 31.0 | 31.2 | | 38.7 | 28.9 | 26.6 | 31 | 33.2 | 32.1 | 50.9 | 104.2 | 18.5 | 46.6 | 60.5 | |
# Datensätze
+ Gesamtanzahl Zeitserien: 397
@@ -13,138 +13,138 @@
+ Anzahl an Zeitserien mit nur Sequenzanomalien: 158
## Gesamtbewertung nach AUC-PR
-| Datensatz | POLY | iForest | Sub-iForest | PCA | USAD | CNN | OmAn | LSTM | kShape | kMeans | OCSVM | LOF | Sub-LOF | KNN | KNNV2 | Datensatz |
-| ------------- | --------- | --------- | ----------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | ----- | --------- | --------- | --------- | ----- | ------------- |
-| UCR | 0.156850 | 0.059389 | | 0.138533 | 0.088392 | 0.069477 | 0.085236 | 0.028041 | 0.465144 | 0.487206 | | 0.048985 | 0.495182 | 0.283830 | | UCR |
-| SMD | 0.639394 | 0.369237 | | 0.495851 | 0.665737 | 0.565370 | 0.531140 | 0.539890 | 0.131250 | 0.260908 | | 0.182353 | 0.486307 | 0.162970 | | SMD |
-| YAHOO | 0.075395 | 0.092730 | | 0.117107 | 0.117266 | 0.561562 | 0.148188 | 0.443957 | 0.435473 | 0.699444 | | 0.409909 | 0.150503 | 0.144445 | | YAHOO |
-| Exathlon | 0.844513 | 0.962806 | | 0.958102 | 0.862358 | 0.654310 | 0.874519 | 0.733875 | 0.420990 | 0.617690 | | 0.245106 | 0.257618 | 0.563481 | | Exathlon |
-| NAB | 0.526439 | 0.314587 | | 0.486528 | 0.300305 | 0.223347 | 0.332227 | 0.153306 | 0.396635 | 0.401505 | | 0.144844 | 0.495381 | 0.311100 | | NAB |
-| OPPORTUNITY | 0.420658 | 0.733153 | | 0.923192 | 0.651235 | 0.619160 | 0.649867 | 0.545535 | 0.366866 | 0.365381 | | 0.153396 | 0.158653 | 0.298396 | | OPPORTUNITY |
-| WSD | 0.427387 | 0.040993 | | 0.216542 | 0.240276 | 0.303128 | 0.251057 | 0.209492 | 0.082639 | 0.146372 | | 0.141918 | 0.539254 | 0.044597 | | WSD |
-| SVDB | 0.506002 | 0.311936 | | 0.558896 | 0.459209 | 0.197103 | 0.476835 | 0.129879 | 0.798760 | 0.809036 | | 0.044940 | 0.356140 | 0.538270 | | SVDB |
-| SMAP | 0.604826 | 0.299302 | | 0.484416 | 0.273159 | 0.329931 | 0.385082 | 0.278827 | 0.601232 | 0.648913 | | 0.180797 | 0.510341 | 0.453816 | | SMAP |
-| IOPS | 0.332866 | 0.231569 | | 0.372849 | 0.403523 | 0.265889 | 0.368163 | 0.320195 | 0.068365 | 0.068715 | | 0.116787 | 0.263492 | 0.070598 | | IOPS |
-| TODS | 0.239024 | 0.076521 | | 0.124321 | 0.163915 | 0.303786 | 0.172369 | 0.205671 | 0.462057 | 0.448703 | | 0.100820 | 0.166507 | 0.410649 | | TODS |
-| Stock | 0.146590 | 0.143940 | | 0.148428 | 0.133570 | 0.929344 | 0.133646 | 0.519232 | 0.095333 | 0.114476 | | 0.112061 | 0.115033 | 0.096103 | | Stock |
-| MGAB | 0.008996 | 0.004336 | | 0.027123 | 0.032097 | 0.003855 | 0.015268 | 0.045860 | 0.084411 | 0.085299 | | 0.002209 | 0.567335 | 0.325650 | | MGAB |
-| MSL | 0.629533 | 0.386085 | | 0.493320 | 0.267256 | 0.307033 | 0.394283 | 0.237442 | 0.409835 | 0.371438 | | 0.151228 | 0.442670 | 0.313525 | | MSL |
-| NEK | 0.709458 | 0.630339 | | 0.891733 | 0.816021 | 0.685573 | 0.836145 | 0.667421 | 0.379153 | 0.346973 | | 0.363561 | 0.554506 | 0.119174 | | NEK |
-| LTDB | 0.455324 | 0.521341 | | 0.489411 | 0.461335 | 0.354462 | 0.479711 | 0.276735 | 0.743642 | 0.765819 | | 0.203241 | 0.333160 | 0.589828 | | LTDB |
-| MITDB | 0.366934 | 0.169557 | | 0.377853 | 0.201810 | 0.160153 | 0.206999 | 0.113066 | 0.594424 | 0.583808 | | 0.051628 | 0.385195 | 0.500834 | | MITDB |
-| TAO | 0.140526 | 0.125817 | | 0.149144 | 0.339781 | 0.994857 | 0.344060 | 0.993397 | 0.103834 | 0.129147 | | 0.167995 | 0.139803 | 0.108733 | | TAO |
-| SED | 0.031421 | 0.691484 | | 0.020994 | 0.026231 | 0.029325 | 0.031663 | 0.030497 | 0.722020 | 0.710575 | | 0.082198 | 0.109660 | 0.707453 | | SED |
-| Daphnet | 0.536827 | 0.496579 | | 0.468647 | 0.192057 | 0.381499 | 0.192069 | 0.132234 | 0.033420 | 0.036445 | | 0.129509 | 0.252282 | 0.033893 | | Daphnet |
-| SWaT | 0.123133 | 0.702432 | | 0.756700 | 0.757484 | 0.738718 | 0.757687 | 0.737695 | 0.458896 | 0.111152 | | 0.120683 | 0.113563 | 0.110376 | | SWaT |
-| Power | 0.203760 | 0.073981 | | 0.078866 | 0.064665 | 0.072400 | 0.064665 | 0.064788 | 0.496250 | 0.507629 | | 0.086245 | 0.175035 | 0.160167 | | Power |
-| CATSv2 | 0.476434 | 0.139416 | | 0.474705 | 0.481625 | 0.411894 | 0.437868 | 0.358071 | 0.452377 | 0.455611 | | 0.071542 | 0.511976 | 0.482166 | | CATSv2 |
-| **Insgesamt** | **0.394** | **0.304** | | **0.407** | **0.352** | **0.366** | **0.354** | **0.313** | **0.383** | **0.444** | | **0.156** | **0.374** | **0.292** | | **Insgesamt** |
+| Datensatz | POLY | iForest | Sub-iForest | PCA | USAD | CNN | OmAn | LSTM | kShape | kMeans | OCSVM | LOF | Sub-LOF | KNN | KNNV2 | Datensatz |
+| ------------- | --------- | --------- | ----------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | ----- | ------------- |
+| UCR | 0.156850 | 0.059389 | | 0.138533 | 0.088392 | 0.069477 | 0.085236 | 0.028041 | 0.465144 | 0.487206 | 0.326989 | 0.048985 | 0.495182 | 0.283830 | | UCR |
+| SMD | 0.639394 | 0.369237 | | 0.495851 | 0.665737 | 0.565370 | 0.531140 | 0.539890 | 0.131250 | 0.260908 | 0.122192 | 0.182353 | 0.486307 | 0.162970 | | SMD |
+| YAHOO | 0.075395 | 0.092730 | | 0.117107 | 0.117266 | 0.561562 | 0.148188 | 0.443957 | 0.435473 | 0.699444 | 0.083868 | 0.409909 | 0.150503 | 0.144445 | | YAHOO |
+| Exathlon | 0.844513 | 0.962806 | | 0.958102 | 0.862358 | 0.654310 | 0.874519 | 0.733875 | 0.420990 | 0.617690 | 0.556786 | 0.245106 | 0.257618 | 0.563481 | | Exathlon |
+| NAB | 0.526439 | 0.314587 | | 0.486528 | 0.300305 | 0.223347 | 0.332227 | 0.153306 | 0.396635 | 0.401505 | 0.343854 | 0.144844 | 0.495381 | 0.311100 | | NAB |
+| OPPORTUNITY | 0.420658 | 0.733153 | | 0.923192 | 0.651235 | 0.619160 | 0.649867 | 0.545535 | 0.366866 | 0.365381 | 0.147631 | 0.153396 | 0.158653 | 0.298396 | | OPPORTUNITY |
+| WSD | 0.427387 | 0.040993 | | 0.216542 | 0.240276 | 0.303128 | 0.251057 | 0.209492 | 0.082639 | 0.146372 | 0.036128 | 0.141918 | 0.539254 | 0.044597 | | WSD |
+| SVDB | 0.506002 | 0.311936 | | 0.558896 | 0.459209 | 0.197103 | 0.476835 | 0.129879 | 0.798760 | 0.809036 | 0.547080 | 0.044940 | 0.356140 | 0.538270 | | SVDB |
+| SMAP | 0.604826 | 0.299302 | | 0.484416 | 0.273159 | 0.329931 | 0.385082 | 0.278827 | 0.601232 | 0.648913 | 0.487651 | 0.180797 | 0.510341 | 0.453816 | | SMAP |
+| IOPS | 0.332866 | 0.231569 | | 0.372849 | 0.403523 | 0.265889 | 0.368163 | 0.320195 | 0.068365 | 0.068715 | 0.051207 | 0.116787 | 0.263492 | 0.070598 | | IOPS |
+| TODS | 0.239024 | 0.076521 | | 0.124321 | 0.163915 | 0.303786 | 0.172369 | 0.205671 | 0.462057 | 0.448703 | 0.404043 | 0.100820 | 0.166507 | 0.410649 | | TODS |
+| Stock | 0.146590 | 0.143940 | | 0.148428 | 0.133570 | 0.929344 | 0.133646 | 0.519232 | 0.095333 | 0.114476 | 0.097345 | 0.112061 | 0.115033 | 0.096103 | | Stock |
+| MGAB | 0.008996 | 0.004336 | | 0.027123 | 0.032097 | 0.003855 | 0.015268 | 0.045860 | 0.084411 | 0.085299 | 0.032883 | 0.002209 | 0.567335 | 0.325650 | | MGAB |
+| MSL | 0.629533 | 0.386085 | | 0.493320 | 0.267256 | 0.307033 | 0.394283 | 0.237442 | 0.409835 | 0.371438 | 0.239125 | 0.151228 | 0.442670 | 0.313525 | | MSL |
+| NEK | 0.709458 | 0.630339 | | 0.891733 | 0.816021 | 0.685573 | 0.836145 | 0.667421 | 0.379153 | 0.346973 | 0.360131 | 0.363561 | 0.554506 | 0.119174 | | NEK |
+| LTDB | 0.455324 | 0.521341 | | 0.489411 | 0.461335 | 0.354462 | 0.479711 | 0.276735 | 0.743642 | 0.765819 | 0.634659 | 0.203241 | 0.333160 | 0.589828 | | LTDB |
+| MITDB | 0.366934 | 0.169557 | | 0.377853 | 0.201810 | 0.160153 | 0.206999 | 0.113066 | 0.594424 | 0.583808 | 0.355826 | 0.051628 | 0.385195 | 0.500834 | | MITDB |
+| TAO | 0.140526 | 0.125817 | | 0.149144 | 0.339781 | 0.994857 | 0.344060 | 0.993397 | 0.103834 | 0.129147 | 0.117776 | 0.167995 | 0.139803 | 0.108733 | | TAO |
+| SED | 0.031421 | 0.691484 | | 0.020994 | 0.026231 | 0.029325 | 0.031663 | 0.030497 | 0.722020 | 0.710575 | 0.187850 | 0.082198 | 0.109660 | 0.707453 | | SED |
+| Daphnet | 0.536827 | 0.496579 | | 0.468647 | 0.192057 | 0.381499 | 0.192069 | 0.132234 | 0.033420 | 0.036445 | 0.066512 | 0.129509 | 0.252282 | 0.033893 | | Daphnet |
+| SWaT | 0.123133 | 0.702432 | | 0.756700 | 0.757484 | 0.738718 | 0.757687 | 0.737695 | 0.458896 | 0.111152 | 0.159515 | 0.120683 | 0.113563 | 0.110376 | | SWaT |
+| Power | 0.203760 | 0.073981 | | 0.078866 | 0.064665 | 0.072400 | 0.064665 | 0.064788 | 0.496250 | 0.507629 | 0.215936 | 0.086245 | 0.175035 | 0.160167 | | Power |
+| CATSv2 | 0.476434 | 0.139416 | | 0.474705 | 0.481625 | 0.411894 | 0.437868 | 0.358071 | 0.452377 | 0.455611 | 0.369080 | 0.071542 | 0.511976 | 0.482166 | | CATSv2 |
+| **Insgesamt** | **0.394** | **0.304** | | **0.407** | **0.352** | **0.366** | **0.354** | **0.313** | **0.383** | **0.444** | **0.275** | **0.156** | **0.374** | **0.292** | | **Insgesamt** |
## Gesamtbewertung nach VUS-PR
-| Datensatz | POLY | iForest | Sub-iForest | PCA | USAD | CNN | OmAn | LSTM | kShape | kMeans | OCSVM | LOF | Sub-LOF | KNN | KNNV2 | Datensatz |
-| ------------- | --------- | --------- | ----------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | ----- | --------- | --------- | --------- | ----- | ------------- |
-| UCR | 0.198780 | 0.058773 | | 0.152678 | 0.084437 | 0.065443 | 0.089552 | 0.023293 | 0.460106 | 0.486822 | | 0.051489 | 0.514326 | 0.329748 | | UCR |
-| SMD | 0.663191 | 0.342406 | | 0.467257 | 0.629404 | 0.545045 | 0.478742 | 0.483944 | 0.122593 | 0.254537 | | 0.134985 | 0.544696 | 0.163803 | | SMD |
-| YAHOO | 0.307653 | 0.135664 | | 0.163729 | 0.192366 | 0.512096 | 0.219787 | 0.417416 | 0.491670 | 0.781824 | | 0.381508 | 0.451001 | 0.502031 | | YAHOO |
-| Exathlon | 0.847945 | 0.964836 | | 0.959173 | 0.864301 | 0.657757 | 0.876372 | 0.735253 | 0.420845 | 0.618051 | | 0.240338 | 0.262478 | 0.562449 | | Exathlon |
-| NAB | 0.584112 | 0.342137 | | 0.511398 | 0.308303 | 0.248091 | 0.342007 | 0.173732 | 0.415046 | 0.427477 | | 0.165966 | 0.532450 | 0.341432 | | NAB |
-| OPPORTUNITY | 0.426326 | 0.736264 | | 0.924516 | 0.651269 | 0.632799 | 0.649931 | 0.563772 | 0.347881 | 0.351828 | | 0.159200 | 0.149548 | 0.295398 | | OPPORTUNITY |
-| WSD | 0.531110 | 0.027487 | | 0.155366 | 0.155950 | 0.224922 | 0.157612 | 0.130270 | 0.112541 | 0.173060 | | 0.102346 | 0.551041 | 0.083204 | | WSD |
-| SVDB | 0.445531 | 0.307322 | | 0.523984 | 0.435722 | 0.199785 | 0.445289 | 0.127489 | 0.810962 | 0.811385 | | 0.050189 | 0.383000 | 0.591484 | | SVDB |
-| SMAP | 0.727079 | 0.327727 | | 0.566842 | 0.302212 | 0.336818 | 0.417852 | 0.273214 | 0.609905 | 0.657738 | | 0.172499 | 0.597854 | 0.558287 | | SMAP |
-| IOPS | 0.365055 | 0.134518 | | 0.260372 | 0.192815 | 0.205826 | 0.192837 | 0.213044 | 0.064609 | 0.107988 | | 0.123657 | 0.380817 | 0.116527 | | IOPS |
-| TODS | 0.586493 | 0.487386 | | 0.552877 | 0.529843 | 0.575941 | 0.538194 | 0.522228 | 0.747606 | 0.739257 | | 0.485079 | 0.691125 | 0.780280 | | TODS |
-| Stock | 0.805375 | 0.735886 | | 0.825382 | 0.771612 | 0.939925 | 0.771658 | 0.817960 | 0.717890 | 0.738198 | | 0.710487 | 0.747567 | 0.691512 | | Stock |
-| MGAB | 0.008269 | 0.004379 | | 0.005551 | 0.005403 | 0.006988 | 0.005586 | 0.025419 | 0.069759 | 0.036645 | | 0.004639 | 0.634748 | 0.238613 | | MGAB |
-| MSL | 0.734244 | 0.448709 | | 0.523310 | 0.295208 | 0.346350 | 0.428048 | 0.305128 | 0.459063 | 0.441543 | | 0.186700 | 0.544711 | 0.368746 | | MSL |
-| NEK | 0.764836 | 0.649735 | | 0.929171 | 0.827283 | 0.714845 | 0.858349 | 0.689697 | 0.357818 | 0.317927 | | 0.395130 | 0.687020 | 0.132908 | | NEK |
-| LTDB | 0.510507 | 0.566359 | | 0.534873 | 0.512266 | 0.406171 | 0.524763 | 0.329828 | 0.811737 | 0.835553 | | 0.256447 | 0.409530 | 0.692355 | | LTDB |
-| MITDB | 0.320425 | 0.159042 | | 0.323526 | 0.158357 | 0.143632 | 0.163465 | 0.112398 | 0.606652 | 0.590892 | | 0.053749 | 0.367125 | 0.548995 | | MITDB |
-| TAO | 0.808453 | 0.728970 | | 0.840276 | 0.928987 | 0.994270 | 0.930020 | 0.995044 | 0.721004 | 0.747945 | | 0.710584 | 0.807694 | 0.684119 | | TAO |
-| SED | 0.040176 | 0.807209 | | 0.030270 | 0.053374 | 0.058416 | 0.055985 | 0.056562 | 0.907086 | 0.895291 | | 0.111105 | 0.253420 | 0.882987 | | SED |
-| Daphnet | 0.519516 | 0.518905 | | 0.441568 | 0.122992 | 0.398456 | 0.123026 | 0.133080 | 0.035690 | 0.039747 | | 0.134804 | 0.253956 | 0.035185 | | Daphnet |
-| SWaT | 0.103364 | 0.368230 | | 0.375512 | 0.365922 | 0.680748 | 0.366250 | 0.669824 | 0.381310 | 0.111519 | | 0.121168 | 0.113915 | 0.110850 | | SWaT |
-| Power | 0.122294 | 0.076373 | | 0.079616 | 0.067090 | 0.075487 | 0.067090 | 0.067250 | 0.510537 | 0.524166 | | 0.090503 | 0.160858 | 0.166562 | | Power |
-| CATSv2 | 0.232252 | 0.105175 | | 0.232417 | 0.406831 | 0.288293 | 0.282282 | 0.329056 | 0.230658 | 0.229268 | | 0.063826 | 0.393045 | 0.354730 | | CATSv2 |
-| **Insgesamt** | **0.478** | **0.341** | | **0.444** | **0.375** | **0.367** | **0.377** | **0.320** | **0.423** | **0.489** | | **0.185** | **0.473** | **0.384** | | **Insgesamt** |
+| Datensatz | POLY | iForest | Sub-iForest | PCA | USAD | CNN | OmAn | LSTM | kShape | kMeans | OCSVM | LOF | Sub-LOF | KNN | KNNV2 | Datensatz |
+| ------------- | --------- | --------- | ----------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | ----- | ------------- |
+| UCR | 0.198780 | 0.058773 | | 0.152678 | 0.084437 | 0.065443 | 0.089552 | 0.023293 | 0.460106 | 0.486822 | 0.336356 | 0.051489 | 0.514326 | 0.329748 | | UCR |
+| SMD | 0.663191 | 0.342406 | | 0.467257 | 0.629404 | 0.545045 | 0.478742 | 0.483944 | 0.122593 | 0.254537 | 0.115908 | 0.134985 | 0.544696 | 0.163803 | | SMD |
+| YAHOO | 0.307653 | 0.135664 | | 0.163729 | 0.192366 | 0.512096 | 0.219787 | 0.417416 | 0.491670 | 0.781824 | 0.306932 | 0.381508 | 0.451001 | 0.502031 | | YAHOO |
+| Exathlon | 0.847945 | 0.964836 | | 0.959173 | 0.864301 | 0.657757 | 0.876372 | 0.735253 | 0.420845 | 0.618051 | 0.557891 | 0.240338 | 0.262478 | 0.562449 | | Exathlon |
+| NAB | 0.584112 | 0.342137 | | 0.511398 | 0.308303 | 0.248091 | 0.342007 | 0.173732 | 0.415046 | 0.427477 | 0.376638 | 0.165966 | 0.532450 | 0.341432 | | NAB |
+| OPPORTUNITY | 0.426326 | 0.736264 | | 0.924516 | 0.651269 | 0.632799 | 0.649931 | 0.563772 | 0.347881 | 0.351828 | 0.148914 | 0.159200 | 0.149548 | 0.295398 | | OPPORTUNITY |
+| WSD | 0.531110 | 0.027487 | | 0.155366 | 0.155950 | 0.224922 | 0.157612 | 0.130270 | 0.112541 | 0.173060 | 0.039999 | 0.102346 | 0.551041 | 0.083204 | | WSD |
+| SVDB | 0.445531 | 0.307322 | | 0.523984 | 0.435722 | 0.199785 | 0.445289 | 0.127489 | 0.810962 | 0.811385 | 0.592468 | 0.050189 | 0.383000 | 0.591484 | | SVDB |
+| SMAP | 0.727079 | 0.327727 | | 0.566842 | 0.302212 | 0.336818 | 0.417852 | 0.273214 | 0.609905 | 0.657738 | 0.565618 | 0.172499 | 0.597854 | 0.558287 | | SMAP |
+| IOPS | 0.365055 | 0.134518 | | 0.260372 | 0.192815 | 0.205826 | 0.192837 | 0.213044 | 0.064609 | 0.107988 | 0.067672 | 0.123657 | 0.380817 | 0.116527 | | IOPS |
+| TODS | 0.586493 | 0.487386 | | 0.552877 | 0.529843 | 0.575941 | 0.538194 | 0.522228 | 0.747606 | 0.739257 | 0.748147 | 0.485079 | 0.691125 | 0.780280 | | TODS |
+| Stock | 0.805375 | 0.735886 | | 0.825382 | 0.771612 | 0.939925 | 0.771658 | 0.817960 | 0.717890 | 0.738198 | 0.717377 | 0.710487 | 0.747567 | 0.691512 | | Stock |
+| MGAB | 0.008269 | 0.004379 | | 0.005551 | 0.005403 | 0.006988 | 0.005586 | 0.025419 | 0.069759 | 0.036645 | 0.007014 | 0.004639 | 0.634748 | 0.238613 | | MGAB |
+| MSL | 0.734244 | 0.448709 | | 0.523310 | 0.295208 | 0.346350 | 0.428048 | 0.305128 | 0.459063 | 0.441543 | 0.276927 | 0.186700 | 0.544711 | 0.368746 | | MSL |
+| NEK | 0.764836 | 0.649735 | | 0.929171 | 0.827283 | 0.714845 | 0.858349 | 0.689697 | 0.357818 | 0.317927 | 0.367261 | 0.395130 | 0.687020 | 0.132908 | | NEK |
+| LTDB | 0.510507 | 0.566359 | | 0.534873 | 0.512266 | 0.406171 | 0.524763 | 0.329828 | 0.811737 | 0.835553 | 0.692470 | 0.256447 | 0.409530 | 0.692355 | | LTDB |
+| MITDB | 0.320425 | 0.159042 | | 0.323526 | 0.158357 | 0.143632 | 0.163465 | 0.112398 | 0.606652 | 0.590892 | 0.351133 | 0.053749 | 0.367125 | 0.548995 | | MITDB |
+| TAO | 0.808453 | 0.728970 | | 0.840276 | 0.928987 | 0.994270 | 0.930020 | 0.995044 | 0.721004 | 0.747945 | 0.720442 | 0.710584 | 0.807694 | 0.684119 | | TAO |
+| SED | 0.040176 | 0.807209 | | 0.030270 | 0.053374 | 0.058416 | 0.055985 | 0.056562 | 0.907086 | 0.895291 | 0.164910 | 0.111105 | 0.253420 | 0.882987 | | SED |
+| Daphnet | 0.519516 | 0.518905 | | 0.441568 | 0.122992 | 0.398456 | 0.123026 | 0.133080 | 0.035690 | 0.039747 | 0.070139 | 0.134804 | 0.253956 | 0.035185 | | Daphnet |
+| SWaT | 0.103364 | 0.368230 | | 0.375512 | 0.365922 | 0.680748 | 0.366250 | 0.669824 | 0.381310 | 0.111519 | 0.159981 | 0.121168 | 0.113915 | 0.110850 | | SWaT |
+| Power | 0.122294 | 0.076373 | | 0.079616 | 0.067090 | 0.075487 | 0.067090 | 0.067250 | 0.510537 | 0.524166 | 0.225393 | 0.090503 | 0.160858 | 0.166562 | | Power |
+| CATSv2 | 0.232252 | 0.105175 | | 0.232417 | 0.406831 | 0.288293 | 0.282282 | 0.329056 | 0.230658 | 0.229268 | 0.311384 | 0.063826 | 0.393045 | 0.354730 | | CATSv2 |
+| **Insgesamt** | **0.478** | **0.341** | | **0.444** | **0.375** | **0.367** | **0.377** | **0.320** | **0.423** | **0.489** | **0.339** | **0.185** | **0.473** | **0.384** | | **Insgesamt** |
## Gesamtbewertung der Punktanomalien nach AUC-PR
-| Datensatz | POLY | iForest | Sub-iForest | PCA | USAD | CNN | OmAn | LSTM | kShape | kMeans | OCSVM | LOF | Sub-LOF | KNN | KNNV2 | Datensatz |
-| ------------- | --------- | --------- | ----------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | ----- | --------- | --------- | --------- | ----- | ------------- |
-| UCR | 0.000257 | 0.000139 | | 0.000196 | 0.000178 | 0.000498 | 0.000178 | 0.000221 | 0.000854 | 0.002843 | | 0.000653 | 0.040927 | 0.003749 | | UCR |
-| YAHOO | 0.024161 | 0.007312 | | 0.060498 | 0.087663 | 0.587049 | 0.122574 | 0.455103 | 0.464439 | 0.742578 | | 0.422520 | 0.141987 | 0.130716 | | YAHOO |
-| WSD | 0.167066 | 0.000501 | | 0.206655 | 0.285712 | 0.342989 | 0.407280 | 0.334638 | 0.001119 | 0.003073 | | 0.003879 | 0.327958 | 0.000945 | | WSD |
-| IOPS | 0.141657 | 0.011860 | | 0.036186 | 0.016979 | 0.210157 | 0.016952 | 0.197084 | 0.148483 | 0.066886 | | 0.176712 | 0.121683 | 0.041389 | | IOPS |
-| TODS | 0.061866 | 0.069264 | | 0.067229 | 0.065809 | 0.531847 | 0.064650 | 0.367334 | 0.081935 | 0.083989 | | 0.071959 | 0.077775 | 0.075986 | | TODS |
-| Stock | 0.146590 | 0.143940 | | 0.148428 | 0.133570 | 0.929344 | 0.133646 | 0.519232 | 0.095333 | 0.114476 | | 0.112061 | 0.115033 | 0.096103 | | Stock |
-| TAO | 0.148356 | 0.126763 | | 0.160428 | 0.330086 | 0.993457 | 0.336829 | 0.994218 | 0.101534 | 0.121904 | | 0.128972 | 0.145212 | 0.098915 | | TAO |
-| **Insgesamt** | **0.060** | **0.039** | | **0.079** | **0.100** | **0.585** | **0.125** | **0.426** | **0.293** | **0.451** | | **0.274** | **0.131** | **0.101** | | **Insgesamt** |
+| Datensatz | POLY | iForest | Sub-iForest | PCA | USAD | CNN | OmAn | LSTM | kShape | kMeans | OCSVM | LOF | Sub-LOF | KNN | KNNV2 | Datensatz |
+| ------------- | --------- | --------- | ----------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | ----- | ------------- |
+| UCR | 0.000257 | 0.000139 | | 0.000196 | 0.000178 | 0.000498 | 0.000178 | 0.000221 | 0.000854 | 0.002843 | 0.001794 | 0.000653 | 0.040927 | 0.003749 | | UCR |
+| YAHOO | 0.024161 | 0.007312 | | 0.060498 | 0.087663 | 0.587049 | 0.122574 | 0.455103 | 0.464439 | 0.742578 | 0.040087 | 0.422520 | 0.141987 | 0.130716 | | YAHOO |
+| WSD | 0.167066 | 0.000501 | | 0.206655 | 0.285712 | 0.342989 | 0.407280 | 0.334638 | 0.001119 | 0.003073 | 0.002159 | 0.003879 | 0.327958 | 0.000945 | | WSD |
+| IOPS | 0.141657 | 0.011860 | | 0.036186 | 0.016979 | 0.210157 | 0.016952 | 0.197084 | 0.148483 | 0.066886 | 0.024701 | 0.176712 | 0.121683 | 0.041389 | | IOPS |
+| TODS | 0.061866 | 0.069264 | | 0.067229 | 0.065809 | 0.531847 | 0.064650 | 0.367334 | 0.081935 | 0.083989 | 0.070625 | 0.071959 | 0.077775 | 0.075986 | | TODS |
+| Stock | 0.146590 | 0.143940 | | 0.148428 | 0.133570 | 0.929344 | 0.133646 | 0.519232 | 0.095333 | 0.114476 | 0.097345 | 0.112061 | 0.115033 | 0.096103 | | Stock |
+| TAO | 0.148356 | 0.126763 | | 0.160428 | 0.330086 | 0.993457 | 0.336829 | 0.994218 | 0.101534 | 0.121904 | 0.110446 | 0.128972 | 0.145212 | 0.098915 | | TAO |
+| **Insgesamt** | **0.060** | **0.039** | | **0.079** | **0.100** | **0.585** | **0.125** | **0.426** | **0.293** | **0.451** | **0.049** | **0.274** | **0.131** | **0.101** | | **Insgesamt** |
## Gesamtbewertung der Punktanomalien nach VUS-PR
-| Datensatz | POLY | iForest | Sub-iForest | PCA | USAD | CNN | OmAn | LSTM | kShape | kMeans | OCSVM | LOF | Sub-LOF | KNN | KNNV2 | Datensatz |
-| ------------- | --------- | --------- | ----------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | ----- | --------- | --------- | --------- | ----- | ------------- |
-| UCR | 0.003356 | 0.000267 | | 0.000408 | 0.000406 | 0.128496 | 0.000406 | 0.000237 | 0.004607 | 0.012483 | | 0.012963 | 0.208122 | 0.011814 | | UCR |
-| YAHOO | 0.282071 | 0.052201 | | 0.113436 | 0.159136 | 0.519819 | 0.190097 | 0.414255 | 0.518467 | 0.826036 | | 0.383203 | 0.475296 | 0.521624 | | YAHOO |
-| WSD | 0.216267 | 0.001036 | | 0.124850 | 0.088179 | 0.131018 | 0.105822 | 0.100076 | 0.007506 | 0.021818 | | 0.035113 | 0.140331 | 0.007947 | | WSD |
-| IOPS | 0.187766 | 0.017686 | | 0.048169 | 0.042432 | 0.147113 | 0.035031 | 0.080739 | 0.065996 | 0.202456 | | 0.073159 | 0.327754 | 0.327373 | | IOPS |
-| TODS | 0.803771 | 0.815915 | | 0.820717 | 0.780347 | 0.895740 | 0.800324 | 0.852834 | 0.850387 | 0.857103 | | 0.786747 | 0.849582 | 0.855125 | | TODS |
-| Stock | 0.805375 | 0.735886 | | 0.825382 | 0.771612 | 0.939925 | 0.771658 | 0.817960 | 0.717890 | 0.738198 | | 0.710487 | 0.747567 | 0.691512 | | Stock |
-| TAO | 0.761585 | 0.654975 | | 0.805852 | 0.907035 | 0.991524 | 0.908594 | 0.992980 | 0.634080 | 0.666432 | | 0.623702 | 0.755775 | 0.570248 | | TAO |
-| **Insgesamt** | **0.392** | **0.232** | | **0.294** | **0.310** | **0.571** | **0.329** | **0.471** | **0.495** | **0.680** | | **0.416** | **0.514** | **0.500** | | **Insgesamt** |
+| Datensatz | POLY | iForest | Sub-iForest | PCA | USAD | CNN | OmAn | LSTM | kShape | kMeans | OCSVM | LOF | Sub-LOF | KNN | KNNV2 | Datensatz |
+| ------------- | --------- | --------- | ----------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | ----- | ------------- |
+| UCR | 0.003356 | 0.000267 | | 0.000408 | 0.000406 | 0.128496 | 0.000406 | 0.000237 | 0.004607 | 0.012483 | 0.027690 | 0.012963 | 0.208122 | 0.011814 | | UCR |
+| YAHOO | 0.282071 | 0.052201 | | 0.113436 | 0.159136 | 0.519819 | 0.190097 | 0.414255 | 0.518467 | 0.826036 | 0.282248 | 0.383203 | 0.475296 | 0.521624 | | YAHOO |
+| WSD | 0.216267 | 0.001036 | | 0.124850 | 0.088179 | 0.131018 | 0.105822 | 0.100076 | 0.007506 | 0.021818 | 0.006227 | 0.035113 | 0.140331 | 0.007947 | | WSD |
+| IOPS | 0.187766 | 0.017686 | | 0.048169 | 0.042432 | 0.147113 | 0.035031 | 0.080739 | 0.065996 | 0.202456 | 0.019092 | 0.073159 | 0.327754 | 0.327373 | | IOPS |
+| TODS | 0.803771 | 0.815915 | | 0.820717 | 0.780347 | 0.895740 | 0.800324 | 0.852834 | 0.850387 | 0.857103 | 0.851819 | 0.786747 | 0.849582 | 0.855125 | | TODS |
+| Stock | 0.805375 | 0.735886 | | 0.825382 | 0.771612 | 0.939925 | 0.771658 | 0.817960 | 0.717890 | 0.738198 | 0.717377 | 0.710487 | 0.747567 | 0.691512 | | Stock |
+| TAO | 0.761585 | 0.654975 | | 0.805852 | 0.907035 | 0.991524 | 0.908594 | 0.992980 | 0.634080 | 0.666432 | 0.624783 | 0.623702 | 0.755775 | 0.570248 | | TAO |
+| **Insgesamt** | **0.392** | **0.232** | | **0.294** | **0.310** | **0.571** | **0.329** | **0.471** | **0.495** | **0.680** | **0.362** | **0.416** | **0.514** | **0.500** | | **Insgesamt** |
## Gesamtbewertung der Sequenzanomalien nach AUC-PR
-| Datensatz | POLY | iForest | Sub-iForest | PCA | USAD | CNN | OmAn | LSTM | kShape | kMeans | OCSVM | LOF | Sub-LOF | KNN | KNNV2 | Datensatz |
-| ------------- | --------- | --------- | ----------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | ----- | --------- | --------- | --------- | ----- | ------------- |
-| SMD | 0.439343 | 0.336128 | | 0.459753 | 0.561216 | 0.505749 | 0.460181 | 0.495482 | 0.077411 | 0.095816 | | 0.163313 | 0.339639 | 0.050501 | | SMD |
-| YAHOO | 0.571428 | 0.981990 | | 0.690088 | 0.349449 | 0.345384 | 0.349465 | 0.344485 | 0.240316 | 0.462352 | | 0.316546 | 0.242005 | 0.306868 | | YAHOO |
-| Exathlon | 0.844513 | 0.962806 | | 0.958102 | 0.862358 | 0.654310 | 0.874519 | 0.733875 | 0.420990 | 0.617690 | | 0.245106 | 0.257618 | 0.563481 | | Exathlon |
-| NAB | 0.402974 | 0.251317 | | 0.439685 | 0.272908 | 0.209869 | 0.335713 | 0.162982 | 0.251877 | 0.230029 | | 0.154619 | 0.457316 | 0.176029 | | NAB |
-| OPPORTUNITY | 0.624496 | 0.870159 | | 0.968964 | 0.661820 | 0.727956 | 0.661818 | 0.547465 | 0.283461 | 0.276125 | | 0.144952 | 0.154555 | 0.263868 | | OPPORTUNITY |
-| WSD | 0.324235 | 0.067695 | | 0.099717 | 0.122887 | 0.242314 | 0.123481 | 0.127908 | 0.143776 | 0.253831 | | 0.094898 | 0.452830 | 0.077550 | | WSD |
-| SVDB | 0.483934 | 0.313933 | | 0.541451 | 0.445765 | 0.198720 | 0.465020 | 0.130291 | 0.788394 | 0.799099 | | 0.046729 | 0.329395 | 0.519348 | | SVDB |
-| SMAP | 0.798189 | 0.481811 | | 0.887517 | 0.865663 | 0.241609 | 0.876352 | 0.233398 | 0.850192 | 0.928300 | | 0.060424 | 0.488277 | 0.788114 | | SMAP |
-| IOPS | 0.482294 | 0.276687 | | 0.549216 | 0.710230 | 0.429909 | 0.721261 | 0.461068 | 0.069434 | 0.061431 | | 0.272670 | 0.157124 | 0.031872 | | IOPS |
-| TODS | 0.281649 | 0.071679 | | 0.149989 | 0.184682 | 0.206602 | 0.191109 | 0.170148 | 0.627511 | 0.592072 | | 0.081599 | 0.240756 | 0.533591 | | TODS |
-| MGAB | 0.008996 | 0.004336 | | 0.027123 | 0.032097 | 0.003855 | 0.015268 | 0.045860 | 0.084411 | 0.085299 | | 0.002209 | 0.567335 | 0.325650 | | MGAB |
-| MSL | 0.557113 | 0.453614 | | 0.475464 | 0.104606 | 0.264200 | 0.364941 | 0.257571 | 0.194985 | 0.288743 | | 0.277979 | 0.368527 | 0.238423 | | MSL |
-| NEK | 0.568731 | 0.688168 | | 0.849600 | 0.863096 | 0.715902 | 0.877024 | 0.681103 | 0.375066 | 0.331911 | | 0.356301 | 0.433487 | 0.146699 | | NEK |
-| LTDB | 0.455324 | 0.521341 | | 0.489411 | 0.461335 | 0.354462 | 0.479711 | 0.276735 | 0.743642 | 0.765819 | | 0.203241 | 0.333160 | 0.589828 | | LTDB |
-| MITDB | 0.366934 | 0.169557 | | 0.377853 | 0.201810 | 0.160153 | 0.206999 | 0.113066 | 0.594424 | 0.583808 | | 0.051628 | 0.385195 | 0.500834 | | MITDB |
-| SED | 0.031421 | 0.691484 | | 0.020994 | 0.026231 | 0.029325 | 0.031663 | 0.030497 | 0.722020 | 0.710575 | | 0.082198 | 0.109660 | 0.707453 | | SED |
-| Daphnet | 0.536827 | 0.496579 | | 0.468647 | 0.192057 | 0.381499 | 0.192069 | 0.132234 | 0.033420 | 0.036445 | | 0.129509 | 0.252282 | 0.033893 | | Daphnet |
-| SWaT | 0.123133 | 0.702432 | | 0.756700 | 0.757484 | 0.738718 | 0.757687 | 0.737695 | 0.458896 | 0.111152 | | 0.120683 | 0.113563 | 0.110376 | | SWaT |
-| Power | 0.203760 | 0.073981 | | 0.078866 | 0.064665 | 0.072400 | 0.064665 | 0.064788 | 0.496250 | 0.507629 | | 0.086245 | 0.175035 | 0.160167 | | Power |
-| CATSv2 | 0.476434 | 0.139416 | | 0.474705 | 0.481625 | 0.411894 | 0.437868 | 0.358071 | 0.452377 | 0.455611 | | 0.071542 | 0.511976 | 0.482166 | | CATSv2 |
-| **Insgesamt** | **0.499** | **0.479** | | **0.553** | **0.474** | **0.387** | **0.481** | **0.357** | **0.396** | **0.449** | | **0.150** | **0.330** | **0.364** | | **Insgesamt** |
+| Datensatz | POLY | iForest | Sub-iForest | PCA | USAD | CNN | OmAn | LSTM | kShape | kMeans | OCSVM | LOF | Sub-LOF | KNN | KNNV2 | Datensatz |
+| ------------- | --------- | --------- | ----------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | ----- | ------------- |
+| SMD | 0.439343 | 0.336128 | | 0.459753 | 0.561216 | 0.505749 | 0.460181 | 0.495482 | 0.077411 | 0.095816 | 0.077154 | 0.163313 | 0.339639 | 0.050501 | | SMD |
+| YAHOO | 0.571428 | 0.981990 | | 0.690088 | 0.349449 | 0.345384 | 0.349465 | 0.344485 | 0.240316 | 0.462352 | 0.498439 | 0.316546 | 0.242005 | 0.306868 | | YAHOO |
+| Exathlon | 0.844513 | 0.962806 | | 0.958102 | 0.862358 | 0.654310 | 0.874519 | 0.733875 | 0.420990 | 0.617690 | 0.556786 | 0.245106 | 0.257618 | 0.563481 | | Exathlon |
+| NAB | 0.402974 | 0.251317 | | 0.439685 | 0.272908 | 0.209869 | 0.335713 | 0.162982 | 0.251877 | 0.230029 | 0.245686 | 0.154619 | 0.457316 | 0.176029 | | NAB |
+| OPPORTUNITY | 0.624496 | 0.870159 | | 0.968964 | 0.661820 | 0.727956 | 0.661818 | 0.547465 | 0.283461 | 0.276125 | 0.090132 | 0.144952 | 0.154555 | 0.263868 | | OPPORTUNITY |
+| WSD | 0.324235 | 0.067695 | | 0.099717 | 0.122887 | 0.242314 | 0.123481 | 0.127908 | 0.143776 | 0.253831 | 0.038635 | 0.094898 | 0.452830 | 0.077550 | | WSD |
+| SVDB | 0.483934 | 0.313933 | | 0.541451 | 0.445765 | 0.198720 | 0.465020 | 0.130291 | 0.788394 | 0.799099 | 0.544488 | 0.046729 | 0.329395 | 0.519348 | | SVDB |
+| SMAP | 0.798189 | 0.481811 | | 0.887517 | 0.865663 | 0.241609 | 0.876352 | 0.233398 | 0.850192 | 0.928300 | 0.760857 | 0.060424 | 0.488277 | 0.788114 | | SMAP |
+| IOPS | 0.482294 | 0.276687 | | 0.549216 | 0.710230 | 0.429909 | 0.721261 | 0.461068 | 0.069434 | 0.061431 | 0.101872 | 0.272670 | 0.157124 | 0.031872 | | IOPS |
+| TODS | 0.281649 | 0.071679 | | 0.149989 | 0.184682 | 0.206602 | 0.191109 | 0.170148 | 0.627511 | 0.592072 | 0.557978 | 0.081599 | 0.240756 | 0.533591 | | TODS |
+| MGAB | 0.008996 | 0.004336 | | 0.027123 | 0.032097 | 0.003855 | 0.015268 | 0.045860 | 0.084411 | 0.085299 | 0.032883 | 0.002209 | 0.567335 | 0.325650 | | MGAB |
+| MSL | 0.557113 | 0.453614 | | 0.475464 | 0.104606 | 0.264200 | 0.364941 | 0.257571 | 0.194985 | 0.288743 | 0.153546 | 0.277979 | 0.368527 | 0.238423 | | MSL |
+| NEK | 0.568731 | 0.688168 | | 0.849600 | 0.863096 | 0.715902 | 0.877024 | 0.681103 | 0.375066 | 0.331911 | 0.420323 | 0.356301 | 0.433487 | 0.146699 | | NEK |
+| LTDB | 0.455324 | 0.521341 | | 0.489411 | 0.461335 | 0.354462 | 0.479711 | 0.276735 | 0.743642 | 0.765819 | 0.634659 | 0.203241 | 0.333160 | 0.589828 | | LTDB |
+| MITDB | 0.366934 | 0.169557 | | 0.377853 | 0.201810 | 0.160153 | 0.206999 | 0.113066 | 0.594424 | 0.583808 | 0.355826 | 0.051628 | 0.385195 | 0.500834 | | MITDB |
+| SED | 0.031421 | 0.691484 | | 0.020994 | 0.026231 | 0.029325 | 0.031663 | 0.030497 | 0.722020 | 0.710575 | 0.187850 | 0.082198 | 0.109660 | 0.707453 | | SED |
+| Daphnet | 0.536827 | 0.496579 | | 0.468647 | 0.192057 | 0.381499 | 0.192069 | 0.132234 | 0.033420 | 0.036445 | 0.066512 | 0.129509 | 0.252282 | 0.033893 | | Daphnet |
+| SWaT | 0.123133 | 0.702432 | | 0.756700 | 0.757484 | 0.738718 | 0.757687 | 0.737695 | 0.458896 | 0.111152 | 0.159515 | 0.120683 | 0.113563 | 0.110376 | | SWaT |
+| Power | 0.203760 | 0.073981 | | 0.078866 | 0.064665 | 0.072400 | 0.064665 | 0.064788 | 0.496250 | 0.507629 | 0.215936 | 0.086245 | 0.175035 | 0.160167 | | Power |
+| CATSv2 | 0.476434 | 0.139416 | | 0.474705 | 0.481625 | 0.411894 | 0.437868 | 0.358071 | 0.452377 | 0.455611 | 0.369080 | 0.071542 | 0.511976 | 0.482166 | | CATSv2 |
+| **Insgesamt** | **0.499** | **0.479** | | **0.553** | **0.474** | **0.387** | **0.481** | **0.357** | **0.396** | **0.449** | **0.335** | **0.150** | **0.330** | **0.364** | | **Insgesamt** |
## Gesamtbewertung der Sequenzanomalien nach VUS-PR
-| Datensatz | POLY | iForest | Sub-iForest | PCA | USAD | CNN | OmAn | LSTM | kShape | kMeans | OCSVM | LOF | Sub-LOF | KNN | KNNV2 | Datensatz |
-| ------------- | --------- | --------- | ----------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | ----- | --------- | --------- | --------- | ----- | ------------- |
-| SMD | 0.482296 | 0.322146 | | 0.423162 | 0.519965 | 0.503535 | 0.410939 | 0.453416 | 0.070547 | 0.092843 | | 0.137760 | 0.457631 | 0.063644 | | SMD |
-| YAHOO | 0.518566 | 0.936245 | | 0.568333 | 0.354527 | 0.351064 | 0.354507 | 0.348855 | 0.276917 | 0.477510 | | 0.304093 | 0.188491 | 0.322916 | | YAHOO |
-| Exathlon | 0.847945 | 0.964836 | | 0.959173 | 0.864301 | 0.657757 | 0.876372 | 0.735253 | 0.420845 | 0.618051 | | 0.240338 | 0.262478 | 0.562449 | | Exathlon |
-| NAB | 0.478372 | 0.285635 | | 0.474780 | 0.262268 | 0.238264 | 0.326918 | 0.181686 | 0.282421 | 0.272775 | | 0.183564 | 0.511944 | 0.219327 | | NAB |
-| OPPORTUNITY | 0.630618 | 0.868003 | | 0.966351 | 0.660160 | 0.741769 | 0.660151 | 0.567052 | 0.253277 | 0.255118 | | 0.152842 | 0.139082 | 0.257788 | | OPPORTUNITY |
-| WSD | 0.457794 | 0.038526 | | 0.067564 | 0.086910 | 0.206990 | 0.087346 | 0.100554 | 0.192884 | 0.293116 | | 0.073377 | 0.608260 | 0.142998 | | WSD |
-| SVDB | 0.423050 | 0.314567 | | 0.508621 | 0.424770 | 0.206479 | 0.436465 | 0.131672 | 0.804276 | 0.804472 | | 0.052218 | 0.359030 | 0.579059 | | SVDB |
-| SMAP | 0.891481 | 0.487664 | | 0.951739 | 0.947702 | 0.237156 | 0.951635 | 0.223192 | 0.861769 | 0.946525 | | 0.046142 | 0.599966 | 0.818737 | | SMAP |
-| IOPS | 0.426245 | 0.267117 | | 0.422834 | 0.341683 | 0.312286 | 0.380533 | 0.334417 | 0.090598 | 0.112229 | | 0.302989 | 0.258387 | 0.111561 | | IOPS |
-| TODS | 0.437694 | 0.174752 | | 0.342464 | 0.336685 | 0.331028 | 0.338275 | 0.263210 | 0.719779 | 0.689051 | | 0.198760 | 0.600606 | 0.773793 | | TODS |
-| MGAB | 0.008269 | 0.004379 | | 0.005551 | 0.005403 | 0.006988 | 0.005586 | 0.025419 | 0.069759 | 0.036645 | | 0.004639 | 0.634748 | 0.238613 | | MGAB |
-| MSL | 0.653967 | 0.514630 | | 0.492975 | 0.142125 | 0.326973 | 0.412021 | 0.309296 | 0.290049 | 0.405174 | | 0.327778 | 0.487796 | 0.303536 | | MSL |
-| NEK | 0.628709 | 0.678337 | | 0.880822 | 0.868114 | 0.756555 | 0.884225 | 0.704672 | 0.282706 | 0.298379 | | 0.392335 | 0.597690 | 0.162564 | | NEK |
-| LTDB | 0.510507 | 0.566359 | | 0.534873 | 0.512266 | 0.406171 | 0.524763 | 0.329828 | 0.811737 | 0.835553 | | 0.256447 | 0.409530 | 0.692355 | | LTDB |
-| MITDB | 0.320425 | 0.159042 | | 0.323526 | 0.158357 | 0.143632 | 0.163465 | 0.112398 | 0.606652 | 0.590892 | | 0.053749 | 0.367125 | 0.548995 | | MITDB |
-| SED | 0.040176 | 0.807209 | | 0.030270 | 0.053374 | 0.058416 | 0.055985 | 0.056562 | 0.907086 | 0.895291 | | 0.111105 | 0.253420 | 0.882987 | | SED |
-| Daphnet | 0.519516 | 0.518905 | | 0.441568 | 0.122992 | 0.398456 | 0.123026 | 0.133080 | 0.035690 | 0.039747 | | 0.134804 | 0.253956 | 0.035185 | | Daphnet |
-| SWaT | 0.103364 | 0.368230 | | 0.375512 | 0.365922 | 0.680748 | 0.366250 | 0.669824 | 0.381310 | 0.111519 | | 0.121168 | 0.113915 | 0.110850 | | SWaT |
-| Power | 0.122294 | 0.076373 | | 0.079616 | 0.067090 | 0.075487 | 0.067090 | 0.067250 | 0.510537 | 0.524166 | | 0.090503 | 0.160858 | 0.166562 | | Power |
-| CATSv2 | 0.232252 | 0.105175 | | 0.232417 | 0.406831 | 0.288293 | 0.282282 | 0.329056 | 0.230658 | 0.229268 | | 0.063826 | 0.393045 | 0.354730 | | CATSv2 |
-| **Insgesamt** | **0.521** | **0.484** | | **0.545** | **0.461** | **0.395** | **0.467** | **0.358** | **0.411** | **0.465** | | **0.158** | **0.393** | **0.399** | | **Insgesamt** |
+| Datensatz | POLY | iForest | Sub-iForest | PCA | USAD | CNN | OmAn | LSTM | kShape | kMeans | OCSVM | LOF | Sub-LOF | KNN | KNNV2 | Datensatz |
+| ------------- | --------- | --------- | ----------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | ----- | ------------- |
+| SMD | 0.482296 | 0.322146 | | 0.423162 | 0.519965 | 0.503535 | 0.410939 | 0.453416 | 0.070547 | 0.092843 | 0.078384 | 0.137760 | 0.457631 | 0.063644 | | SMD |
+| YAHOO | 0.518566 | 0.936245 | | 0.568333 | 0.354527 | 0.351064 | 0.354507 | 0.348855 | 0.276917 | 0.477510 | 0.484099 | 0.304093 | 0.188491 | 0.322916 | | YAHOO |
+| Exathlon | 0.847945 | 0.964836 | | 0.959173 | 0.864301 | 0.657757 | 0.876372 | 0.735253 | 0.420845 | 0.618051 | 0.557891 | 0.240338 | 0.262478 | 0.562449 | | Exathlon |
+| NAB | 0.478372 | 0.285635 | | 0.474780 | 0.262268 | 0.238264 | 0.326918 | 0.181686 | 0.282421 | 0.272775 | 0.297149 | 0.183564 | 0.511944 | 0.219327 | | NAB |
+| OPPORTUNITY | 0.630618 | 0.868003 | | 0.966351 | 0.660160 | 0.741769 | 0.660151 | 0.567052 | 0.253277 | 0.255118 | 0.092515 | 0.152842 | 0.139082 | 0.257788 | | OPPORTUNITY |
+| WSD | 0.457794 | 0.038526 | | 0.067564 | 0.086910 | 0.206990 | 0.087346 | 0.100554 | 0.192884 | 0.293116 | 0.048826 | 0.073377 | 0.608260 | 0.142998 | | WSD |
+| SVDB | 0.423050 | 0.314567 | | 0.508621 | 0.424770 | 0.206479 | 0.436465 | 0.131672 | 0.804276 | 0.804472 | 0.597908 | 0.052218 | 0.359030 | 0.579059 | | SVDB |
+| SMAP | 0.891481 | 0.487664 | | 0.951739 | 0.947702 | 0.237156 | 0.951635 | 0.223192 | 0.861769 | 0.946525 | 0.795429 | 0.046142 | 0.599966 | 0.818737 | | SMAP |
+| IOPS | 0.426245 | 0.267117 | | 0.422834 | 0.341683 | 0.312286 | 0.380533 | 0.334417 | 0.090598 | 0.112229 | 0.081453 | 0.302989 | 0.258387 | 0.111561 | | IOPS |
+| TODS | 0.437694 | 0.174752 | | 0.342464 | 0.336685 | 0.331028 | 0.338275 | 0.263210 | 0.719779 | 0.689051 | 0.721308 | 0.198760 | 0.600606 | 0.773793 | | TODS |
+| MGAB | 0.008269 | 0.004379 | | 0.005551 | 0.005403 | 0.006988 | 0.005586 | 0.025419 | 0.069759 | 0.036645 | 0.007014 | 0.004639 | 0.634748 | 0.238613 | | MGAB |
+| MSL | 0.653967 | 0.514630 | | 0.492975 | 0.142125 | 0.326973 | 0.412021 | 0.309296 | 0.290049 | 0.405174 | 0.208802 | 0.327778 | 0.487796 | 0.303536 | | MSL |
+| NEK | 0.628709 | 0.678337 | | 0.880822 | 0.868114 | 0.756555 | 0.884225 | 0.704672 | 0.282706 | 0.298379 | 0.379838 | 0.392335 | 0.597690 | 0.162564 | | NEK |
+| LTDB | 0.510507 | 0.566359 | | 0.534873 | 0.512266 | 0.406171 | 0.524763 | 0.329828 | 0.811737 | 0.835553 | 0.692470 | 0.256447 | 0.409530 | 0.692355 | | LTDB |
+| MITDB | 0.320425 | 0.159042 | | 0.323526 | 0.158357 | 0.143632 | 0.163465 | 0.112398 | 0.606652 | 0.590892 | 0.351133 | 0.053749 | 0.367125 | 0.548995 | | MITDB |
+| SED | 0.040176 | 0.807209 | | 0.030270 | 0.053374 | 0.058416 | 0.055985 | 0.056562 | 0.907086 | 0.895291 | 0.164910 | 0.111105 | 0.253420 | 0.882987 | | SED |
+| Daphnet | 0.519516 | 0.518905 | | 0.441568 | 0.122992 | 0.398456 | 0.123026 | 0.133080 | 0.035690 | 0.039747 | 0.070139 | 0.134804 | 0.253956 | 0.035185 | | Daphnet |
+| SWaT | 0.103364 | 0.368230 | | 0.375512 | 0.365922 | 0.680748 | 0.366250 | 0.669824 | 0.381310 | 0.111519 | 0.159981 | 0.121168 | 0.113915 | 0.110850 | | SWaT |
+| Power | 0.122294 | 0.076373 | | 0.079616 | 0.067090 | 0.075487 | 0.067090 | 0.067250 | 0.510537 | 0.524166 | 0.225393 | 0.090503 | 0.160858 | 0.166562 | | Power |
+| CATSv2 | 0.232252 | 0.105175 | | 0.232417 | 0.406831 | 0.288293 | 0.282282 | 0.329056 | 0.230658 | 0.229268 | 0.311384 | 0.063826 | 0.393045 | 0.354730 | | CATSv2 |
+| **Insgesamt** | **0.521** | **0.484** | | **0.545** | **0.461** | **0.395** | **0.467** | **0.358** | **0.411** | **0.465** | **0.354** | **0.158** | **0.393** | **0.399** | | **Insgesamt** |
# POLY:
+ Prozessor: CPU
@@ -249,10 +249,10 @@
+ Durchschnitt Gesamt:
+ Recall:
+ Precision:
- + Standard-F1: (vgl. , , $\approx$ %)
- + Auc-Roc: (vgl. , + , $\approx$ %)
- + Auc-PR: (vgl. , +, $\approx$ %)
- + Vus-PR: (vgl. , + , $\approx$ %)
+ + Standard-F1: (vgl. 0.22, , $\approx$ %)
+ + Auc-Roc: (vgl. 0.63, + , $\approx$ %)
+ + Auc-PR: (vgl. 0.16, +, $\approx$ %)
+ + Vus-PR: (vgl. 0.22, + , $\approx$ %)
+ Durchschnitt Punktanomalien:
+ Recall:
+ Precision:
@@ -272,14 +272,14 @@
+ Deutlich besser bei -anomalien
+ Beste erkannte Gruppe:
+ nach AUC-PR:
- +
- + nach VUS-PR:
+
+ + nach VUS-PR:
+ + (vgl. MGAB: 0.00)
+ Schlechteste erkannte Gruppe:
+ nach AUC-PR:
+
+ nach VUS-PR:
- +
+ + (vgl. TAO: 0.90)
# Sub-PCA
+ Prozessor: CPU
@@ -567,7 +567,7 @@ $\rightarrow$ Optimalen Hyperparameter pro Datensatz stimmen mit denen der Autor
**TAO DATENSATZ**
*Optimale Hyperparameter der Autoren:*
+ {'periodicity': 1}
-*Hyperparameter nach Tuning pro Datensatz V1:*
+*Hyperparameter nach Tuning pro Datensatz:*
+ {'periodicity': 1} ,257_TAO_id_1_Environment_tr_500_1st_3.csv
+ {'periodicity': 3}, 258_TAO_id_2_Environment_tr_500_1st_4.csv
+ {'periodicity': 2}, 259_TAO_id_3_Environment_tr_500_1st_7.csv
@@ -659,39 +659,65 @@ $\rightarrow$ Ergebnisse mit den optimalen Hyperparametern der Autoren noch schl
**Evaluation:**
+ Durchschnitt Gesamt:
- + Recall:
- + Precision:
- + Standard-F1: (vgl. 0.22)
- + Auc-Roc: (vgl. 0.65)
- + Auc-PR: (vgl. 0.16)
- + Vus-PR: (vgl. 0.23)
+ + Recall: 0.155
+ + Precision: 0.219
+ + Standard-F1: 0.329 (vgl. 0.22, + 0.109, $\approx$ 49.5 %)
+ + Auc-Roc: 0.773 (vgl. 0.65, + 0.123, $\approx$ 18.9 %)
+ + Auc-PR: 0.275 (vgl. 0.16, + 0.115, $\approx$ 71.9 %)
+ + Vus-PR: 0.339 (vgl. 0.23, + 0.109, $\approx$ 47.4 %)
+ Durchschnitt Punktanomalien:
- + Recall:
- + Precision:
- + Standard-F1:
- + Auc-Roc:
- + Auc-PR:
- + Vus-PR:
+ + Recall: 0.068
+ + Precision: 0.042
+ + Standard-F1: 0.096
+ + Auc-Roc: 0.781
+ + Auc-PR: 0.049
+ + Vus-PR: 0.362
+ Durchschnitt Sequenzanomalien:
- + Recall:
- + Precision:
- + Standard-F1:
- + Auc-Roc:
- + Auc-PR:
- + Vus-PR:
+ + Recall: 0.083
+ + Precision: 0.266
+ + Standard-F1: 0.384
+ + Auc-Roc: 0.755
+ + Auc-PR: 0.335
+ + Vus-PR: 0.354
**Bemerkung:**
-+ Deutlich besser bei
++ Deutlich besser bei Sequenzanomalien nach AUC-PR, nach VUS-PR relativ ähnlich tendenziell aber bei Punktanomalien mehr
++ Starke Unterschiede bei AUC-PR und VUS-PR in den Datensätzen Stock, TAO und TODS
++ Starke Abweichung der VUS-PR auf dem TAO Datensatz zw. Autoren und uns
+ Beste erkannte Gruppe:
+ nach AUC-PR:
- +
+ + LTDB: 0.634659 (TAO: 0.117776)
+ nach VUS-PR:
- + (vgl. TAO 0.92)
+ + TODS 0.748147 (vgl. 0.65; LTDB: 0.692470, vgl. 0.33; TAO: 0.720442, vgl. TAO 0.92)
+ Schlechteste erkannte Gruppe:
+ nach AUC-PR:
- +
+ + MGAB: 0.032883
+ nach VUS-PR:
- + (vgl. MGAB 0.01)
+ + MGAB: 0.007014 (vgl. MGAB 0.01)
+
+**TAO DATENSATZ**
+*Optimale Hyperparameter der Autoren:*
++ {'periodicity': 2, 'kernel': 'rbf'}
+*Hyperparameter nach Tuning pro Datensatz:*
++ {'periodicity': 1, 'kernel': 'poly'} ,257_TAO_id_1_Environment_tr_500_1st_3.csv
++ {'periodicity': 2, 'kernel': 'rbf'}, 258_TAO_id_2_Environment_tr_500_1st_4.csv
++ {'periodicity': 1, 'kernel': 'sigmoid'}, 259_TAO_id_3_Environment_tr_500_1st_7.csv
+
+**Ergebnisse mit optimalen Hyperparametern der Autoren:**
++ Datei: 257_TAO_id_1_Environment_tr_500_1st_3.csv,
+ + AUC-PR: 0.12245353442042406
+ + VUS-PR: 0.904907986376071
++ Datei: 258_TAO_id_2_Environment_tr_500_1st_4.csv,
+ + AUC-PR: 0.15662321342792412
+ + VUS-PR: 0.9449252771845215
++ Datei: 259_TAO_id_3_Environment_tr_500_1st_7.csv,
+ + AUC-PR: 0.054974065991650625
+ + VUS-PR: 0.34755231499773087
++ Durchschnittlicher AUC-PR: 0.1113502712799996
++ Durchschnittlicher VUS-PR: 0.7324618595194411
+
+$\rightarrow$ Ergebnisse mit den optimalen Hyperparametern dezent besser, allerdings nah an unseren dran. Die großen Unterschiede zu den Ergebnissen der Autoren bestehen weiterhin!
+
# LOF
+ Prozessor: CPU
+ Modell: iCore 5 8th
diff --git a/docs/evaluation/groupedDataframes/AUC-PR_combined_by_group.csv b/docs/evaluation/groupedDataframes/AUC-PR_combined_by_group.csv
index 7bcdde61476512afdaedb8412610ec43bcc4fabf..052e8ebdb973b9b4687eaa43bddd793137e8de97 100644
--- a/docs/evaluation/groupedDataframes/AUC-PR_combined_by_group.csv
+++ b/docs/evaluation/groupedDataframes/AUC-PR_combined_by_group.csv
@@ -1,12 +1,14 @@
-,Model,IOPS,SWaT,SED,MSL,SVDB,MGAB,LTDB,SMAP,Daphnet,Stock,WSD,MITDB,UCR,Power,NEK,CATSv2,NAB,Exathlon,TAO,OPPORTUNITY,TODS,SMD,YAHOO
-0,CNN,0.2658893118021881,0.7387181088941073,0.029325049402745367,0.30703296485567955,0.19710262016040547,0.003854961812131611,0.3544623948216704,0.3299309288297937,0.3814990500329981,0.9293442608636605,0.30312825425966844,0.1601528087440646,0.06947703302366708,0.0723996101568745,0.6855731430154801,0.4118939698305733,0.22334685517173777,0.6543096941901486,0.9948566473921369,0.6191602168303323,0.3037856723254471,0.565369591802724,0.5615618691240216
-0,IForest,0.23156929513103078,0.7024320276028485,0.691483898843896,0.38608540888302983,0.3119364827416871,0.004336120023749678,0.5213405915521423,0.2993019518138772,0.4965785621747466,0.14393995693030376,0.04099291102913815,0.16955657029296148,0.059389091972092405,0.0739812238848341,0.630339411513941,0.1394155160045949,0.3145868144981536,0.9628064611603744,0.12581675403237186,0.7331529032690003,0.07652134064844376,0.36923736187055917,0.09272991506414939
-0,KMeansAD_U,0.06871545205888992,0.1111516911833987,0.7105752596393731,0.37143817671367996,0.8090356381810129,0.08529855686124818,0.7658193255633926,0.6489127043129476,0.0364448974253113,0.11447566194398832,0.14637226924693936,0.5838081485697818,0.48720584604063366,0.507629476682699,0.3469725689956081,0.4556114877404176,0.401504819120054,0.617689617530909,0.12914709845456196,0.3653810197119157,0.4487028103262184,0.2609082130751905,0.6994439310340633
-0,LOF,0.11678669465120142,0.1206834698967151,0.08219767855332698,0.15122846324983763,0.04493994647519603,0.002208833983716011,0.20324070530989097,0.18079700554505512,0.129509098249193,0.11206072684081272,0.14191760711722487,0.05162771249827228,0.0489848149626439,0.0862447643103002,0.36356090912215866,0.0715421233322276,0.1448442788333725,0.2451059410575724,0.1679948274250915,0.15339581045465292,0.10081987259876368,0.18235312998281822,0.4099090939695364
-0,LSTMAD,0.3201945495278017,0.7376946947277406,0.030497343412870064,0.23744155439224032,0.12987864790676196,0.04585981775047413,0.27673530608030433,0.2788270775280182,0.1322337577607795,0.5192324559661586,0.20949249626949865,0.11306591669413261,0.028041338054278185,0.0647879297198734,0.6674208880035603,0.3580709697520762,0.1533056834456119,0.733874544363356,0.9933974763498682,0.5455348153510139,0.20567074108559213,0.5398904963082672,0.4439573166657559
-0,OmniAnomaly,0.36816289562529086,0.7576872065528726,0.031663480582993765,0.39428271688844074,0.4768352264302811,0.015268247817355054,0.4797110125580823,0.38508194752955704,0.1920691814960085,0.13364632394088924,0.251057174157317,0.20699912890412075,0.08523551844806315,0.0646648482156372,0.8361454306772048,0.4378677976960084,0.3322271540944519,0.8745189065767695,0.344060221602637,0.6498671005600911,0.17236897672776852,0.5311401685436009,0.1481883731109026
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diff --git a/docs/evaluation/groupedDataframes/VUS-PR_combined_by_group.csv b/docs/evaluation/groupedDataframes/VUS-PR_combined_by_group.csv
index 4bc325d198cbaa4ccf184c9ff7b250bc98a207d1..9b212a4c8e2d48ee75e87fb940cde68df8292432 100644
--- a/docs/evaluation/groupedDataframes/VUS-PR_combined_by_group.csv
+++ b/docs/evaluation/groupedDataframes/VUS-PR_combined_by_group.csv
@@ -1,12 +1,14 @@
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+0,LSTMAD,0.30512791043157395,0.9950444582863889,0.8179595058481393,0.669824054008361,0.11239751245844268,0.4174161081456283,0.5637720546144361,0.273213536450389,0.17373197532380166,0.02541916388299069,0.023292741312739982,0.5222277602405662,0.1330800298859931,0.32982840878783637,0.12748852543364644,0.735253231133451,0.6896972412727194,0.13026969854826323,0.0672501040011019,0.48394405976309657,0.056562170107036035,0.2130441268776781,0.3290558627970383
+0,OCSVM,0.27692696600880706,0.7204415330029587,0.7173772009864774,0.1599810140732196,0.35113283798670825,0.30693205830151654,0.1489143952562089,0.5656177731239515,0.37663811821971843,0.007014053543410155,0.336356149911562,0.7481473031631273,0.0701389976338681,0.6924703331629415,0.5924675292497075,0.5578912704843039,0.3672605028423727,0.03999904262889158,0.2253927416249083,0.11590785827605281,0.16491010075541357,0.0676715989156669,0.3113843528453291
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diff --git a/src/group_evaluation/create_combined_dataframe_with_metrics_and_plot.ipynb b/src/group_evaluation/create_combined_dataframe_with_metrics_and_plot.ipynb
index 6b3894edcd679026801cf3ed6f809e4eb4914211..9b871ce6a3ad5194c46c1c27a1dd20c236a9f498 100644
--- a/src/group_evaluation/create_combined_dataframe_with_metrics_and_plot.ipynb
+++ b/src/group_evaluation/create_combined_dataframe_with_metrics_and_plot.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
@@ -13,7 +13,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 2,
"metadata": {},
"outputs": [
{
@@ -24,11 +24,14 @@
"Not correct file type: groupedDataframes\n",
"Found: IForest.csv\n",
"Found: KMeansAD_U.csv\n",
+ "Found: KShapeAD.csv\n",
"Found: LOF.csv\n",
"Found: LSTMAD.csv\n",
+ "Found: OCSVM.csv\n",
"Found: OmniAnomaly.csv\n",
"Found: PCA.csv\n",
"Found: POLY.csv\n",
+ "Not correct file type: Ranking der Modelle im Vergleich zu den Autoren.md\n",
"Found: Sub-LOF.csv\n",
"Found: Sub_KNN.csv\n",
"Not correct file type: temp\n",
@@ -63,7 +66,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 3,
"metadata": {},
"outputs": [
{
@@ -254,7 +257,7 @@
"4 0.232558 CNN "
]
},
- "execution_count": 4,
+ "execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
@@ -265,16 +268,16 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "(4367, 18)"
+ "(5161, 18)"
]
},
- "execution_count": 5,
+ "execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
@@ -285,7 +288,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
@@ -295,8 +298,10 @@
"CNN 397\n",
"IForest 397\n",
"KMeansAD_U 397\n",
+ "KShapeAD 397\n",
"LOF 397\n",
"LSTMAD 397\n",
+ "OCSVM 397\n",
"OmniAnomaly 397\n",
"PCA 397\n",
"POLY 397\n",
@@ -306,7 +311,7 @@
"Name: count, dtype: int64"
]
},
- "execution_count": 6,
+ "execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
@@ -317,7 +322,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
@@ -340,7 +345,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 24,
"metadata": {},
"outputs": [
{
@@ -362,7358 +367,7358 @@
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+ "hovertemplate": "Model=%{x}<br>AUC-PR=%{y}<extra></extra>",
+ "legendgroup": "KMeansAD_U",
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+ "color": "darkseagreen"
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- "name": "Sub-LOF",
+ "name": "KMeansAD_U",
"notched": false,
- "offsetgroup": "Sub-LOF",
+ "offsetgroup": "KMeansAD_U",
"orientation": "v",
"showlegend": true,
"type": "box",
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],
"y0": " ",
"yaxis": "y"
@@ -9347,7 +10984,7 @@
"font": {
"color": "black",
"family": "Courier",
- "size": 12
+ "size": 14
},
"orientation": "h",
"title": {
@@ -9357,8 +10994,8 @@
"text": "Group"
},
"tracegroupgap": 0,
- "x": 0.1,
- "y": -0.1
+ "x": -0.05,
+ "y": -0.25
},
"margin": {
"t": 60
@@ -9926,7 +11563,7 @@
}
},
"title": {
- "text": "Vergleich der Performanz der Modelle anhand Recall",
+ "text": "Vergleich der Performanz der Modelle anhand AUC-PR",
"x": 0.5,
"xanchor": "center",
"y": 0.94,
@@ -9936,16 +11573,18 @@
"xaxis": {
"anchor": "y",
"categoryarray": [
- "Sub-LOF",
"KMeansAD_U",
- "Sub_KNN",
- "POLY",
"PCA",
+ "POLY",
+ "KShapeAD",
+ "Sub-LOF",
"CNN",
"OmniAnomaly",
"USAD",
"LSTMAD",
"IForest",
+ "Sub_KNN",
+ "OCSVM",
"LOF"
],
"categoryorder": "array",
@@ -9954,7 +11593,7 @@
1
],
"dtick": 1,
- "tickangle": 0,
+ "tickangle": -60,
"title": {
"text": "Model"
}
@@ -9966,7 +11605,7 @@
1
],
"title": {
- "text": "Recall"
+ "text": "AUC-PR"
}
}
}
@@ -9977,19 +11616,19 @@
}
],
"source": [
- "metric = 'Recall'#'VUS-PR'\n",
+ "metric = 'AUC-PR'#'Recall'#\n",
"#sort by average values\n",
"mean_sorted = df.groupby(\"Model\")[metric].mean().sort_values(ascending=False).index\n",
"\n",
"fig = px.box(df, x='Model', y=metric, color='Model', category_orders={'Model':mean_sorted}, color_discrete_map=colors)\n",
"fig.update_layout( legend=dict(\n",
- " x=0.1,\n",
- " y=-0.1,\n",
+ " x=-0.05,\n",
+ " y=-0.25,\n",
" orientation=\"h\",\n",
" title_font_family=\"Times New Roman\",\n",
" font=dict(\n",
" family=\"Courier\",\n",
- " size=12,\n",
+ " size=14,\n",
" color=\"black\"\n",
" ),\n",
" title_text='Group',\n",
@@ -10012,13 +11651,13 @@
" color='DarkSlateGrey'),\n",
" ),\n",
" selector=dict(mode='markers'))\n",
- "fig.update_xaxes( dtick = 1,tickangle = 360)\n",
+ "fig.update_xaxes( dtick = 1,tickangle = 300)\n",
"fig.show()"
]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 20,
"metadata": {},
"outputs": [
{
@@ -10194,6 +11833,32 @@
],
"yaxis": "y"
},
+ {
+ "alignmentgroup": "True",
+ "hovertemplate": "Model=%{x}<br>duration=%{y}<extra></extra>",
+ "legendgroup": "KShapeAD",
+ "marker": {
+ "color": "darksalmon",
+ "opacity": 0.8,
+ "pattern": {
+ "shape": ""
+ }
+ },
+ "name": "KShapeAD",
+ "offsetgroup": "KShapeAD",
+ "orientation": "v",
+ "showlegend": true,
+ "textposition": "auto",
+ "type": "bar",
+ "x": [
+ "KShapeAD"
+ ],
+ "xaxis": "x",
+ "y": [
+ 32.13738634904626
+ ],
+ "yaxis": "y"
+ },
{
"alignmentgroup": "True",
"hovertemplate": "Model=%{x}<br>duration=%{y}<extra></extra>",
@@ -10323,6 +11988,32 @@
60.541785299027296
],
"yaxis": "y"
+ },
+ {
+ "alignmentgroup": "True",
+ "hovertemplate": "Model=%{x}<br>duration=%{y}<extra></extra>",
+ "legendgroup": "OCSVM",
+ "marker": {
+ "color": "darkorange",
+ "opacity": 0.8,
+ "pattern": {
+ "shape": ""
+ }
+ },
+ "name": "OCSVM",
+ "offsetgroup": "OCSVM",
+ "orientation": "v",
+ "showlegend": true,
+ "textposition": "auto",
+ "type": "bar",
+ "x": [
+ "OCSVM"
+ ],
+ "xaxis": "x",
+ "y": [
+ 104.21769456779326
+ ],
+ "yaxis": "y"
}
],
"layout": {
@@ -10335,7 +12026,7 @@
"font": {
"color": "black",
"family": "Courier",
- "size": 12
+ "size": 14
},
"orientation": "h",
"title": {
@@ -10345,8 +12036,8 @@
"text": "Group"
},
"tracegroupgap": 0,
- "x": 0.02,
- "y": -0.1
+ "x": -0.05,
+ "y": -0.25
},
"margin": {
"t": 60
@@ -10930,11 +12621,13 @@
"POLY",
"OmniAnomaly",
"IForest",
+ "KShapeAD",
"LSTMAD",
"PCA",
"Sub-LOF",
"KMeansAD_U",
- "Sub_KNN"
+ "Sub_KNN",
+ "OCSVM"
],
"categoryorder": "array",
"domain": [
@@ -10942,7 +12635,7 @@
1
],
"dtick": 1,
- "tickangle": 0,
+ "tickangle": -60,
"title": {
"text": "Model"
}
@@ -10972,13 +12665,13 @@
"\n",
"fig = px.bar(df_mean, x='Model', y=duration, color='Model', category_orders={'Model':mean_sorted}, color_discrete_map=colors,opacity=0.8)\n",
"fig.update_layout( legend=dict(\n",
- " x=0.02,\n",
- " y=-0.1,\n",
+ " x=-0.05,\n",
+ " y=-0.25,\n",
" orientation=\"h\",\n",
" title_font_family=\"Times New Roman\",\n",
" font=dict(\n",
" family=\"Courier\",\n",
- " size=12,\n",
+ " size=14,\n",
" color=\"black\"\n",
" ),\n",
" title_text='Group',\n",
@@ -11001,7 +12694,7 @@
" color='DarkSlateGrey'),\n",
" ),\n",
" selector=dict(mode='markers'))\n",
- "fig.update_xaxes( dtick = 1,tickangle = 360)\n",
+ "fig.update_xaxes( dtick = 1,tickangle = 300)\n",
"fig.update_yaxes( title='Durchschnittliche Laufzeit')\n",
"fig.show()"
]
diff --git a/src/group_evaluation/create_heatmap.ipynb b/src/group_evaluation/create_heatmap.ipynb
index 6b470f6e258502f987cbc38d5bacf62e5de5bd9e..c56f43886115aed01fb2af49b920e57b0056847c 100644
--- a/src/group_evaluation/create_heatmap.ipynb
+++ b/src/group_evaluation/create_heatmap.ipynb
@@ -12,7 +12,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
@@ -36,27 +36,27 @@
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
- " <th>IOPS</th>\n",
- " <th>SWaT</th>\n",
- " <th>SED</th>\n",
" <th>MSL</th>\n",
- " <th>SVDB</th>\n",
- " <th>MGAB</th>\n",
- " <th>LTDB</th>\n",
- " <th>SMAP</th>\n",
- " <th>Daphnet</th>\n",
+ " <th>TAO</th>\n",
" <th>Stock</th>\n",
- " <th>...</th>\n",
- " <th>Power</th>\n",
- " <th>NEK</th>\n",
- " <th>CATSv2</th>\n",
+ " <th>SWaT</th>\n",
+ " <th>MITDB</th>\n",
+ " <th>YAHOO</th>\n",
+ " <th>OPPORTUNITY</th>\n",
+ " <th>SMAP</th>\n",
" <th>NAB</th>\n",
+ " <th>MGAB</th>\n",
+ " <th>...</th>\n",
+ " <th>LTDB</th>\n",
+ " <th>SVDB</th>\n",
" <th>Exathlon</th>\n",
- " <th>TAO</th>\n",
- " <th>OPPORTUNITY</th>\n",
- " <th>TODS</th>\n",
+ " <th>NEK</th>\n",
+ " <th>WSD</th>\n",
+ " <th>Power</th>\n",
" <th>SMD</th>\n",
- " <th>YAHOO</th>\n",
+ " <th>SED</th>\n",
+ " <th>IOPS</th>\n",
+ " <th>CATSv2</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Model</th>\n",
@@ -86,123 +86,123 @@
" <tbody>\n",
" <tr>\n",
" <th>CNN</th>\n",
- " <td>0.205826</td>\n",
- " <td>0.680748</td>\n",
- " <td>0.058416</td>\n",
- " <td>0.346350</td>\n",
- " <td>0.199785</td>\n",
- " <td>0.006988</td>\n",
- " <td>0.406171</td>\n",
- " <td>0.336818</td>\n",
- " <td>0.398456</td>\n",
- " <td>0.939925</td>\n",
+ " <td>0.307033</td>\n",
+ " <td>0.994857</td>\n",
+ " <td>0.929344</td>\n",
+ " <td>0.738718</td>\n",
+ " <td>0.160153</td>\n",
+ " <td>0.561562</td>\n",
+ " <td>0.619160</td>\n",
+ " <td>0.329931</td>\n",
+ " <td>0.223347</td>\n",
+ " <td>0.003855</td>\n",
" <td>...</td>\n",
- " <td>0.075487</td>\n",
- " <td>0.714845</td>\n",
- " <td>0.288293</td>\n",
- " <td>0.248091</td>\n",
- " <td>0.657757</td>\n",
- " <td>0.994270</td>\n",
- " <td>0.632799</td>\n",
- " <td>0.575941</td>\n",
- " <td>0.545045</td>\n",
- " <td>0.512096</td>\n",
+ " <td>0.354462</td>\n",
+ " <td>0.197103</td>\n",
+ " <td>0.654310</td>\n",
+ " <td>0.685573</td>\n",
+ " <td>0.303128</td>\n",
+ " <td>0.072400</td>\n",
+ " <td>0.565370</td>\n",
+ " <td>0.029325</td>\n",
+ " <td>0.265889</td>\n",
+ " <td>0.411894</td>\n",
" </tr>\n",
" <tr>\n",
" <th>IForest</th>\n",
- " <td>0.134518</td>\n",
- " <td>0.368230</td>\n",
- " <td>0.807209</td>\n",
- " <td>0.448709</td>\n",
- " <td>0.307322</td>\n",
- " <td>0.004379</td>\n",
- " <td>0.566359</td>\n",
- " <td>0.327727</td>\n",
- " <td>0.518905</td>\n",
- " <td>0.735886</td>\n",
+ " <td>0.386085</td>\n",
+ " <td>0.125817</td>\n",
+ " <td>0.143940</td>\n",
+ " <td>0.702432</td>\n",
+ " <td>0.169557</td>\n",
+ " <td>0.092730</td>\n",
+ " <td>0.733153</td>\n",
+ " <td>0.299302</td>\n",
+ " <td>0.314587</td>\n",
+ " <td>0.004336</td>\n",
" <td>...</td>\n",
- " <td>0.076373</td>\n",
- " <td>0.649735</td>\n",
- " <td>0.105175</td>\n",
- " <td>0.342137</td>\n",
- " <td>0.964836</td>\n",
- " <td>0.728970</td>\n",
- " <td>0.736264</td>\n",
- " <td>0.487386</td>\n",
- " <td>0.342406</td>\n",
- " <td>0.135664</td>\n",
+ " <td>0.521341</td>\n",
+ " <td>0.311936</td>\n",
+ " <td>0.962806</td>\n",
+ " <td>0.630339</td>\n",
+ " <td>0.040993</td>\n",
+ " <td>0.073981</td>\n",
+ " <td>0.369237</td>\n",
+ " <td>0.691484</td>\n",
+ " <td>0.231569</td>\n",
+ " <td>0.139416</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KMeansAD_U</th>\n",
- " <td>0.107988</td>\n",
- " <td>0.111519</td>\n",
- " <td>0.895291</td>\n",
- " <td>0.441543</td>\n",
- " <td>0.811385</td>\n",
- " <td>0.036645</td>\n",
- " <td>0.835553</td>\n",
- " <td>0.657738</td>\n",
- " <td>0.039747</td>\n",
- " <td>0.738198</td>\n",
+ " <td>0.371438</td>\n",
+ " <td>0.129147</td>\n",
+ " <td>0.114476</td>\n",
+ " <td>0.111152</td>\n",
+ " <td>0.583808</td>\n",
+ " <td>0.699444</td>\n",
+ " <td>0.365381</td>\n",
+ " <td>0.648913</td>\n",
+ " <td>0.401505</td>\n",
+ " <td>0.085299</td>\n",
" <td>...</td>\n",
- " <td>0.524166</td>\n",
- " <td>0.317927</td>\n",
- " <td>0.229268</td>\n",
- " <td>0.427477</td>\n",
- " <td>0.618051</td>\n",
- " <td>0.747945</td>\n",
- " <td>0.351828</td>\n",
- " <td>0.739257</td>\n",
- " <td>0.254537</td>\n",
- " <td>0.781824</td>\n",
+ " <td>0.765819</td>\n",
+ " <td>0.809036</td>\n",
+ " <td>0.617690</td>\n",
+ " <td>0.346973</td>\n",
+ " <td>0.146372</td>\n",
+ " <td>0.507629</td>\n",
+ " <td>0.260908</td>\n",
+ " <td>0.710575</td>\n",
+ " <td>0.068715</td>\n",
+ " <td>0.455611</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>LOF</th>\n",
- " <td>0.123657</td>\n",
- " <td>0.121168</td>\n",
- " <td>0.111105</td>\n",
- " <td>0.186700</td>\n",
- " <td>0.050189</td>\n",
- " <td>0.004639</td>\n",
- " <td>0.256447</td>\n",
- " <td>0.172499</td>\n",
- " <td>0.134804</td>\n",
- " <td>0.710487</td>\n",
+ " <th>KShapeAD</th>\n",
+ " <td>0.409835</td>\n",
+ " <td>0.103834</td>\n",
+ " <td>0.095333</td>\n",
+ " <td>0.458896</td>\n",
+ " <td>0.594424</td>\n",
+ " <td>0.435473</td>\n",
+ " <td>0.366866</td>\n",
+ " <td>0.601232</td>\n",
+ " <td>0.396635</td>\n",
+ " <td>0.084411</td>\n",
" <td>...</td>\n",
- " <td>0.090503</td>\n",
- " <td>0.395130</td>\n",
- " <td>0.063826</td>\n",
- " <td>0.165966</td>\n",
- " <td>0.240338</td>\n",
- " <td>0.710584</td>\n",
- " <td>0.159200</td>\n",
- " <td>0.485079</td>\n",
- " <td>0.134985</td>\n",
- " <td>0.381508</td>\n",
+ " <td>0.743642</td>\n",
+ " <td>0.798760</td>\n",
+ " <td>0.420990</td>\n",
+ " <td>0.379153</td>\n",
+ " <td>0.082639</td>\n",
+ " <td>0.496250</td>\n",
+ " <td>0.131250</td>\n",
+ " <td>0.722020</td>\n",
+ " <td>0.068365</td>\n",
+ " <td>0.452377</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>LSTMAD</th>\n",
- " <td>0.213044</td>\n",
- " <td>0.669824</td>\n",
- " <td>0.056562</td>\n",
- " <td>0.305128</td>\n",
- " <td>0.127489</td>\n",
- " <td>0.025419</td>\n",
- " <td>0.329828</td>\n",
- " <td>0.273214</td>\n",
- " <td>0.133080</td>\n",
- " <td>0.817960</td>\n",
+ " <th>LOF</th>\n",
+ " <td>0.151228</td>\n",
+ " <td>0.167995</td>\n",
+ " <td>0.112061</td>\n",
+ " <td>0.120683</td>\n",
+ " <td>0.051628</td>\n",
+ " <td>0.409909</td>\n",
+ " <td>0.153396</td>\n",
+ " <td>0.180797</td>\n",
+ " <td>0.144844</td>\n",
+ " <td>0.002209</td>\n",
" <td>...</td>\n",
- " <td>0.067250</td>\n",
- " <td>0.689697</td>\n",
- " <td>0.329056</td>\n",
- " <td>0.173732</td>\n",
- " <td>0.735253</td>\n",
- " <td>0.995044</td>\n",
- " <td>0.563772</td>\n",
- " <td>0.522228</td>\n",
- " <td>0.483944</td>\n",
- " <td>0.417416</td>\n",
+ " <td>0.203241</td>\n",
+ " <td>0.044940</td>\n",
+ " <td>0.245106</td>\n",
+ " <td>0.363561</td>\n",
+ " <td>0.141918</td>\n",
+ " <td>0.086245</td>\n",
+ " <td>0.182353</td>\n",
+ " <td>0.082198</td>\n",
+ " <td>0.116787</td>\n",
+ " <td>0.071542</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@@ -210,49 +210,49 @@
"</div>"
],
"text/plain": [
- " IOPS SWaT SED MSL SVDB MGAB \\\n",
+ " MSL TAO Stock SWaT MITDB YAHOO \\\n",
"Model \n",
- "CNN 0.205826 0.680748 0.058416 0.346350 0.199785 0.006988 \n",
- "IForest 0.134518 0.368230 0.807209 0.448709 0.307322 0.004379 \n",
- "KMeansAD_U 0.107988 0.111519 0.895291 0.441543 0.811385 0.036645 \n",
- "LOF 0.123657 0.121168 0.111105 0.186700 0.050189 0.004639 \n",
- "LSTMAD 0.213044 0.669824 0.056562 0.305128 0.127489 0.025419 \n",
+ "CNN 0.307033 0.994857 0.929344 0.738718 0.160153 0.561562 \n",
+ "IForest 0.386085 0.125817 0.143940 0.702432 0.169557 0.092730 \n",
+ "KMeansAD_U 0.371438 0.129147 0.114476 0.111152 0.583808 0.699444 \n",
+ "KShapeAD 0.409835 0.103834 0.095333 0.458896 0.594424 0.435473 \n",
+ "LOF 0.151228 0.167995 0.112061 0.120683 0.051628 0.409909 \n",
"\n",
- " LTDB SMAP Daphnet Stock ... Power NEK \\\n",
- "Model ... \n",
- "CNN 0.406171 0.336818 0.398456 0.939925 ... 0.075487 0.714845 \n",
- "IForest 0.566359 0.327727 0.518905 0.735886 ... 0.076373 0.649735 \n",
- "KMeansAD_U 0.835553 0.657738 0.039747 0.738198 ... 0.524166 0.317927 \n",
- "LOF 0.256447 0.172499 0.134804 0.710487 ... 0.090503 0.395130 \n",
- "LSTMAD 0.329828 0.273214 0.133080 0.817960 ... 0.067250 0.689697 \n",
+ " OPPORTUNITY SMAP NAB MGAB ... LTDB \\\n",
+ "Model ... \n",
+ "CNN 0.619160 0.329931 0.223347 0.003855 ... 0.354462 \n",
+ "IForest 0.733153 0.299302 0.314587 0.004336 ... 0.521341 \n",
+ "KMeansAD_U 0.365381 0.648913 0.401505 0.085299 ... 0.765819 \n",
+ "KShapeAD 0.366866 0.601232 0.396635 0.084411 ... 0.743642 \n",
+ "LOF 0.153396 0.180797 0.144844 0.002209 ... 0.203241 \n",
"\n",
- " CATSv2 NAB Exathlon TAO OPPORTUNITY TODS \\\n",
- "Model \n",
- "CNN 0.288293 0.248091 0.657757 0.994270 0.632799 0.575941 \n",
- "IForest 0.105175 0.342137 0.964836 0.728970 0.736264 0.487386 \n",
- "KMeansAD_U 0.229268 0.427477 0.618051 0.747945 0.351828 0.739257 \n",
- "LOF 0.063826 0.165966 0.240338 0.710584 0.159200 0.485079 \n",
- "LSTMAD 0.329056 0.173732 0.735253 0.995044 0.563772 0.522228 \n",
+ " SVDB Exathlon NEK WSD Power SMD \\\n",
+ "Model \n",
+ "CNN 0.197103 0.654310 0.685573 0.303128 0.072400 0.565370 \n",
+ "IForest 0.311936 0.962806 0.630339 0.040993 0.073981 0.369237 \n",
+ "KMeansAD_U 0.809036 0.617690 0.346973 0.146372 0.507629 0.260908 \n",
+ "KShapeAD 0.798760 0.420990 0.379153 0.082639 0.496250 0.131250 \n",
+ "LOF 0.044940 0.245106 0.363561 0.141918 0.086245 0.182353 \n",
"\n",
- " SMD YAHOO \n",
- "Model \n",
- "CNN 0.545045 0.512096 \n",
- "IForest 0.342406 0.135664 \n",
- "KMeansAD_U 0.254537 0.781824 \n",
- "LOF 0.134985 0.381508 \n",
- "LSTMAD 0.483944 0.417416 \n",
+ " SED IOPS CATSv2 \n",
+ "Model \n",
+ "CNN 0.029325 0.265889 0.411894 \n",
+ "IForest 0.691484 0.231569 0.139416 \n",
+ "KMeansAD_U 0.710575 0.068715 0.455611 \n",
+ "KShapeAD 0.722020 0.068365 0.452377 \n",
+ "LOF 0.082198 0.116787 0.071542 \n",
"\n",
"[5 rows x 23 columns]"
]
},
- "execution_count": 10,
+ "execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"path = '../../docs/evaluation/groupedDataframes/'\n",
- "metric = 'VUS-PR'\n",
+ "metric = 'AUC-PR'\n",
"file = metric +'_combined_by_group.csv'\n",
"file_path = path + file\n",
"df = pd.read_csv(file_path, index_col='Model')\n",
@@ -262,7 +262,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
@@ -313,36 +313,38 @@
],
"type": "heatmap",
"x": [
- "IOPS",
- "SWaT",
- "SED",
"MSL",
- "SVDB",
- "MGAB",
- "LTDB",
- "SMAP",
- "Daphnet",
+ "TAO",
"Stock",
- "WSD",
+ "SWaT",
"MITDB",
- "UCR",
- "Power",
- "NEK",
- "CATSv2",
- "NAB",
- "Exathlon",
- "TAO",
+ "YAHOO",
"OPPORTUNITY",
+ "SMAP",
+ "NAB",
+ "MGAB",
+ "UCR",
"TODS",
+ "Daphnet",
+ "LTDB",
+ "SVDB",
+ "Exathlon",
+ "NEK",
+ "WSD",
+ "Power",
"SMD",
- "YAHOO"
+ "SED",
+ "IOPS",
+ "CATSv2"
],
"y": [
"CNN",
"IForest",
"KMeansAD_U",
+ "KShapeAD",
"LOF",
"LSTMAD",
+ "OCSVM",
"OmniAnomaly",
"PCA",
"POLY",
@@ -352,279 +354,329 @@
],
"z": [
[
- 0.2058264075504484,
- 0.6807482076261672,
- 0.0584161141736485,
- 0.3463500458820062,
- 0.1997846992126922,
- 0.0069880026447859,
- 0.4061714307248641,
- 0.3368175788334636,
- 0.3984561332333746,
- 0.9399247021670544,
- 0.2249216197293689,
- 0.1436318602643481,
- 0.0654427561172705,
- 0.0754866098747693,
- 0.7148450524753189,
- 0.2882929464081713,
- 0.2480906432796945,
- 0.6577570780079647,
- 0.9942696053962238,
- 0.6327991152012451,
- 0.575941184620618,
- 0.5450449067296064,
- 0.5120964005826543
+ 0.3070329648556795,
+ 0.9948566473921368,
+ 0.9293442608636604,
+ 0.7387181088941073,
+ 0.1601528087440646,
+ 0.5615618691240216,
+ 0.6191602168303323,
+ 0.3299309288297937,
+ 0.2233468551717377,
+ 0.0038549618121316,
+ 0.069477033023667,
+ 0.3037856723254471,
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}
@@ -1447,7 +1499,7 @@
}
},
"title": {
- "text": "Oranges Heatmap der VUS-PR von den einzelnen Modelle auf den jeweiligen Datensätzen"
+ "text": "Oranges Heatmap der AUC-PR von den einzelnen Modelle auf den jeweiligen Datensätzen"
},
"xaxis": {
"title": {
diff --git a/src/group_evaluation/generate_combined_dataframe.ipynb b/src/group_evaluation/generate_combined_dataframe.ipynb
index 7e41157e859cc470122ec7c14afb287a169d1a11..7c63dbab31648433158f9edf698c898c8c699a6f 100644
--- a/src/group_evaluation/generate_combined_dataframe.ipynb
+++ b/src/group_evaluation/generate_combined_dataframe.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
@@ -12,7 +12,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
@@ -25,7 +25,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
@@ -50,26 +50,26 @@
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Model</th>\n",
- " <th>IOPS</th>\n",
- " <th>SWaT</th>\n",
- " <th>SED</th>\n",
" <th>MSL</th>\n",
- " <th>SVDB</th>\n",
- " <th>MGAB</th>\n",
- " <th>LTDB</th>\n",
+ " <th>TAO</th>\n",
+ " <th>Stock</th>\n",
+ " <th>SWaT</th>\n",
+ " <th>MITDB</th>\n",
+ " <th>YAHOO</th>\n",
+ " <th>OPPORTUNITY</th>\n",
" <th>SMAP</th>\n",
- " <th>Daphnet</th>\n",
- " <th>...</th>\n",
- " <th>Power</th>\n",
- " <th>NEK</th>\n",
- " <th>CATSv2</th>\n",
" <th>NAB</th>\n",
+ " <th>...</th>\n",
+ " <th>LTDB</th>\n",
+ " <th>SVDB</th>\n",
" <th>Exathlon</th>\n",
- " <th>TAO</th>\n",
- " <th>OPPORTUNITY</th>\n",
- " <th>TODS</th>\n",
+ " <th>NEK</th>\n",
+ " <th>WSD</th>\n",
+ " <th>Power</th>\n",
" <th>SMD</th>\n",
- " <th>YAHOO</th>\n",
+ " <th>SED</th>\n",
+ " <th>IOPS</th>\n",
+ " <th>CATSv2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
@@ -80,13 +80,13 @@
],
"text/plain": [
"Empty DataFrame\n",
- "Columns: [Model, IOPS, SWaT, SED, MSL, SVDB, MGAB, LTDB, SMAP, Daphnet, Stock, WSD, MITDB, UCR, Power, NEK, CATSv2, NAB, Exathlon, TAO, OPPORTUNITY, TODS, SMD, YAHOO]\n",
+ "Columns: [Model, MSL, TAO, Stock, SWaT, MITDB, YAHOO, OPPORTUNITY, SMAP, NAB, MGAB, UCR, TODS, Daphnet, LTDB, SVDB, Exathlon, NEK, WSD, Power, SMD, SED, IOPS, CATSv2]\n",
"Index: []\n",
"\n",
"[0 rows x 24 columns]"
]
},
- "execution_count": 8,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
@@ -101,7 +101,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
@@ -112,11 +112,14 @@
"Not correct file type: groupedDataframes\n",
"Found: IForest.csv\n",
"Found: KMeansAD_U.csv\n",
+ "Found: KShapeAD.csv\n",
"Found: LOF.csv\n",
"Found: LSTMAD.csv\n",
+ "Found: OCSVM.csv\n",
"Found: OmniAnomaly.csv\n",
"Found: PCA.csv\n",
"Found: POLY.csv\n",
+ "Not correct file type: Ranking der Modelle im Vergleich zu den Autoren.md\n",
"Found: Sub-LOF.csv\n",
"Found: Sub_KNN.csv\n",
"Not correct file type: temp\n",
@@ -126,7 +129,7 @@
}
],
"source": [
- "col = 'VUS-PR'\n",
+ "col = 'AUC-PR'\n",
"\n",
"for file in os.listdir(path):\n",
" suffix = file.split('.')\n",
@@ -158,7 +161,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
@@ -183,148 +186,148 @@
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Model</th>\n",
- " <th>IOPS</th>\n",
- " <th>SWaT</th>\n",
- " <th>SED</th>\n",
" <th>MSL</th>\n",
- " <th>SVDB</th>\n",
- " <th>MGAB</th>\n",
- " <th>LTDB</th>\n",
+ " <th>TAO</th>\n",
+ " <th>Stock</th>\n",
+ " <th>SWaT</th>\n",
+ " <th>MITDB</th>\n",
+ " <th>YAHOO</th>\n",
+ " <th>OPPORTUNITY</th>\n",
" <th>SMAP</th>\n",
- " <th>Daphnet</th>\n",
- " <th>...</th>\n",
- " <th>Power</th>\n",
- " <th>NEK</th>\n",
- " <th>CATSv2</th>\n",
" <th>NAB</th>\n",
+ " <th>...</th>\n",
+ " <th>LTDB</th>\n",
+ " <th>SVDB</th>\n",
" <th>Exathlon</th>\n",
- " <th>TAO</th>\n",
- " <th>OPPORTUNITY</th>\n",
- " <th>TODS</th>\n",
+ " <th>NEK</th>\n",
+ " <th>WSD</th>\n",
+ " <th>Power</th>\n",
" <th>SMD</th>\n",
- " <th>YAHOO</th>\n",
+ " <th>SED</th>\n",
+ " <th>IOPS</th>\n",
+ " <th>CATSv2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>CNN</td>\n",
- " <td>0.205826</td>\n",
- " <td>0.680748</td>\n",
- " <td>0.058416</td>\n",
- " <td>0.346350</td>\n",
- " <td>0.199785</td>\n",
- " <td>0.006988</td>\n",
- " <td>0.406171</td>\n",
- " <td>0.336818</td>\n",
- " <td>0.398456</td>\n",
+ " <td>0.307033</td>\n",
+ " <td>0.994857</td>\n",
+ " <td>0.929344</td>\n",
+ " <td>0.738718</td>\n",
+ " <td>0.160153</td>\n",
+ " <td>0.561562</td>\n",
+ " <td>0.619160</td>\n",
+ " <td>0.329931</td>\n",
+ " <td>0.223347</td>\n",
" <td>...</td>\n",
- " <td>0.075487</td>\n",
- " <td>0.714845</td>\n",
- " <td>0.288293</td>\n",
- " <td>0.248091</td>\n",
- " <td>0.657757</td>\n",
- " <td>0.994270</td>\n",
- " <td>0.632799</td>\n",
- " <td>0.575941</td>\n",
- " <td>0.545045</td>\n",
- " <td>0.512096</td>\n",
+ " <td>0.354462</td>\n",
+ " <td>0.197103</td>\n",
+ " <td>0.654310</td>\n",
+ " <td>0.685573</td>\n",
+ " <td>0.303128</td>\n",
+ " <td>0.072400</td>\n",
+ " <td>0.565370</td>\n",
+ " <td>0.029325</td>\n",
+ " <td>0.265889</td>\n",
+ " <td>0.411894</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>IForest</td>\n",
- " <td>0.134518</td>\n",
- " <td>0.368230</td>\n",
- " <td>0.807209</td>\n",
- " <td>0.448709</td>\n",
- " <td>0.307322</td>\n",
- " <td>0.004379</td>\n",
- " <td>0.566359</td>\n",
- " <td>0.327727</td>\n",
- " <td>0.518905</td>\n",
+ " <td>0.386085</td>\n",
+ " <td>0.125817</td>\n",
+ " <td>0.143940</td>\n",
+ " <td>0.702432</td>\n",
+ " <td>0.169557</td>\n",
+ " <td>0.092730</td>\n",
+ " <td>0.733153</td>\n",
+ " <td>0.299302</td>\n",
+ " <td>0.314587</td>\n",
" <td>...</td>\n",
- " <td>0.076373</td>\n",
- " <td>0.649735</td>\n",
- " <td>0.105175</td>\n",
- " <td>0.342137</td>\n",
- " <td>0.964836</td>\n",
- " <td>0.728970</td>\n",
- " <td>0.736264</td>\n",
- " <td>0.487386</td>\n",
- " <td>0.342406</td>\n",
- " <td>0.135664</td>\n",
+ " <td>0.521341</td>\n",
+ " <td>0.311936</td>\n",
+ " <td>0.962806</td>\n",
+ " <td>0.630339</td>\n",
+ " <td>0.040993</td>\n",
+ " <td>0.073981</td>\n",
+ " <td>0.369237</td>\n",
+ " <td>0.691484</td>\n",
+ " <td>0.231569</td>\n",
+ " <td>0.139416</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>KMeansAD_U</td>\n",
- " <td>0.107988</td>\n",
- " <td>0.111519</td>\n",
- " <td>0.895291</td>\n",
- " <td>0.441543</td>\n",
- " <td>0.811385</td>\n",
- " <td>0.036645</td>\n",
- " <td>0.835553</td>\n",
- " <td>0.657738</td>\n",
- " <td>0.039747</td>\n",
+ " <td>0.371438</td>\n",
+ " <td>0.129147</td>\n",
+ " <td>0.114476</td>\n",
+ " <td>0.111152</td>\n",
+ " <td>0.583808</td>\n",
+ " <td>0.699444</td>\n",
+ " <td>0.365381</td>\n",
+ " <td>0.648913</td>\n",
+ " <td>0.401505</td>\n",
" <td>...</td>\n",
- " <td>0.524166</td>\n",
- " <td>0.317927</td>\n",
- " <td>0.229268</td>\n",
- " <td>0.427477</td>\n",
- " <td>0.618051</td>\n",
- " <td>0.747945</td>\n",
- " <td>0.351828</td>\n",
- " <td>0.739257</td>\n",
- " <td>0.254537</td>\n",
- " <td>0.781824</td>\n",
+ " <td>0.765819</td>\n",
+ " <td>0.809036</td>\n",
+ " <td>0.617690</td>\n",
+ " <td>0.346973</td>\n",
+ " <td>0.146372</td>\n",
+ " <td>0.507629</td>\n",
+ " <td>0.260908</td>\n",
+ " <td>0.710575</td>\n",
+ " <td>0.068715</td>\n",
+ " <td>0.455611</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
- " <td>LOF</td>\n",
- " <td>0.123657</td>\n",
- " <td>0.121168</td>\n",
- " <td>0.111105</td>\n",
- " <td>0.186700</td>\n",
- " <td>0.050189</td>\n",
- " <td>0.004639</td>\n",
- " <td>0.256447</td>\n",
- " <td>0.172499</td>\n",
- " <td>0.134804</td>\n",
+ " <td>KShapeAD</td>\n",
+ " <td>0.409835</td>\n",
+ " <td>0.103834</td>\n",
+ " <td>0.095333</td>\n",
+ " <td>0.458896</td>\n",
+ " <td>0.594424</td>\n",
+ " <td>0.435473</td>\n",
+ " <td>0.366866</td>\n",
+ " <td>0.601232</td>\n",
+ " <td>0.396635</td>\n",
" <td>...</td>\n",
- " <td>0.090503</td>\n",
- " <td>0.395130</td>\n",
- " <td>0.063826</td>\n",
- " <td>0.165966</td>\n",
- " <td>0.240338</td>\n",
- " <td>0.710584</td>\n",
- " <td>0.159200</td>\n",
- " <td>0.485079</td>\n",
- " <td>0.134985</td>\n",
- " <td>0.381508</td>\n",
+ " <td>0.743642</td>\n",
+ " <td>0.798760</td>\n",
+ " <td>0.420990</td>\n",
+ " <td>0.379153</td>\n",
+ " <td>0.082639</td>\n",
+ " <td>0.496250</td>\n",
+ " <td>0.131250</td>\n",
+ " <td>0.722020</td>\n",
+ " <td>0.068365</td>\n",
+ " <td>0.452377</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
- " <td>LSTMAD</td>\n",
- " <td>0.213044</td>\n",
- " <td>0.669824</td>\n",
- " <td>0.056562</td>\n",
- " <td>0.305128</td>\n",
- " <td>0.127489</td>\n",
- " <td>0.025419</td>\n",
- " <td>0.329828</td>\n",
- " <td>0.273214</td>\n",
- " <td>0.133080</td>\n",
+ " <td>LOF</td>\n",
+ " <td>0.151228</td>\n",
+ " <td>0.167995</td>\n",
+ " <td>0.112061</td>\n",
+ " <td>0.120683</td>\n",
+ " <td>0.051628</td>\n",
+ " <td>0.409909</td>\n",
+ " <td>0.153396</td>\n",
+ " <td>0.180797</td>\n",
+ " <td>0.144844</td>\n",
" <td>...</td>\n",
- " <td>0.067250</td>\n",
- " <td>0.689697</td>\n",
- " <td>0.329056</td>\n",
- " <td>0.173732</td>\n",
- " <td>0.735253</td>\n",
- " <td>0.995044</td>\n",
- " <td>0.563772</td>\n",
- " <td>0.522228</td>\n",
- " <td>0.483944</td>\n",
- " <td>0.417416</td>\n",
+ " <td>0.203241</td>\n",
+ " <td>0.044940</td>\n",
+ " <td>0.245106</td>\n",
+ " <td>0.363561</td>\n",
+ " <td>0.141918</td>\n",
+ " <td>0.086245</td>\n",
+ " <td>0.182353</td>\n",
+ " <td>0.082198</td>\n",
+ " <td>0.116787</td>\n",
+ " <td>0.071542</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@@ -332,31 +335,31 @@
"</div>"
],
"text/plain": [
- " Model IOPS SWaT SED MSL SVDB MGAB \\\n",
- "0 CNN 0.205826 0.680748 0.058416 0.346350 0.199785 0.006988 \n",
- "0 IForest 0.134518 0.368230 0.807209 0.448709 0.307322 0.004379 \n",
- "0 KMeansAD_U 0.107988 0.111519 0.895291 0.441543 0.811385 0.036645 \n",
- "0 LOF 0.123657 0.121168 0.111105 0.186700 0.050189 0.004639 \n",
- "0 LSTMAD 0.213044 0.669824 0.056562 0.305128 0.127489 0.025419 \n",
+ " Model MSL TAO Stock SWaT MITDB YAHOO \\\n",
+ "0 CNN 0.307033 0.994857 0.929344 0.738718 0.160153 0.561562 \n",
+ "0 IForest 0.386085 0.125817 0.143940 0.702432 0.169557 0.092730 \n",
+ "0 KMeansAD_U 0.371438 0.129147 0.114476 0.111152 0.583808 0.699444 \n",
+ "0 KShapeAD 0.409835 0.103834 0.095333 0.458896 0.594424 0.435473 \n",
+ "0 LOF 0.151228 0.167995 0.112061 0.120683 0.051628 0.409909 \n",
"\n",
- " LTDB SMAP Daphnet ... Power NEK CATSv2 NAB \\\n",
- "0 0.406171 0.336818 0.398456 ... 0.075487 0.714845 0.288293 0.248091 \n",
- "0 0.566359 0.327727 0.518905 ... 0.076373 0.649735 0.105175 0.342137 \n",
- "0 0.835553 0.657738 0.039747 ... 0.524166 0.317927 0.229268 0.427477 \n",
- "0 0.256447 0.172499 0.134804 ... 0.090503 0.395130 0.063826 0.165966 \n",
- "0 0.329828 0.273214 0.133080 ... 0.067250 0.689697 0.329056 0.173732 \n",
+ " OPPORTUNITY SMAP NAB ... LTDB SVDB Exathlon \\\n",
+ "0 0.619160 0.329931 0.223347 ... 0.354462 0.197103 0.654310 \n",
+ "0 0.733153 0.299302 0.314587 ... 0.521341 0.311936 0.962806 \n",
+ "0 0.365381 0.648913 0.401505 ... 0.765819 0.809036 0.617690 \n",
+ "0 0.366866 0.601232 0.396635 ... 0.743642 0.798760 0.420990 \n",
+ "0 0.153396 0.180797 0.144844 ... 0.203241 0.044940 0.245106 \n",
"\n",
- " Exathlon TAO OPPORTUNITY TODS SMD YAHOO \n",
- "0 0.657757 0.994270 0.632799 0.575941 0.545045 0.512096 \n",
- "0 0.964836 0.728970 0.736264 0.487386 0.342406 0.135664 \n",
- "0 0.618051 0.747945 0.351828 0.739257 0.254537 0.781824 \n",
- "0 0.240338 0.710584 0.159200 0.485079 0.134985 0.381508 \n",
- "0 0.735253 0.995044 0.563772 0.522228 0.483944 0.417416 \n",
+ " NEK WSD Power SMD SED IOPS CATSv2 \n",
+ "0 0.685573 0.303128 0.072400 0.565370 0.029325 0.265889 0.411894 \n",
+ "0 0.630339 0.040993 0.073981 0.369237 0.691484 0.231569 0.139416 \n",
+ "0 0.346973 0.146372 0.507629 0.260908 0.710575 0.068715 0.455611 \n",
+ "0 0.379153 0.082639 0.496250 0.131250 0.722020 0.068365 0.452377 \n",
+ "0 0.363561 0.141918 0.086245 0.182353 0.082198 0.116787 0.071542 \n",
"\n",
"[5 rows x 24 columns]"
]
},
- "execution_count": 10,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
@@ -367,7 +370,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
diff --git a/src/models/desi/desi_evaluate_groups.ipynb b/src/models/desi/desi_evaluate_groups.ipynb
index debcaf00067f35e5a889bd77702c8093e5229e30..7d3426493bd609334a2018b510ca48dcd2935b98 100644
--- a/src/models/desi/desi_evaluate_groups.ipynb
+++ b/src/models/desi/desi_evaluate_groups.ipynb
@@ -13,7 +13,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 31,
"metadata": {},
"outputs": [
{
@@ -59,146 +59,153 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
- " <td>{'periodicity': 2}</td>\n",
+ " <td>{'periodicity': 2, 'kernel': 'rbf'}</td>\n",
" <td>001_NAB_id_1_Facility_tr_1007_1st_2014.csv</td>\n",
- " <td>5.479764</td>\n",
+ " <td>1.670064</td>\n",
" <td>NAB</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
- " <td>0.171235</td>\n",
- " <td>0.622904</td>\n",
- " <td>0.156004</td>\n",
- " <td>0.610862</td>\n",
- " <td>0.223828</td>\n",
- " <td>0.871545</td>\n",
- " <td>0.679245</td>\n",
- " <td>0.246553</td>\n",
- " <td>0.719035</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
+ " <td>0.139487</td>\n",
+ " <td>0.549979</td>\n",
+ " <td>0.129207</td>\n",
+ " <td>0.554000</td>\n",
+ " <td>0.180031</td>\n",
+ " <td>0.988473</td>\n",
+ " <td>0.800000</td>\n",
+ " <td>0.299881</td>\n",
+ " <td>0.870591</td>\n",
+ " <td>0.03207</td>\n",
+ " <td>0.611111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
- " <td>{'periodicity': 2}</td>\n",
+ " <td>{'periodicity': 2, 'kernel': 'rbf'}</td>\n",
" <td>002_NAB_id_2_WebService_tr_1500_1st_4106.csv</td>\n",
- " <td>4.908555</td>\n",
+ " <td>3.762166</td>\n",
" <td>NAB</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
- " <td>0.127993</td>\n",
- " <td>0.542582</td>\n",
- " <td>0.131516</td>\n",
- " <td>0.559355</td>\n",
- " <td>0.203427</td>\n",
- " <td>0.996091</td>\n",
- " <td>0.369231</td>\n",
- " <td>0.400327</td>\n",
- " <td>0.734259</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
+ " <td>0.122769</td>\n",
+ " <td>0.531865</td>\n",
+ " <td>0.130022</td>\n",
+ " <td>0.557598</td>\n",
+ " <td>0.201978</td>\n",
+ " <td>0.216814</td>\n",
+ " <td>0.199095</td>\n",
+ " <td>0.233291</td>\n",
+ " <td>0.672758</td>\n",
+ " <td>0.00000</td>\n",
+ " <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
- " <td>{'periodicity': 3}</td>\n",
+ " <td>{'periodicity': 1, 'kernel': 'rbf'}</td>\n",
" <td>003_NAB_id_3_WebService_tr_1362_1st_1462.csv</td>\n",
- " <td>8.295331</td>\n",
+ " <td>8.578760</td>\n",
" <td>NAB</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
- " <td>0.078955</td>\n",
- " <td>0.416574</td>\n",
- " <td>0.080216</td>\n",
- " <td>0.422209</td>\n",
+ " <td>0.110890</td>\n",
+ " <td>0.509784</td>\n",
+ " <td>0.107358</td>\n",
+ " <td>0.513948</td>\n",
" <td>0.175308</td>\n",
- " <td>0.723859</td>\n",
- " <td>0.175214</td>\n",
- " <td>0.269313</td>\n",
- " <td>0.671400</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
+ " <td>0.184004</td>\n",
+ " <td>0.175893</td>\n",
+ " <td>0.211018</td>\n",
+ " <td>0.669765</td>\n",
+ " <td>0.00000</td>\n",
+ " <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
- " <td>{'periodicity': 2}</td>\n",
+ " <td>{'periodicity': 3, 'kernel': 'rbf'}</td>\n",
" <td>004_NAB_id_4_Facility_tr_1007_1st_1437.csv</td>\n",
- " <td>2.563025</td>\n",
+ " <td>1.934317</td>\n",
" <td>NAB</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
- " <td>0.073780</td>\n",
- " <td>0.369817</td>\n",
- " <td>0.076946</td>\n",
- " <td>0.383963</td>\n",
- " <td>0.186259</td>\n",
- " <td>0.398612</td>\n",
- " <td>0.188907</td>\n",
- " <td>0.347752</td>\n",
- " <td>0.670988</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
+ " <td>0.324734</td>\n",
+ " <td>0.668543</td>\n",
+ " <td>0.329563</td>\n",
+ " <td>0.687405</td>\n",
+ " <td>0.397208</td>\n",
+ " <td>0.996283</td>\n",
+ " <td>0.869565</td>\n",
+ " <td>0.275367</td>\n",
+ " <td>0.948430</td>\n",
+ " <td>0.00000</td>\n",
+ " <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
- " <td>{'periodicity': 2}</td>\n",
+ " <td>{'periodicity': 3, 'kernel': 'rbf'}</td>\n",
" <td>005_NAB_id_5_Traffic_tr_594_1st_1645.csv</td>\n",
- " <td>1.041672</td>\n",
+ " <td>0.983028</td>\n",
" <td>NAB</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
- " <td>0.062074</td>\n",
- " <td>0.176755</td>\n",
- " <td>0.064509</td>\n",
- " <td>0.178353</td>\n",
- " <td>0.209967</td>\n",
- " <td>0.214898</td>\n",
- " <td>0.209323</td>\n",
- " <td>0.329651</td>\n",
- " <td>0.671004</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
+ " <td>0.123734</td>\n",
+ " <td>0.496138</td>\n",
+ " <td>0.125226</td>\n",
+ " <td>0.500474</td>\n",
+ " <td>0.227087</td>\n",
+ " <td>0.908397</td>\n",
+ " <td>0.384615</td>\n",
+ " <td>0.272977</td>\n",
+ " <td>0.844777</td>\n",
+ " <td>0.00000</td>\n",
+ " <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
- " params file_name duration \\\n",
- "0 {'periodicity': 2} 001_NAB_id_1_Facility_tr_1007_1st_2014.csv 5.479764 \n",
- "1 {'periodicity': 2} 002_NAB_id_2_WebService_tr_1500_1st_4106.csv 4.908555 \n",
- "2 {'periodicity': 3} 003_NAB_id_3_WebService_tr_1362_1st_1462.csv 8.295331 \n",
- "3 {'periodicity': 2} 004_NAB_id_4_Facility_tr_1007_1st_1437.csv 2.563025 \n",
- "4 {'periodicity': 2} 005_NAB_id_5_Traffic_tr_594_1st_1645.csv 1.041672 \n",
+ " params \\\n",
+ "0 {'periodicity': 2, 'kernel': 'rbf'} \n",
+ "1 {'periodicity': 2, 'kernel': 'rbf'} \n",
+ "2 {'periodicity': 1, 'kernel': 'rbf'} \n",
+ "3 {'periodicity': 3, 'kernel': 'rbf'} \n",
+ "4 {'periodicity': 3, 'kernel': 'rbf'} \n",
"\n",
- " group point anomaly seq anomaly AUC-PR AUC-ROC VUS-PR VUS-ROC \\\n",
- "0 NAB False True 0.171235 0.622904 0.156004 0.610862 \n",
- "1 NAB False False 0.127993 0.542582 0.131516 0.559355 \n",
- "2 NAB False True 0.078955 0.416574 0.080216 0.422209 \n",
- "3 NAB False False 0.073780 0.369817 0.076946 0.383963 \n",
- "4 NAB False False 0.062074 0.176755 0.064509 0.178353 \n",
+ " file_name duration group \\\n",
+ "0 001_NAB_id_1_Facility_tr_1007_1st_2014.csv 1.670064 NAB \n",
+ "1 002_NAB_id_2_WebService_tr_1500_1st_4106.csv 3.762166 NAB \n",
+ "2 003_NAB_id_3_WebService_tr_1362_1st_1462.csv 8.578760 NAB \n",
+ "3 004_NAB_id_4_Facility_tr_1007_1st_1437.csv 1.934317 NAB \n",
+ "4 005_NAB_id_5_Traffic_tr_594_1st_1645.csv 0.983028 NAB \n",
"\n",
- " Standard-F1 PA-F1 Event-based-F1 R-based-F1 Affiliation-F Recall \\\n",
- "0 0.223828 0.871545 0.679245 0.246553 0.719035 0.0 \n",
- "1 0.203427 0.996091 0.369231 0.400327 0.734259 0.0 \n",
- "2 0.175308 0.723859 0.175214 0.269313 0.671400 0.0 \n",
- "3 0.186259 0.398612 0.188907 0.347752 0.670988 0.0 \n",
- "4 0.209967 0.214898 0.209323 0.329651 0.671004 0.0 \n",
+ " point anomaly seq anomaly AUC-PR AUC-ROC VUS-PR VUS-ROC \\\n",
+ "0 False True 0.139487 0.549979 0.129207 0.554000 \n",
+ "1 False False 0.122769 0.531865 0.130022 0.557598 \n",
+ "2 False True 0.110890 0.509784 0.107358 0.513948 \n",
+ "3 False False 0.324734 0.668543 0.329563 0.687405 \n",
+ "4 False False 0.123734 0.496138 0.125226 0.500474 \n",
+ "\n",
+ " Standard-F1 PA-F1 Event-based-F1 R-based-F1 Affiliation-F Recall \\\n",
+ "0 0.180031 0.988473 0.800000 0.299881 0.870591 0.03207 \n",
+ "1 0.201978 0.216814 0.199095 0.233291 0.672758 0.00000 \n",
+ "2 0.175308 0.184004 0.175893 0.211018 0.669765 0.00000 \n",
+ "3 0.397208 0.996283 0.869565 0.275367 0.948430 0.00000 \n",
+ "4 0.227087 0.908397 0.384615 0.272977 0.844777 0.00000 \n",
"\n",
" Precision \n",
- "0 0.0 \n",
- "1 0.0 \n",
- "2 0.0 \n",
- "3 0.0 \n",
- "4 0.0 "
+ "0 0.611111 \n",
+ "1 0.000000 \n",
+ "2 0.000000 \n",
+ "3 0.000000 \n",
+ "4 0.000000 "
]
},
- "execution_count": 2,
+ "execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Dataframe erstellen\n",
- "model = 'KShapeAD'\n",
+ "model = 'OCSVM'\n",
"path = '../../../docs/evaluation/'+model+'.csv'\n",
"df = pd.read_csv(path)\n",
"df.head()\n",
@@ -207,7 +214,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 32,
"metadata": {},
"outputs": [
{
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"Name: count, dtype: int64"
]
},
- "execution_count": 3,
+ "execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
@@ -251,7 +258,7 @@
},
{
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- "execution_count": 4,
+ "execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
@@ -291,7 +298,7 @@
},
{
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- "execution_count": 18,
+ "execution_count": 47,
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+ "text": "VUS-PR"
}
}
}
@@ -2691,7 +2698,7 @@
],
"source": [
"#Boxplot\n",
- "metric = 'AUC-PR'\n",
+ "metric = 'VUS-PR'\n",
"fig = px.box(df, x = 'group',y=metric, color='group',color_discrete_map= colors,hover_name=\"group\", hover_data=[\"group\"])\n",
"fig.update_layout( \n",
" template = 'ggplot2',\n",
@@ -2722,7 +2729,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 48,
"metadata": {},
"outputs": [
{
@@ -2776,107 +2783,107 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
- " <td>{'periodicity': 2}</td>\n",
+ " <td>{'periodicity': 2, 'kernel': 'rbf'}</td>\n",
" <td>001_NAB_id_1_Facility_tr_1007_1st_2014.csv</td>\n",
- " <td>5.479764</td>\n",
+ " <td>1.670064</td>\n",
" <td>NAB</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
- " <td>0.171235</td>\n",
- " <td>0.622904</td>\n",
- " <td>0.156004</td>\n",
- " <td>0.610862</td>\n",
- " <td>0.223828</td>\n",
- " <td>0.871545</td>\n",
- " <td>0.679245</td>\n",
- " <td>0.246553</td>\n",
- " <td>0.719035</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.0</td>\n",
+ " <td>0.139487</td>\n",
+ " <td>0.549979</td>\n",
+ " <td>0.129207</td>\n",
+ " <td>0.554000</td>\n",
+ " <td>0.180031</td>\n",
+ " <td>0.988473</td>\n",
+ " <td>0.800000</td>\n",
+ " <td>0.299881</td>\n",
+ " <td>0.870591</td>\n",
+ " <td>0.03207</td>\n",
+ " <td>0.611111</td>\n",
" <td>Sequenzeanomalie</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
- " <td>{'periodicity': 3}</td>\n",
+ " <td>{'periodicity': 1, 'kernel': 'rbf'}</td>\n",
" <td>003_NAB_id_3_WebService_tr_1362_1st_1462.csv</td>\n",
- " <td>8.295331</td>\n",
+ " <td>8.578760</td>\n",
" <td>NAB</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
- " <td>0.078955</td>\n",
- " <td>0.416574</td>\n",
- " <td>0.080216</td>\n",
- " <td>0.422209</td>\n",
+ " <td>0.110890</td>\n",
+ " <td>0.509784</td>\n",
+ " <td>0.107358</td>\n",
+ " <td>0.513948</td>\n",
" <td>0.175308</td>\n",
- " <td>0.723859</td>\n",
- " <td>0.175214</td>\n",
- " <td>0.269313</td>\n",
- " <td>0.671400</td>\n",
+ " <td>0.184004</td>\n",
+ " <td>0.175893</td>\n",
+ " <td>0.211018</td>\n",
+ " <td>0.669765</td>\n",
+ " <td>0.00000</td>\n",
" <td>0.000000</td>\n",
- " <td>0.0</td>\n",
" <td>Sequenzeanomalie</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
- " <td>{'periodicity': 3}</td>\n",
+ " <td>{'periodicity': 1, 'kernel': 'poly'}</td>\n",
" <td>006_NAB_id_6_Traffic_tr_2579_1st_5839.csv</td>\n",
- " <td>6.932669</td>\n",
+ " <td>6.128746</td>\n",
" <td>NAB</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
- " <td>0.844872</td>\n",
- " <td>0.964931</td>\n",
- " <td>0.867664</td>\n",
- " <td>0.982314</td>\n",
- " <td>0.762540</td>\n",
- " <td>0.937216</td>\n",
- " <td>0.907008</td>\n",
- " <td>0.814333</td>\n",
- " <td>0.905203</td>\n",
- " <td>0.154369</td>\n",
- " <td>1.0</td>\n",
+ " <td>0.290995</td>\n",
+ " <td>0.682420</td>\n",
+ " <td>0.321546</td>\n",
+ " <td>0.736206</td>\n",
+ " <td>0.298230</td>\n",
+ " <td>0.879590</td>\n",
+ " <td>0.776065</td>\n",
+ " <td>0.296481</td>\n",
+ " <td>0.834563</td>\n",
+ " <td>0.00000</td>\n",
+ " <td>0.000000</td>\n",
" <td>Sequenzeanomalie</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
- " <td>{'periodicity': 1}</td>\n",
+ " <td>{'periodicity': 3, 'kernel': 'sigmoid'}</td>\n",
" <td>007_NAB_id_7_Traffic_tr_624_1st_2087.csv</td>\n",
- " <td>3.026761</td>\n",
+ " <td>2.183970</td>\n",
" <td>NAB</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
- " <td>0.164313</td>\n",
- " <td>0.649297</td>\n",
- " <td>0.280987</td>\n",
- " <td>0.731016</td>\n",
- " <td>0.258263</td>\n",
- " <td>0.745865</td>\n",
- " <td>0.380952</td>\n",
- " <td>0.525571</td>\n",
- " <td>0.730944</td>\n",
+ " <td>0.147641</td>\n",
+ " <td>0.587805</td>\n",
+ " <td>0.180629</td>\n",
+ " <td>0.684500</td>\n",
+ " <td>0.222013</td>\n",
+ " <td>0.990020</td>\n",
+ " <td>0.444444</td>\n",
+ " <td>0.313184</td>\n",
+ " <td>0.850189</td>\n",
+ " <td>0.00000</td>\n",
" <td>0.000000</td>\n",
- " <td>0.0</td>\n",
" <td>Sequenzeanomalie</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
- " <td>{'periodicity': 2}</td>\n",
+ " <td>{'periodicity': 1, 'kernel': 'rbf'}</td>\n",
" <td>010_NAB_id_10_WebService_tr_500_1st_271.csv</td>\n",
- " <td>1.673692</td>\n",
+ " <td>1.792047</td>\n",
" <td>NAB</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
- " <td>0.413914</td>\n",
- " <td>0.496511</td>\n",
- " <td>0.359138</td>\n",
- " <td>0.560205</td>\n",
- " <td>0.489792</td>\n",
- " <td>0.484848</td>\n",
- " <td>0.494845</td>\n",
- " <td>0.494845</td>\n",
- " <td>0.675674</td>\n",
+ " <td>0.339656</td>\n",
+ " <td>0.808955</td>\n",
+ " <td>0.511266</td>\n",
+ " <td>0.890510</td>\n",
+ " <td>0.470793</td>\n",
+ " <td>0.755102</td>\n",
+ " <td>0.573770</td>\n",
+ " <td>0.366489</td>\n",
+ " <td>0.832721</td>\n",
+ " <td>0.00000</td>\n",
" <td>0.000000</td>\n",
- " <td>0.0</td>\n",
" <td>Sequenzeanomalie</td>\n",
" </tr>\n",
" </tbody>\n",
@@ -2884,36 +2891,43 @@
"</div>"
],
"text/plain": [
- " params file_name duration \\\n",
- "0 {'periodicity': 2} 001_NAB_id_1_Facility_tr_1007_1st_2014.csv 5.479764 \n",
- "2 {'periodicity': 3} 003_NAB_id_3_WebService_tr_1362_1st_1462.csv 8.295331 \n",
- "5 {'periodicity': 3} 006_NAB_id_6_Traffic_tr_2579_1st_5839.csv 6.932669 \n",
- "6 {'periodicity': 1} 007_NAB_id_7_Traffic_tr_624_1st_2087.csv 3.026761 \n",
- "9 {'periodicity': 2} 010_NAB_id_10_WebService_tr_500_1st_271.csv 1.673692 \n",
+ " params \\\n",
+ "0 {'periodicity': 2, 'kernel': 'rbf'} \n",
+ "2 {'periodicity': 1, 'kernel': 'rbf'} \n",
+ "5 {'periodicity': 1, 'kernel': 'poly'} \n",
+ "6 {'periodicity': 3, 'kernel': 'sigmoid'} \n",
+ "9 {'periodicity': 1, 'kernel': 'rbf'} \n",
+ "\n",
+ " file_name duration group point anomaly \\\n",
+ "0 001_NAB_id_1_Facility_tr_1007_1st_2014.csv 1.670064 NAB False \n",
+ "2 003_NAB_id_3_WebService_tr_1362_1st_1462.csv 8.578760 NAB False \n",
+ "5 006_NAB_id_6_Traffic_tr_2579_1st_5839.csv 6.128746 NAB False \n",
+ "6 007_NAB_id_7_Traffic_tr_624_1st_2087.csv 2.183970 NAB False \n",
+ "9 010_NAB_id_10_WebService_tr_500_1st_271.csv 1.792047 NAB False \n",
"\n",
- " group point anomaly seq anomaly AUC-PR AUC-ROC VUS-PR VUS-ROC \\\n",
- "0 NAB False True 0.171235 0.622904 0.156004 0.610862 \n",
- "2 NAB False True 0.078955 0.416574 0.080216 0.422209 \n",
- "5 NAB False True 0.844872 0.964931 0.867664 0.982314 \n",
- "6 NAB False True 0.164313 0.649297 0.280987 0.731016 \n",
- "9 NAB False True 0.413914 0.496511 0.359138 0.560205 \n",
+ " seq anomaly AUC-PR AUC-ROC VUS-PR VUS-ROC Standard-F1 PA-F1 \\\n",
+ "0 True 0.139487 0.549979 0.129207 0.554000 0.180031 0.988473 \n",
+ "2 True 0.110890 0.509784 0.107358 0.513948 0.175308 0.184004 \n",
+ "5 True 0.290995 0.682420 0.321546 0.736206 0.298230 0.879590 \n",
+ "6 True 0.147641 0.587805 0.180629 0.684500 0.222013 0.990020 \n",
+ "9 True 0.339656 0.808955 0.511266 0.890510 0.470793 0.755102 \n",
"\n",
- " Standard-F1 PA-F1 Event-based-F1 R-based-F1 Affiliation-F Recall \\\n",
- "0 0.223828 0.871545 0.679245 0.246553 0.719035 0.000000 \n",
- "2 0.175308 0.723859 0.175214 0.269313 0.671400 0.000000 \n",
- "5 0.762540 0.937216 0.907008 0.814333 0.905203 0.154369 \n",
- "6 0.258263 0.745865 0.380952 0.525571 0.730944 0.000000 \n",
- "9 0.489792 0.484848 0.494845 0.494845 0.675674 0.000000 \n",
+ " Event-based-F1 R-based-F1 Affiliation-F Recall Precision \\\n",
+ "0 0.800000 0.299881 0.870591 0.03207 0.611111 \n",
+ "2 0.175893 0.211018 0.669765 0.00000 0.000000 \n",
+ "5 0.776065 0.296481 0.834563 0.00000 0.000000 \n",
+ "6 0.444444 0.313184 0.850189 0.00000 0.000000 \n",
+ "9 0.573770 0.366489 0.832721 0.00000 0.000000 \n",
"\n",
- " Precision anomaly \n",
- "0 0.0 Sequenzeanomalie \n",
- "2 0.0 Sequenzeanomalie \n",
- "5 1.0 Sequenzeanomalie \n",
- "6 0.0 Sequenzeanomalie \n",
- "9 0.0 Sequenzeanomalie "
+ " anomaly \n",
+ "0 Sequenzeanomalie \n",
+ "2 Sequenzeanomalie \n",
+ "5 Sequenzeanomalie \n",
+ "6 Sequenzeanomalie \n",
+ "9 Sequenzeanomalie "
]
},
- "execution_count": 19,
+ "execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
@@ -2928,7 +2942,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 49,
"metadata": {},
"outputs": [
{
@@ -2988,7 +3002,7 @@
"NAB"
]
],
- "hovertemplate": "<b>%{hovertext}</b><br><br>group=%{customdata[0]}<br>anomaly=%{x}<br>AUC-PR=%{y}<extra></extra>",
+ "hovertemplate": "<b>%{hovertext}</b><br><br>group=%{customdata[0]}<br>anomaly=%{x}<br>VUS-PR=%{y}<extra></extra>",
"hovertext": [
"NAB",
"NAB",
@@ -3030,18 +3044,18 @@
"x0": " ",
"xaxis": "x",
"y": [
- 0.1712348422234141,
- 0.0789554093695429,
- 0.8448721013149383,
- 0.1643133093288575,
- 0.4139142377604495,
- 0.6343432487173599,
- 0.100314997438485,
- 0.0626935274371929,
- 0.0989292543388991,
- 0.2124202620562435,
- 0.1230694768866554,
- 0.1174605970685635
+ 0.1292072496284678,
+ 0.1073576983442578,
+ 0.3215459408637963,
+ 0.1806288423462511,
+ 0.5112663974848028,
+ 0.7231785882949368,
+ 0.17414099358733,
+ 0.4808231601638217,
+ 0.1312270472890565,
+ 0.3201277578179863,
+ 0.3823562425735545,
+ 0.1039243752600688
],
"y0": " ",
"yaxis": "y"
@@ -3098,7 +3112,7 @@
"WSD"
]
],
- "hovertemplate": "<b>%{hovertext}</b><br><br>group=%{customdata[0]}<br>anomaly=%{x}<br>AUC-PR=%{y}<extra></extra>",
+ "hovertemplate": "<b>%{hovertext}</b><br><br>group=%{customdata[0]}<br>anomaly=%{x}<br>VUS-PR=%{y}<extra></extra>",
"hovertext": [
"WSD",
"WSD",
@@ -3148,22 +3162,22 @@
"x0": " ",
"xaxis": "x",
"y": [
- 0.4348862741947762,
- 0.0072786051792892,
- 0.0020465926712742,
- 0.0001921277811436,
- 0.0834662530029794,
- 0.0061991739007884,
- 0.0020053363752814,
- 0.1192128272261476,
- 0.0085989440968323,
- 0.0152894468020548,
- 0.4406961973340506,
- 0.0549985033722748,
- 0.0116241455693966,
- 0.2465903973907245,
- 0.2023049769793997,
- 0.3797092325918696
+ 0.0229763520567632,
+ 0.037666217112104,
+ 0.0099968899939866,
+ 0.0024570298426776,
+ 0.0316720375062317,
+ 0.0343588820899651,
+ 0.0961076916644369,
+ 0.0417201115404397,
+ 0.073119431861344,
+ 0.0767033462327252,
+ 0.0282146561333983,
+ 0.0935462520470479,
+ 0.0332059559852135,
+ 0.0181424605670779,
+ 0.0365117553586275,
+ 0.0596138004605485
],
"y0": " ",
"yaxis": "y"
@@ -3181,7 +3195,7 @@
"MSL"
]
],
- "hovertemplate": "<b>%{hovertext}</b><br><br>group=%{customdata[0]}<br>anomaly=%{x}<br>AUC-PR=%{y}<extra></extra>",
+ "hovertemplate": "<b>%{hovertext}</b><br><br>group=%{customdata[0]}<br>anomaly=%{x}<br>VUS-PR=%{y}<extra></extra>",
"hovertext": [
"MSL",
"MSL",
@@ -3205,9 +3219,9 @@
"x0": " ",
"xaxis": "x",
"y": [
- 0.2208742315494321,
- 0.2565381964504278,
- 0.1075420689922313
+ 0.296626480136044,
+ 0.2359898879353481,
+ 0.0937889043310272
],
"y0": " ",
"yaxis": "y"
@@ -3246,7 +3260,7 @@
"Stock"
]
],
- "hovertemplate": "<b>%{hovertext}</b><br><br>group=%{customdata[0]}<br>anomaly=%{x}<br>AUC-PR=%{y}<extra></extra>",
+ "hovertemplate": "<b>%{hovertext}</b><br><br>group=%{customdata[0]}<br>anomaly=%{x}<br>VUS-PR=%{y}<extra></extra>",
"hovertext": [
"Stock",
"Stock",
@@ -3284,16 +3298,16 @@
"x0": " ",
"xaxis": "x",
"y": [
- 0.0526347926958866,
- 0.0618658700544456,
- 0.1583384472261794,
- 0.055437436098787,
- 0.0506131535126924,
- 0.0578458419493768,
- 0.0493450471873438,
- 0.1509019416397851,
- 0.150226418280182,
- 0.1661255440161962
+ 0.7486686118858309,
+ 0.8821615428837951,
+ 0.9098167761612456,
+ 0.8928182313105654,
+ 0.1551074439636654,
+ 0.6392334244176398,
+ 0.1912333094071469,
+ 0.9278514447466232,
+ 0.9720634119744124,
+ 0.8548178131138489
],
"y0": " ",
"yaxis": "y"
@@ -3305,7 +3319,7 @@
"Daphnet"
]
],
- "hovertemplate": "<b>%{hovertext}</b><br><br>group=%{customdata[0]}<br>anomaly=%{x}<br>AUC-PR=%{y}<extra></extra>",
+ "hovertemplate": "<b>%{hovertext}</b><br><br>group=%{customdata[0]}<br>anomaly=%{x}<br>VUS-PR=%{y}<extra></extra>",
"hovertext": [
"Daphnet"
],
@@ -3325,7 +3339,7 @@
"x0": " ",
"xaxis": "x",
"y": [
- 0.0334203029926677
+ 0.0701389976338681
],
"y0": " ",
"yaxis": "y"
@@ -3358,7 +3372,7 @@
"MITDB"
]
],
- "hovertemplate": "<b>%{hovertext}</b><br><br>group=%{customdata[0]}<br>anomaly=%{x}<br>AUC-PR=%{y}<extra></extra>",
+ "hovertemplate": "<b>%{hovertext}</b><br><br>group=%{customdata[0]}<br>anomaly=%{x}<br>VUS-PR=%{y}<extra></extra>",
"hovertext": [
"MITDB",
"MITDB",
@@ -3392,14 +3406,14 @@
"x0": " ",
"xaxis": "x",
"y": [
- 0.8554879783837588,
- 0.9402112403968608,
- 0.3165555306032828,
- 0.5698766529238668,
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- 0.2863473889933967,
- 0.9595256269953926,
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+ 0.2050837850436249,
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+ 0.9460149148594176,
+ 0.1966201642023121
],
"y0": " ",
"yaxis": "y"
@@ -3450,7 +3464,7 @@
"SMD"
]
],
- "hovertemplate": "<b>%{hovertext}</b><br><br>group=%{customdata[0]}<br>anomaly=%{x}<br>AUC-PR=%{y}<extra></extra>",
+ "hovertemplate": "<b>%{hovertext}</b><br><br>group=%{customdata[0]}<br>anomaly=%{x}<br>VUS-PR=%{y}<extra></extra>",
"hovertext": [
"SMD",
"SMD",
@@ -3496,20 +3510,20 @@
"x0": " ",
"xaxis": "x",
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- 0.3222309042663593,
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+ 0.0597597047009422,
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+ 0.1128848315625419
],
"y0": " ",
"yaxis": "y"
@@ -3545,7 +3559,7 @@
"LTDB"
]
],
- "hovertemplate": "<b>%{hovertext}</b><br><br>group=%{customdata[0]}<br>anomaly=%{x}<br>AUC-PR=%{y}<extra></extra>",
+ "hovertemplate": "<b>%{hovertext}</b><br><br>group=%{customdata[0]}<br>anomaly=%{x}<br>VUS-PR=%{y}<extra></extra>",
"hovertext": [
"LTDB",
"LTDB",
@@ -3581,15 +3595,15 @@
"x0": " ",
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- 0.823099074302394,
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+ 0.681523108603212,
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],
"y0": " ",
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@@ -3625,7 +3639,7 @@
"MGAB"
]
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- "hovertemplate": "<b>%{hovertext}</b><br><br>group=%{customdata[0]}<br>anomaly=%{x}<br>AUC-PR=%{y}<extra></extra>",
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}
},
"title": {
- "text": "<b> Vergleich der Ergebnisse der Anomalien zwischen den Gruppen für KShapeAD </b>",
+ "text": "<b> Vergleich der Ergebnisse der Anomalien zwischen den Gruppen für OCSVM </b>",
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1
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}
@@ -5458,7 +5472,7 @@
},
{
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- "execution_count": 21,
+ "execution_count": 50,
"metadata": {},
"outputs": [
{
@@ -5467,81 +5481,81 @@
"text": [
" duration AUC-PR AUC-ROC VUS-PR VUS-ROC Standard-F1 \\\n",
"group \n",
- "CATSv2 236.819437 0.452377 0.724201 0.230658 0.696605 0.507399 \n",
- "Daphnet 35.623369 0.033420 0.120788 0.035690 0.119038 0.114305 \n",
- "Exathlon 30.996458 0.420990 0.704579 0.420845 0.705633 0.476715 \n",
- "IOPS 55.233800 0.068365 0.547448 0.064609 0.518019 0.119151 \n",
- "LTDB 93.477461 0.743642 0.864903 0.811737 0.883000 0.743372 \n",
- "MGAB 36.636040 0.084411 0.693115 0.069759 0.687555 0.229185 \n",
- "MITDB 443.862543 0.594424 0.947580 0.606652 0.961219 0.600204 \n",
- "MSL 2.604013 0.409835 0.825987 0.459063 0.839459 0.504202 \n",
- "NAB 7.395364 0.396635 0.660959 0.415046 0.674869 0.478648 \n",
- "NEK 0.978331 0.379153 0.673130 0.357818 0.587360 0.478314 \n",
- "OPPORTUNITY 7.531728 0.366866 0.757637 0.347881 0.734117 0.434931 \n",
- "Power 17.223233 0.496250 0.737337 0.510537 0.760943 0.566831 \n",
- "SED 11.265208 0.722020 0.989946 0.907086 0.996810 0.780849 \n",
- "SMAP 4.100383 0.601232 0.767636 0.609905 0.778792 0.593002 \n",
- "SMD 11.754587 0.131250 0.655848 0.122593 0.658158 0.186660 \n",
- "SVDB 69.581790 0.798760 0.984325 0.810962 0.987226 0.764633 \n",
- "SWaT 1051.773305 0.458896 0.799756 0.381310 0.756757 0.629526 \n",
- "Stock 57.478806 0.095333 0.529748 0.717890 0.836341 0.159066 \n",
- "TAO 29.975217 0.103834 0.517935 0.721004 0.825628 0.172782 \n",
- "TODS 8.645978 0.462057 0.809107 0.747606 0.885093 0.492481 \n",
- "UCR 17.042999 0.465144 0.889491 0.460106 0.897374 0.480275 \n",
- "WSD 9.334371 0.082639 0.662574 0.112541 0.680839 0.128861 \n",
- "YAHOO 1.173619 0.435473 0.746907 0.491670 0.765170 0.466854 \n",
+ "CATSv2 576.820577 0.369080 0.706380 0.311384 0.729282 0.426982 \n",
+ "Daphnet 27.302989 0.066512 0.507790 0.070139 0.523716 0.116764 \n",
+ "Exathlon 37.826101 0.556786 0.830684 0.557891 0.833418 0.577401 \n",
+ "IOPS 47.575929 0.051207 0.663177 0.067672 0.660286 0.109625 \n",
+ "LTDB 106.949586 0.634659 0.796257 0.692470 0.850891 0.638340 \n",
+ "MGAB 177.334281 0.032883 0.537176 0.007014 0.642782 0.084602 \n",
+ "MITDB 1624.883895 0.355826 0.856398 0.351133 0.897026 0.435382 \n",
+ "MSL 2.056389 0.239125 0.739126 0.276927 0.795165 0.362317 \n",
+ "NAB 2.769945 0.343854 0.666136 0.376638 0.709715 0.402922 \n",
+ "NEK 0.791268 0.360131 0.710666 0.367261 0.743413 0.432511 \n",
+ "OPPORTUNITY 7.374317 0.147631 0.661025 0.148914 0.667929 0.228057 \n",
+ "Power 51.094489 0.215936 0.652180 0.225393 0.677670 0.237451 \n",
+ "SED 14.702547 0.187850 0.610291 0.164910 0.722816 0.220954 \n",
+ "SMAP 2.776193 0.487651 0.861212 0.565618 0.876350 0.563891 \n",
+ "SMD 12.425167 0.122192 0.744937 0.115908 0.774569 0.182900 \n",
+ "SVDB 363.206067 0.547080 0.930221 0.592468 0.951451 0.598525 \n",
+ "SWaT 1210.999244 0.159515 0.518403 0.159981 0.520024 0.309398 \n",
+ "Stock 174.322859 0.097345 0.542291 0.717377 0.897425 0.170772 \n",
+ "TAO 54.669874 0.117776 0.577303 0.720442 0.912899 0.205507 \n",
+ "TODS 8.419030 0.404043 0.740680 0.748147 0.906301 0.437865 \n",
+ "UCR 151.752637 0.326989 0.853025 0.336356 0.877345 0.374480 \n",
+ "WSD 7.525696 0.036128 0.746600 0.039999 0.818527 0.074105 \n",
+ "YAHOO 0.962506 0.083868 0.877014 0.306932 0.936604 0.135211 \n",
"\n",
" PA-F1 Event-based-F1 R-based-F1 Affiliation-F Recall \\\n",
"group \n",
- "CATSv2 0.626398 0.439105 0.170890 0.702560 0.300878 \n",
- "Daphnet 0.128525 0.114273 0.396142 0.832210 0.000000 \n",
- "Exathlon 0.909250 0.697865 0.443662 0.869385 0.012750 \n",
- "IOPS 0.311225 0.106215 0.202900 0.736656 0.073274 \n",
- "LTDB 0.860404 0.827142 0.731083 0.920124 0.097710 \n",
- "MGAB 0.248686 0.229101 0.252603 0.739126 0.118095 \n",
- "MITDB 0.699544 0.643637 0.647918 0.918732 0.373862 \n",
- "MSL 0.650031 0.535044 0.532483 0.849117 0.145087 \n",
- "NAB 0.766138 0.586103 0.525518 0.831252 0.050599 \n",
- "NEK 0.615688 0.486707 0.522330 0.770504 0.001764 \n",
- "OPPORTUNITY 0.860770 0.675563 0.377457 0.877531 0.021827 \n",
- "Power 0.955110 0.863916 0.562924 0.957009 0.182667 \n",
- "SED 0.882984 0.826164 0.808976 0.986410 0.655488 \n",
- "SMAP 0.745946 0.722506 0.636957 0.907428 0.559008 \n",
- "SMD 0.468453 0.288651 0.255678 0.771941 0.095875 \n",
- "SVDB 0.878824 0.832142 0.697537 0.940637 0.659443 \n",
- "SWaT 0.895086 0.433666 0.141600 0.705099 0.000000 \n",
- "Stock 0.158497 0.158075 0.134519 0.667352 0.000479 \n",
- "TAO 0.172423 0.172095 0.098197 0.679173 0.000000 \n",
- "TODS 0.510765 0.327060 0.331376 0.747656 0.187118 \n",
- "UCR 0.603514 0.540664 0.514970 0.885032 0.536193 \n",
- "WSD 0.169441 0.143924 0.195417 0.756749 0.009756 \n",
- "YAHOO 0.397359 0.388677 0.440259 0.854958 0.414790 \n",
+ "CATSv2 0.873839 0.709922 0.158695 0.727890 0.040811 \n",
+ "Daphnet 0.916632 0.178742 0.247652 0.770237 0.002168 \n",
+ "Exathlon 0.953952 0.831443 0.342542 0.906246 0.019985 \n",
+ "IOPS 0.447280 0.157321 0.185504 0.720738 0.075638 \n",
+ "LTDB 0.897274 0.799590 0.574608 0.892653 0.086590 \n",
+ "MGAB 0.218686 0.194577 0.099208 0.677589 0.000000 \n",
+ "MITDB 0.765995 0.550179 0.349144 0.861050 0.217145 \n",
+ "MSL 0.755050 0.478430 0.359768 0.831862 0.109275 \n",
+ "NAB 0.915571 0.751830 0.400021 0.895345 0.072529 \n",
+ "NEK 0.766319 0.617067 0.404298 0.844997 0.031221 \n",
+ "OPPORTUNITY 0.902061 0.400628 0.217145 0.773705 0.002064 \n",
+ "Power 1.000000 1.000000 0.352493 0.966658 0.000000 \n",
+ "SED 0.596144 0.588889 0.296696 0.784206 0.000000 \n",
+ "SMAP 0.745047 0.624615 0.539847 0.893603 0.546802 \n",
+ "SMD 0.682397 0.320169 0.208408 0.781595 0.069495 \n",
+ "SVDB 0.840533 0.747939 0.469409 0.909202 0.304473 \n",
+ "SWaT 0.764353 0.248020 0.108407 0.689201 0.000000 \n",
+ "Stock 0.162702 0.160067 0.183969 0.680238 0.000489 \n",
+ "TAO 0.204991 0.202438 0.209541 0.681314 0.000000 \n",
+ "TODS 0.514495 0.303923 0.229365 0.744793 0.149799 \n",
+ "UCR 0.705609 0.527446 0.361082 0.873885 0.371525 \n",
+ "WSD 0.245465 0.169022 0.095856 0.765850 0.018010 \n",
+ "YAHOO 0.161616 0.134211 0.125869 0.862998 0.108753 \n",
"\n",
" Precision \n",
"group \n",
- "CATSv2 0.997312 \n",
- "Daphnet 0.000000 \n",
- "Exathlon 0.164047 \n",
- "IOPS 0.007659 \n",
- "LTDB 0.847458 \n",
- "MGAB 0.150940 \n",
- "MITDB 0.613016 \n",
- "MSL 0.222222 \n",
- "NAB 0.205914 \n",
- "NEK 0.008547 \n",
- "OPPORTUNITY 0.071429 \n",
- "Power 0.708926 \n",
- "SED 0.687006 \n",
- "SMAP 0.360987 \n",
- "SMD 0.110133 \n",
- "SVDB 0.751826 \n",
+ "CATSv2 0.971061 \n",
+ "Daphnet 0.024038 \n",
+ "Exathlon 0.356978 \n",
+ "IOPS 0.125516 \n",
+ "LTDB 0.838906 \n",
+ "MGAB 0.000000 \n",
+ "MITDB 0.299381 \n",
+ "MSL 0.105778 \n",
+ "NAB 0.276158 \n",
+ "NEK 0.325397 \n",
+ "OPPORTUNITY 0.021490 \n",
+ "Power 0.000000 \n",
+ "SED 0.000000 \n",
+ "SMAP 0.338855 \n",
+ "SMD 0.096921 \n",
+ "SVDB 0.520652 \n",
"SWaT 0.000000 \n",
- "Stock 0.010490 \n",
+ "Stock 0.036376 \n",
"TAO 0.000000 \n",
- "TODS 0.538600 \n",
- "UCR 0.177598 \n",
- "WSD 0.006061 \n",
- "YAHOO 0.019690 \n"
+ "TODS 0.603929 \n",
+ "UCR 0.230630 \n",
+ "WSD 0.054808 \n",
+ "YAHOO 0.038252 \n"
]
}
],
@@ -5554,15 +5568,15 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 51,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Gesamtdauer Training in s: 12758.542, in min: 212.642, in h: 3.544\n",
- "Durchschnittliches Training pro Datei in s: 32.137\n"
+ "Gesamtdauer Training in s: 41374.425, in min: 689.574, in h: 11.493\n",
+ "Durchschnittliches Training pro Datei in s: 104.218\n"
]
}
],
@@ -5581,15 +5595,15 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 52,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Durchschnittle Gesamt-Performanz des Modells: KShapeAD\n",
- "Recall: 0.247, Precision: 0.205, F1: 0.425, Auc-Roc: 0.757, Auc-PR: 0.383, Vus-PR: 0.423\n"
+ "Durchschnittle Gesamt-Performanz des Modells: OCSVM\n",
+ "Recall: 0.155, Precision: 0.219, F1: 0.329, Auc-Roc: 0.773, Auc-PR: 0.275, Vus-PR: 0.339\n"
]
}
],
@@ -5608,7 +5622,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 53,
"metadata": {},
"outputs": [
{
@@ -5616,8 +5630,8 @@
"output_type": "stream",
"text": [
"Anzahl an Datensätzen mit Punktanomalien: 55\n",
- "Durchschnittle Gesamt-Performanz des Modells: KShapeAD\n",
- "Recall: 0.262, Precision: 0.019, F1: 0.321, Auc-Roc: 0.682, Auc-PR: 0.293, Vus-PR: 0.495\n"
+ "Durchschnittle Gesamt-Performanz des Modells: OCSVM\n",
+ "Recall: 0.068, Precision: 0.042, F1: 0.096, Auc-Roc: 0.781, Auc-PR: 0.049, Vus-PR: 0.362\n"
]
}
],
@@ -5639,42 +5653,42 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 54,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- " duration AUC-PR AUC-ROC VUS-PR VUS-ROC Standard-F1 \\\n",
- "group \n",
- "IOPS 45.516933 0.148483 0.503996 0.065996 0.368166 0.221140 \n",
- "Stock 57.478806 0.095333 0.529748 0.717890 0.836341 0.159066 \n",
- "TAO 29.587291 0.101534 0.511766 0.634080 0.773293 0.170023 \n",
- "TODS 12.472389 0.081935 0.601164 0.850387 0.908232 0.134504 \n",
- "UCR 27.806806 0.000854 0.874113 0.004607 0.898198 0.001704 \n",
- "WSD 10.255568 0.001119 0.384502 0.007506 0.386503 0.005422 \n",
- "YAHOO 1.144278 0.464439 0.749328 0.518467 0.767219 0.478743 \n",
+ " duration AUC-PR AUC-ROC VUS-PR VUS-ROC Standard-F1 \\\n",
+ "group \n",
+ "IOPS 13.898301 0.024701 0.584329 0.019092 0.557235 0.039854 \n",
+ "Stock 174.322859 0.097345 0.542291 0.717377 0.897425 0.170772 \n",
+ "TAO 63.681650 0.110446 0.567642 0.624783 0.893879 0.206042 \n",
+ "TODS 12.997364 0.070625 0.565769 0.851819 0.930252 0.123233 \n",
+ "UCR 475.566384 0.001794 0.911254 0.027690 0.954942 0.003573 \n",
+ "WSD 8.183030 0.002159 0.880521 0.006227 0.914005 0.006674 \n",
+ "YAHOO 0.987759 0.040087 0.877964 0.282248 0.947224 0.086138 \n",
"\n",
" PA-F1 Event-based-F1 R-based-F1 Affiliation-F Recall \\\n",
"group \n",
- "IOPS 0.499519 0.251189 0.446503 0.800048 0.000000 \n",
- "Stock 0.158497 0.158075 0.134519 0.667352 0.000479 \n",
- "TAO 0.169646 0.169593 0.089392 0.679750 0.000000 \n",
- "TODS 0.131855 0.131021 0.254328 0.673227 0.005109 \n",
- "UCR 0.001661 0.001661 0.001008 0.812952 0.000000 \n",
- "WSD 0.004479 0.004478 0.253760 0.733111 0.000000 \n",
- "YAHOO 0.383285 0.383191 0.433819 0.864452 0.464516 \n",
+ "IOPS 0.507532 0.257335 0.410526 0.821911 0.013319 \n",
+ "Stock 0.162702 0.160067 0.183969 0.680238 0.000489 \n",
+ "TAO 0.203796 0.202339 0.181920 0.682888 0.000000 \n",
+ "TODS 0.121369 0.121340 0.107453 0.671491 0.007663 \n",
+ "UCR 0.003412 0.003412 0.001708 0.714992 0.000000 \n",
+ "WSD 0.010685 0.005739 0.008498 0.804068 0.000000 \n",
+ "YAHOO 0.078227 0.072557 0.090661 0.861206 0.118280 \n",
"\n",
" Precision \n",
"group \n",
- "IOPS 0.000000 \n",
- "Stock 0.010490 \n",
+ "IOPS 0.500000 \n",
+ "Stock 0.036376 \n",
"TAO 0.000000 \n",
- "TODS 0.111111 \n",
+ "TODS 0.232258 \n",
"UCR 0.000000 \n",
"WSD 0.000000 \n",
- "YAHOO 0.019426 \n"
+ "YAHOO 0.008449 \n"
]
}
],
@@ -5686,7 +5700,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 55,
"metadata": {},
"outputs": [
{
@@ -5694,8 +5708,8 @@
"output_type": "stream",
"text": [
"Anzahl an Datensätzen mit Sequenzanomalien: 158\n",
- "Durchschnittle Gesamt-Performanz des Modells: KShapeAD\n",
- "Recall: 0.161, Precision: 0.302, F1: 0.453, Auc-Roc: 0.760, Auc-PR: 0.396, Vus-PR: 0.411\n"
+ "Durchschnittle Gesamt-Performanz des Modells: OCSVM\n",
+ "Recall: 0.083, Precision: 0.266, F1: 0.384, Auc-Roc: 0.755, Auc-PR: 0.335, Vus-PR: 0.354\n"
]
}
],
@@ -5717,7 +5731,7 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 56,
"metadata": {},
"outputs": [
{
@@ -5726,72 +5740,72 @@
"text": [
" duration AUC-PR AUC-ROC VUS-PR VUS-ROC Standard-F1 \\\n",
"group \n",
- "CATSv2 236.819437 0.452377 0.724201 0.230658 0.696605 0.507399 \n",
- "Daphnet 35.623369 0.033420 0.120788 0.035690 0.119038 0.114305 \n",
- "Exathlon 30.996458 0.420990 0.704579 0.420845 0.705633 0.476715 \n",
- "IOPS 22.287605 0.069434 0.560650 0.090598 0.576921 0.142728 \n",
- "LTDB 93.477461 0.743642 0.864903 0.811737 0.883000 0.743372 \n",
- "MGAB 36.636040 0.084411 0.693115 0.069759 0.687555 0.229185 \n",
- "MITDB 443.862543 0.594424 0.947580 0.606652 0.961219 0.600204 \n",
- "MSL 2.294159 0.194985 0.726015 0.290049 0.736139 0.411590 \n",
- "NAB 4.943181 0.251877 0.589256 0.282421 0.613141 0.332874 \n",
- "NEK 1.092160 0.375066 0.635456 0.282706 0.445377 0.503649 \n",
- "OPPORTUNITY 8.709118 0.283461 0.710465 0.253277 0.657397 0.362509 \n",
- "Power 17.223233 0.496250 0.737337 0.510537 0.760943 0.566831 \n",
- "SED 11.265208 0.722020 0.989946 0.907086 0.996810 0.780849 \n",
- "SMAP 3.354420 0.850192 0.977862 0.861769 0.982293 0.813132 \n",
- "SMD 10.538638 0.077411 0.648348 0.070547 0.640256 0.154819 \n",
- "SVDB 71.883359 0.788394 0.983502 0.804276 0.986567 0.753662 \n",
- "SWaT 1051.773305 0.458896 0.799756 0.381310 0.756757 0.629526 \n",
- "TODS 5.017621 0.627511 0.917800 0.719779 0.916483 0.630390 \n",
- "WSD 8.363367 0.143776 0.756489 0.192884 0.782639 0.218349 \n",
- "YAHOO 1.174419 0.240316 0.691290 0.276917 0.691833 0.439016 \n",
+ "CATSv2 576.820577 0.369080 0.706380 0.311384 0.729282 0.426982 \n",
+ "Daphnet 27.302989 0.066512 0.507790 0.070139 0.523716 0.116764 \n",
+ "Exathlon 37.826101 0.556786 0.830684 0.557891 0.833418 0.577401 \n",
+ "IOPS 11.544123 0.101872 0.723348 0.081453 0.696403 0.192020 \n",
+ "LTDB 106.949586 0.634659 0.796257 0.692470 0.850891 0.638340 \n",
+ "MGAB 177.334281 0.032883 0.537176 0.007014 0.642782 0.084602 \n",
+ "MITDB 1624.883895 0.355826 0.856398 0.351133 0.897026 0.435382 \n",
+ "MSL 2.013455 0.153546 0.633375 0.208802 0.702745 0.276630 \n",
+ "NAB 3.472690 0.245686 0.637900 0.297149 0.694854 0.325859 \n",
+ "NEK 0.886990 0.420323 0.723537 0.379838 0.777410 0.467305 \n",
+ "OPPORTUNITY 6.902177 0.090132 0.614360 0.092515 0.623037 0.163974 \n",
+ "Power 51.094489 0.215936 0.652180 0.225393 0.677670 0.237451 \n",
+ "SED 14.702547 0.187850 0.610291 0.164910 0.722816 0.220954 \n",
+ "SMAP 2.937379 0.760857 0.795312 0.795429 0.826134 0.814674 \n",
+ "SMD 14.381292 0.077154 0.691572 0.078384 0.739877 0.137919 \n",
+ "SVDB 369.966945 0.544488 0.927809 0.597908 0.950057 0.599689 \n",
+ "SWaT 1210.999244 0.159515 0.518403 0.159981 0.520024 0.309398 \n",
+ "TODS 4.506914 0.557978 0.840121 0.721308 0.918289 0.567518 \n",
+ "WSD 7.389923 0.038635 0.757766 0.048826 0.825840 0.081554 \n",
+ "YAHOO 0.791543 0.498439 0.834057 0.484099 0.809580 0.552448 \n",
"\n",
" PA-F1 Event-based-F1 R-based-F1 Affiliation-F Recall \\\n",
"group \n",
- "CATSv2 0.626398 0.439105 0.170890 0.702560 0.300878 \n",
- "Daphnet 0.128525 0.114273 0.396142 0.832210 0.000000 \n",
- "Exathlon 0.909250 0.697865 0.443662 0.869385 0.012750 \n",
- "IOPS 0.239655 0.149653 0.281651 0.815485 0.333333 \n",
- "LTDB 0.860404 0.827142 0.731083 0.920124 0.097710 \n",
- "MGAB 0.248686 0.229101 0.252603 0.739126 0.118095 \n",
- "MITDB 0.699544 0.643637 0.647918 0.918732 0.373862 \n",
- "MSL 0.571469 0.412982 0.472232 0.781600 0.000000 \n",
- "NAB 0.732157 0.449236 0.372593 0.764737 0.014752 \n",
- "NEK 0.677964 0.478924 0.551187 0.711606 0.003968 \n",
- "OPPORTUNITY 0.816442 0.580687 0.360515 0.853285 0.047013 \n",
- "Power 0.955110 0.863916 0.562924 0.957009 0.182667 \n",
- "SED 0.882984 0.826164 0.808976 0.986410 0.655488 \n",
- "SMAP 1.000000 1.000000 0.828955 0.998218 0.796642 \n",
- "SMD 0.357111 0.175342 0.224130 0.732669 0.054639 \n",
- "SVDB 0.872447 0.823307 0.695783 0.937513 0.641519 \n",
- "SWaT 0.895086 0.433666 0.141600 0.705099 0.000000 \n",
- "TODS 0.668963 0.523462 0.519941 0.853262 0.169565 \n",
- "WSD 0.287165 0.246194 0.286497 0.797086 0.017422 \n",
- "YAHOO 0.584627 0.463809 0.542449 0.805978 0.000000 \n",
+ "CATSv2 0.873839 0.709922 0.158695 0.727890 0.040811 \n",
+ "Daphnet 0.916632 0.178742 0.247652 0.770237 0.002168 \n",
+ "Exathlon 0.953952 0.831443 0.342542 0.906246 0.019985 \n",
+ "IOPS 0.338957 0.247820 0.126320 0.738963 0.387755 \n",
+ "LTDB 0.897274 0.799590 0.574608 0.892653 0.086590 \n",
+ "MGAB 0.218686 0.194577 0.099208 0.677589 0.000000 \n",
+ "MITDB 0.765995 0.550179 0.349144 0.861050 0.217145 \n",
+ "MSL 0.776111 0.357713 0.292643 0.753637 0.000000 \n",
+ "NAB 0.884031 0.611094 0.341621 0.826769 0.002672 \n",
+ "NEK 0.770002 0.562500 0.377398 0.795358 0.059259 \n",
+ "OPPORTUNITY 0.855501 0.335362 0.198465 0.749830 0.000000 \n",
+ "Power 1.000000 1.000000 0.352493 0.966658 0.000000 \n",
+ "SED 0.596144 0.588889 0.296696 0.784206 0.000000 \n",
+ "SMAP 0.947423 0.900000 0.610368 0.898112 0.464674 \n",
+ "SMD 0.719776 0.307068 0.209528 0.782645 0.022702 \n",
+ "SVDB 0.832140 0.734672 0.459895 0.904491 0.319174 \n",
+ "SWaT 0.764353 0.248020 0.108407 0.689201 0.000000 \n",
+ "TODS 0.680617 0.501154 0.408574 0.853316 0.169615 \n",
+ "WSD 0.260319 0.185103 0.112414 0.760044 0.010463 \n",
+ "YAHOO 0.907476 0.681121 0.337645 0.875085 0.007342 \n",
"\n",
" Precision \n",
"group \n",
- "CATSv2 0.997312 \n",
- "Daphnet 0.000000 \n",
- "Exathlon 0.164047 \n",
- "IOPS 0.006051 \n",
- "LTDB 0.847458 \n",
- "MGAB 0.150940 \n",
- "MITDB 0.613016 \n",
+ "CATSv2 0.971061 \n",
+ "Daphnet 0.024038 \n",
+ "Exathlon 0.356978 \n",
+ "IOPS 0.068093 \n",
+ "LTDB 0.838906 \n",
+ "MGAB 0.000000 \n",
+ "MITDB 0.299381 \n",
"MSL 0.000000 \n",
- "NAB 0.147133 \n",
- "NEK 0.019231 \n",
- "OPPORTUNITY 0.153846 \n",
- "Power 0.708926 \n",
- "SED 0.687006 \n",
- "SMAP 0.737909 \n",
- "SMD 0.124538 \n",
- "SVDB 0.758392 \n",
+ "NAB 0.050926 \n",
+ "NEK 0.482143 \n",
+ "OPPORTUNITY 0.000000 \n",
+ "Power 0.000000 \n",
+ "SED 0.000000 \n",
+ "SMAP 0.431818 \n",
+ "SMD 0.081385 \n",
+ "SVDB 0.495423 \n",
"SWaT 0.000000 \n",
- "TODS 0.524876 \n",
- "WSD 0.010823 \n",
- "YAHOO 0.000000 \n"
+ "TODS 0.708457 \n",
+ "WSD 0.015899 \n",
+ "YAHOO 0.333333 \n"
]
}
],
@@ -5810,7 +5824,7 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 57,
"metadata": {},
"outputs": [
{
@@ -5854,210 +5868,210 @@
" <tbody>\n",
" <tr>\n",
" <th>CATSv2</th>\n",
- " <td>0.452377</td>\n",
- " <td>0.724201</td>\n",
- " <td>0.230658</td>\n",
- " <td>0.507399</td>\n",
- " <td>0.300878</td>\n",
- " <td>0.997312</td>\n",
+ " <td>0.369080</td>\n",
+ " <td>0.706380</td>\n",
+ " <td>0.311384</td>\n",
+ " <td>0.426982</td>\n",
+ " <td>0.040811</td>\n",
+ " <td>0.971061</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Daphnet</th>\n",
- " <td>0.033420</td>\n",
- " <td>0.120788</td>\n",
- " <td>0.035690</td>\n",
- " <td>0.114305</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.000000</td>\n",
+ " <td>0.066512</td>\n",
+ " <td>0.507790</td>\n",
+ " <td>0.070139</td>\n",
+ " <td>0.116764</td>\n",
+ " <td>0.002168</td>\n",
+ " <td>0.024038</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Exathlon</th>\n",
- " <td>0.420990</td>\n",
- " <td>0.704579</td>\n",
- " <td>0.420845</td>\n",
- " <td>0.476715</td>\n",
- " <td>0.012750</td>\n",
- " <td>0.164047</td>\n",
+ " <td>0.556786</td>\n",
+ " <td>0.830684</td>\n",
+ " <td>0.557891</td>\n",
+ " <td>0.577401</td>\n",
+ " <td>0.019985</td>\n",
+ " <td>0.356978</td>\n",
" </tr>\n",
" <tr>\n",
" <th>IOPS</th>\n",
- " <td>0.068365</td>\n",
- " <td>0.547448</td>\n",
- " <td>0.064609</td>\n",
- " <td>0.119151</td>\n",
- " <td>0.073274</td>\n",
- " <td>0.007659</td>\n",
+ " <td>0.051207</td>\n",
+ " <td>0.663177</td>\n",
+ " <td>0.067672</td>\n",
+ " <td>0.109625</td>\n",
+ " <td>0.075638</td>\n",
+ " <td>0.125516</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LTDB</th>\n",
- " <td>0.743642</td>\n",
- " <td>0.864903</td>\n",
- " <td>0.811737</td>\n",
- " <td>0.743372</td>\n",
- " <td>0.097710</td>\n",
- " <td>0.847458</td>\n",
+ " <td>0.634659</td>\n",
+ " <td>0.796257</td>\n",
+ " <td>0.692470</td>\n",
+ " <td>0.638340</td>\n",
+ " <td>0.086590</td>\n",
+ " <td>0.838906</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MGAB</th>\n",
- " <td>0.084411</td>\n",
- " <td>0.693115</td>\n",
- " <td>0.069759</td>\n",
- " <td>0.229185</td>\n",
- " <td>0.118095</td>\n",
- " <td>0.150940</td>\n",
+ " <td>0.032883</td>\n",
+ " <td>0.537176</td>\n",
+ " <td>0.007014</td>\n",
+ " <td>0.084602</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MITDB</th>\n",
- " <td>0.594424</td>\n",
- " <td>0.947580</td>\n",
- " <td>0.606652</td>\n",
- " <td>0.600204</td>\n",
- " <td>0.373862</td>\n",
- " <td>0.613016</td>\n",
+ " <td>0.355826</td>\n",
+ " <td>0.856398</td>\n",
+ " <td>0.351133</td>\n",
+ " <td>0.435382</td>\n",
+ " <td>0.217145</td>\n",
+ " <td>0.299381</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MSL</th>\n",
- " <td>0.409835</td>\n",
- " <td>0.825987</td>\n",
- " <td>0.459063</td>\n",
- " <td>0.504202</td>\n",
- " <td>0.145087</td>\n",
- " <td>0.222222</td>\n",
+ " <td>0.239125</td>\n",
+ " <td>0.739126</td>\n",
+ " <td>0.276927</td>\n",
+ " <td>0.362317</td>\n",
+ " <td>0.109275</td>\n",
+ " <td>0.105778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NAB</th>\n",
- " <td>0.396635</td>\n",
- " <td>0.660959</td>\n",
- " <td>0.415046</td>\n",
- " <td>0.478648</td>\n",
- " <td>0.050599</td>\n",
- " <td>0.205914</td>\n",
+ " <td>0.343854</td>\n",
+ " <td>0.666136</td>\n",
+ " <td>0.376638</td>\n",
+ " <td>0.402922</td>\n",
+ " <td>0.072529</td>\n",
+ " <td>0.276158</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NEK</th>\n",
- " <td>0.379153</td>\n",
- " <td>0.673130</td>\n",
- " <td>0.357818</td>\n",
- " <td>0.478314</td>\n",
- " <td>0.001764</td>\n",
- " <td>0.008547</td>\n",
+ " <td>0.360131</td>\n",
+ " <td>0.710666</td>\n",
+ " <td>0.367261</td>\n",
+ " <td>0.432511</td>\n",
+ " <td>0.031221</td>\n",
+ " <td>0.325397</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OPPORTUNITY</th>\n",
- " <td>0.366866</td>\n",
- " <td>0.757637</td>\n",
- " <td>0.347881</td>\n",
- " <td>0.434931</td>\n",
- " <td>0.021827</td>\n",
- " <td>0.071429</td>\n",
+ " <td>0.147631</td>\n",
+ " <td>0.661025</td>\n",
+ " <td>0.148914</td>\n",
+ " <td>0.228057</td>\n",
+ " <td>0.002064</td>\n",
+ " <td>0.021490</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Power</th>\n",
- " <td>0.496250</td>\n",
- " <td>0.737337</td>\n",
- " <td>0.510537</td>\n",
- " <td>0.566831</td>\n",
- " <td>0.182667</td>\n",
- " <td>0.708926</td>\n",
+ " <td>0.215936</td>\n",
+ " <td>0.652180</td>\n",
+ " <td>0.225393</td>\n",
+ " <td>0.237451</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SED</th>\n",
- " <td>0.722020</td>\n",
- " <td>0.989946</td>\n",
- " <td>0.907086</td>\n",
- " <td>0.780849</td>\n",
- " <td>0.655488</td>\n",
- " <td>0.687006</td>\n",
+ " <td>0.187850</td>\n",
+ " <td>0.610291</td>\n",
+ " <td>0.164910</td>\n",
+ " <td>0.220954</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SMAP</th>\n",
- " <td>0.601232</td>\n",
- " <td>0.767636</td>\n",
- " <td>0.609905</td>\n",
- " <td>0.593002</td>\n",
- " <td>0.559008</td>\n",
- " <td>0.360987</td>\n",
+ " <td>0.487651</td>\n",
+ " <td>0.861212</td>\n",
+ " <td>0.565618</td>\n",
+ " <td>0.563891</td>\n",
+ " <td>0.546802</td>\n",
+ " <td>0.338855</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SMD</th>\n",
- " <td>0.131250</td>\n",
- " <td>0.655848</td>\n",
- " <td>0.122593</td>\n",
- " <td>0.186660</td>\n",
- " <td>0.095875</td>\n",
- " <td>0.110133</td>\n",
+ " <td>0.122192</td>\n",
+ " <td>0.744937</td>\n",
+ " <td>0.115908</td>\n",
+ " <td>0.182900</td>\n",
+ " <td>0.069495</td>\n",
+ " <td>0.096921</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SVDB</th>\n",
- " <td>0.798760</td>\n",
- " <td>0.984325</td>\n",
- " <td>0.810962</td>\n",
- " <td>0.764633</td>\n",
- " <td>0.659443</td>\n",
- " <td>0.751826</td>\n",
+ " <td>0.547080</td>\n",
+ " <td>0.930221</td>\n",
+ " <td>0.592468</td>\n",
+ " <td>0.598525</td>\n",
+ " <td>0.304473</td>\n",
+ " <td>0.520652</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SWaT</th>\n",
- " <td>0.458896</td>\n",
- " <td>0.799756</td>\n",
- " <td>0.381310</td>\n",
- " <td>0.629526</td>\n",
+ " <td>0.159515</td>\n",
+ " <td>0.518403</td>\n",
+ " <td>0.159981</td>\n",
+ " <td>0.309398</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Stock</th>\n",
- " <td>0.095333</td>\n",
- " <td>0.529748</td>\n",
- " <td>0.717890</td>\n",
- " <td>0.159066</td>\n",
- " <td>0.000479</td>\n",
- " <td>0.010490</td>\n",
+ " <td>0.097345</td>\n",
+ " <td>0.542291</td>\n",
+ " <td>0.717377</td>\n",
+ " <td>0.170772</td>\n",
+ " <td>0.000489</td>\n",
+ " <td>0.036376</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TAO</th>\n",
- " <td>0.103834</td>\n",
- " <td>0.517935</td>\n",
- " <td>0.721004</td>\n",
- " <td>0.172782</td>\n",
+ " <td>0.117776</td>\n",
+ " <td>0.577303</td>\n",
+ " <td>0.720442</td>\n",
+ " <td>0.205507</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TODS</th>\n",
- " <td>0.462057</td>\n",
- " <td>0.809107</td>\n",
- " <td>0.747606</td>\n",
- " <td>0.492481</td>\n",
- " <td>0.187118</td>\n",
- " <td>0.538600</td>\n",
+ " <td>0.404043</td>\n",
+ " <td>0.740680</td>\n",
+ " <td>0.748147</td>\n",
+ " <td>0.437865</td>\n",
+ " <td>0.149799</td>\n",
+ " <td>0.603929</td>\n",
" </tr>\n",
" <tr>\n",
" <th>UCR</th>\n",
- " <td>0.465144</td>\n",
- " <td>0.889491</td>\n",
- " <td>0.460106</td>\n",
- " <td>0.480275</td>\n",
- " <td>0.536193</td>\n",
- " <td>0.177598</td>\n",
+ " <td>0.326989</td>\n",
+ " <td>0.853025</td>\n",
+ " <td>0.336356</td>\n",
+ " <td>0.374480</td>\n",
+ " <td>0.371525</td>\n",
+ " <td>0.230630</td>\n",
" </tr>\n",
" <tr>\n",
" <th>WSD</th>\n",
- " <td>0.082639</td>\n",
- " <td>0.662574</td>\n",
- " <td>0.112541</td>\n",
- " <td>0.128861</td>\n",
- " <td>0.009756</td>\n",
- " <td>0.006061</td>\n",
+ " <td>0.036128</td>\n",
+ " <td>0.746600</td>\n",
+ " <td>0.039999</td>\n",
+ " <td>0.074105</td>\n",
+ " <td>0.018010</td>\n",
+ " <td>0.054808</td>\n",
" </tr>\n",
" <tr>\n",
" <th>YAHOO</th>\n",
- " <td>0.435473</td>\n",
- " <td>0.746907</td>\n",
- " <td>0.491670</td>\n",
- " <td>0.466854</td>\n",
- " <td>0.414790</td>\n",
- " <td>0.019690</td>\n",
+ " <td>0.083868</td>\n",
+ " <td>0.877014</td>\n",
+ " <td>0.306932</td>\n",
+ " <td>0.135211</td>\n",
+ " <td>0.108753</td>\n",
+ " <td>0.038252</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@@ -6066,32 +6080,32 @@
"text/plain": [
" AUC-PR AUC-ROC VUS-PR Standard-F1 Recall Precision\n",
"group \n",
- "CATSv2 0.452377 0.724201 0.230658 0.507399 0.300878 0.997312\n",
- "Daphnet 0.033420 0.120788 0.035690 0.114305 0.000000 0.000000\n",
- "Exathlon 0.420990 0.704579 0.420845 0.476715 0.012750 0.164047\n",
- "IOPS 0.068365 0.547448 0.064609 0.119151 0.073274 0.007659\n",
- "LTDB 0.743642 0.864903 0.811737 0.743372 0.097710 0.847458\n",
- "MGAB 0.084411 0.693115 0.069759 0.229185 0.118095 0.150940\n",
- "MITDB 0.594424 0.947580 0.606652 0.600204 0.373862 0.613016\n",
- "MSL 0.409835 0.825987 0.459063 0.504202 0.145087 0.222222\n",
- "NAB 0.396635 0.660959 0.415046 0.478648 0.050599 0.205914\n",
- "NEK 0.379153 0.673130 0.357818 0.478314 0.001764 0.008547\n",
- "OPPORTUNITY 0.366866 0.757637 0.347881 0.434931 0.021827 0.071429\n",
- "Power 0.496250 0.737337 0.510537 0.566831 0.182667 0.708926\n",
- "SED 0.722020 0.989946 0.907086 0.780849 0.655488 0.687006\n",
- "SMAP 0.601232 0.767636 0.609905 0.593002 0.559008 0.360987\n",
- "SMD 0.131250 0.655848 0.122593 0.186660 0.095875 0.110133\n",
- "SVDB 0.798760 0.984325 0.810962 0.764633 0.659443 0.751826\n",
- "SWaT 0.458896 0.799756 0.381310 0.629526 0.000000 0.000000\n",
- "Stock 0.095333 0.529748 0.717890 0.159066 0.000479 0.010490\n",
- "TAO 0.103834 0.517935 0.721004 0.172782 0.000000 0.000000\n",
- "TODS 0.462057 0.809107 0.747606 0.492481 0.187118 0.538600\n",
- "UCR 0.465144 0.889491 0.460106 0.480275 0.536193 0.177598\n",
- "WSD 0.082639 0.662574 0.112541 0.128861 0.009756 0.006061\n",
- "YAHOO 0.435473 0.746907 0.491670 0.466854 0.414790 0.019690"
+ "CATSv2 0.369080 0.706380 0.311384 0.426982 0.040811 0.971061\n",
+ "Daphnet 0.066512 0.507790 0.070139 0.116764 0.002168 0.024038\n",
+ "Exathlon 0.556786 0.830684 0.557891 0.577401 0.019985 0.356978\n",
+ "IOPS 0.051207 0.663177 0.067672 0.109625 0.075638 0.125516\n",
+ "LTDB 0.634659 0.796257 0.692470 0.638340 0.086590 0.838906\n",
+ "MGAB 0.032883 0.537176 0.007014 0.084602 0.000000 0.000000\n",
+ "MITDB 0.355826 0.856398 0.351133 0.435382 0.217145 0.299381\n",
+ "MSL 0.239125 0.739126 0.276927 0.362317 0.109275 0.105778\n",
+ "NAB 0.343854 0.666136 0.376638 0.402922 0.072529 0.276158\n",
+ "NEK 0.360131 0.710666 0.367261 0.432511 0.031221 0.325397\n",
+ "OPPORTUNITY 0.147631 0.661025 0.148914 0.228057 0.002064 0.021490\n",
+ "Power 0.215936 0.652180 0.225393 0.237451 0.000000 0.000000\n",
+ "SED 0.187850 0.610291 0.164910 0.220954 0.000000 0.000000\n",
+ "SMAP 0.487651 0.861212 0.565618 0.563891 0.546802 0.338855\n",
+ "SMD 0.122192 0.744937 0.115908 0.182900 0.069495 0.096921\n",
+ "SVDB 0.547080 0.930221 0.592468 0.598525 0.304473 0.520652\n",
+ "SWaT 0.159515 0.518403 0.159981 0.309398 0.000000 0.000000\n",
+ "Stock 0.097345 0.542291 0.717377 0.170772 0.000489 0.036376\n",
+ "TAO 0.117776 0.577303 0.720442 0.205507 0.000000 0.000000\n",
+ "TODS 0.404043 0.740680 0.748147 0.437865 0.149799 0.603929\n",
+ "UCR 0.326989 0.853025 0.336356 0.374480 0.371525 0.230630\n",
+ "WSD 0.036128 0.746600 0.039999 0.074105 0.018010 0.054808\n",
+ "YAHOO 0.083868 0.877014 0.306932 0.135211 0.108753 0.038252"
]
},
- "execution_count": 28,
+ "execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
@@ -6104,40 +6118,40 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"group\n",
- "Daphnet 0.033420\n",
- "IOPS 0.068365\n",
- "WSD 0.082639\n",
- "MGAB 0.084411\n",
- "Stock 0.095333\n",
- "TAO 0.103834\n",
- "SMD 0.131250\n",
- "OPPORTUNITY 0.366866\n",
- "NEK 0.379153\n",
- "NAB 0.396635\n",
- "MSL 0.409835\n",
- "Exathlon 0.420990\n",
- "YAHOO 0.435473\n",
- "CATSv2 0.452377\n",
- "SWaT 0.458896\n",
- "TODS 0.462057\n",
- "UCR 0.465144\n",
- "Power 0.496250\n",
- "MITDB 0.594424\n",
- "SMAP 0.601232\n",
- "SED 0.722020\n",
- "LTDB 0.743642\n",
- "SVDB 0.798760\n",
+ "MGAB 0.032883\n",
+ "WSD 0.036128\n",
+ "IOPS 0.051207\n",
+ "Daphnet 0.066512\n",
+ "YAHOO 0.083868\n",
+ "Stock 0.097345\n",
+ "TAO 0.117776\n",
+ "SMD 0.122192\n",
+ "OPPORTUNITY 0.147631\n",
+ "SWaT 0.159515\n",
+ "SED 0.187850\n",
+ "Power 0.215936\n",
+ "MSL 0.239125\n",
+ "UCR 0.326989\n",
+ "NAB 0.343854\n",
+ "MITDB 0.355826\n",
+ "NEK 0.360131\n",
+ "CATSv2 0.369080\n",
+ "TODS 0.404043\n",
+ "SMAP 0.487651\n",
+ "SVDB 0.547080\n",
+ "Exathlon 0.556786\n",
+ "LTDB 0.634659\n",
"Name: AUC-PR, dtype: float64"
]
},
- "execution_count": 29,
+ "execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
@@ -6149,40 +6163,40 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"group\n",
- "Daphnet 0.035690\n",
- "IOPS 0.064609\n",
- "MGAB 0.069759\n",
- "WSD 0.112541\n",
- "SMD 0.122593\n",
- "CATSv2 0.230658\n",
- "OPPORTUNITY 0.347881\n",
- "NEK 0.357818\n",
- "SWaT 0.381310\n",
- "NAB 0.415046\n",
- "Exathlon 0.420845\n",
- "MSL 0.459063\n",
- "UCR 0.460106\n",
- "YAHOO 0.491670\n",
- "Power 0.510537\n",
- "MITDB 0.606652\n",
- "SMAP 0.609905\n",
- "Stock 0.717890\n",
- "TAO 0.721004\n",
- "TODS 0.747606\n",
- "SVDB 0.810962\n",
- "LTDB 0.811737\n",
- "SED 0.907086\n",
+ "MGAB 0.007014\n",
+ "WSD 0.039999\n",
+ "IOPS 0.067672\n",
+ "Daphnet 0.070139\n",
+ "SMD 0.115908\n",
+ "OPPORTUNITY 0.148914\n",
+ "SWaT 0.159981\n",
+ "SED 0.164910\n",
+ "Power 0.225393\n",
+ "MSL 0.276927\n",
+ "YAHOO 0.306932\n",
+ "CATSv2 0.311384\n",
+ "UCR 0.336356\n",
+ "MITDB 0.351133\n",
+ "NEK 0.367261\n",
+ "NAB 0.376638\n",
+ "Exathlon 0.557891\n",
+ "SMAP 0.565618\n",
+ "SVDB 0.592468\n",
+ "LTDB 0.692470\n",
+ "Stock 0.717377\n",
+ "TAO 0.720442\n",
+ "TODS 0.748147\n",
"Name: VUS-PR, dtype: float64"
]
},
- "execution_count": 30,
+ "execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
diff --git a/src/models/desi/test_hyperparameters.ipynb b/src/models/desi/test_hyperparameters.ipynb
index 12eb203b8030991ede5c2e9d36b03d07739b9648..7f77986a8a34715fd100b72f56e49a22a9fcc28b 100644
--- a/src/models/desi/test_hyperparameters.ipynb
+++ b/src/models/desi/test_hyperparameters.ipynb
@@ -2,18 +2,9 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 9,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CUDA available: True\n",
- "cuDNN version: 90100\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import numpy as np\n",
"\n",
@@ -23,37 +14,37 @@
"from src.utils.slidingWindows import find_length_rank\n",
"from src.run_model_wrapper import preprocess_data\n",
"from src.evaluation import get_metrics\n",
- "from SAND import SAND"
+ "from OCSVM import OCSVM"
]
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"#Hyperparamater\n",
- "params = {'periodicity': 1}"
+ "params = {'periodicity': 2, 'kernel': 'rbf'}"
]
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"#Fit Funktion\n",
- "def run_KShapeAD(data, periodicity=1):\n",
- " slidingWindow = find_length_rank(data, rank=periodicity)\n",
- " clf = SAND(pattern_length=slidingWindow, subsequence_length=4*(slidingWindow))\n",
- " clf.fit(X=data.squeeze(),online=False, overlaping_rate=int(1.5*slidingWindow))\n",
- " score = clf.decision_scores_\n",
+ "def run_Sub_OCSVM(data_train, data_test, kernel='rbf', nu=0.5, periodicity=1, n_jobs=1):\n",
+ " slidingWindow = find_length_rank(data_test, rank=periodicity)\n",
+ " clf = OCSVM(slidingWindow=slidingWindow, kernel=kernel, nu=nu)\n",
+ " clf.fit(data_train)\n",
+ " score = clf.decision_function(data_test)\n",
" return score.ravel()"
]
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
@@ -63,7 +54,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
@@ -73,7 +64,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
@@ -87,46 +78,6 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
"c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
]
@@ -142,30 +93,6 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
"c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
]
@@ -181,34 +108,6 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
- " warnings.warn(\n",
"c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
]
@@ -226,8 +125,8 @@
" label = np.array(time_series['label'])\n",
" train_data = time_series['train_data']\n",
" \n",
- " #scores = run_LOF(train_data,data, **params)\n",
- " scores = run_KShapeAD(data, **params)\n",
+ " scores = run_Sub_OCSVM(train_data,data, **params) #semi-supervised\n",
+ " #scores = run_KShapeAD(data, **params) #unsupervised\n",
"\n",
" slidingWindow = time_series['sliding_window']\n",
" metrics = get_metrics(scores, labels=label, slidingWindow=slidingWindow)\n",
@@ -236,18 +135,18 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Datei: 257_TAO_id_1_Environment_tr_500_1st_3.csv, AUC-PR: 0.0983782436258824, VUS-PR: 0.8871743329644867\n",
- "Datei: 258_TAO_id_2_Environment_tr_500_1st_4.csv, AUC-PR: 0.14906529874605562, VUS-PR: 0.9406478652985084\n",
- "Datei: 259_TAO_id_3_Environment_tr_500_1st_7.csv, AUC-PR: 0.04592060635184418, VUS-PR: 0.2505436799139267\n",
- "Durchschnittlicher AUC-PR: 0.09778804957459407\n",
- "Durchschnittlicher VUS-PR: 0.6927886260589741\n"
+ "Datei: 257_TAO_id_1_Environment_tr_500_1st_3.csv, AUC-PR: 0.12245353442042406, VUS-PR: 0.904907986376071\n",
+ "Datei: 258_TAO_id_2_Environment_tr_500_1st_4.csv, AUC-PR: 0.15662321342792412, VUS-PR: 0.9449252771845215\n",
+ "Datei: 259_TAO_id_3_Environment_tr_500_1st_7.csv, AUC-PR: 0.054974065991650625, VUS-PR: 0.34755231499773087\n",
+ "Durchschnittlicher AUC-PR: 0.1113502712799996\n",
+ "Durchschnittlicher VUS-PR: 0.7324618595194411\n"
]
}
],
@@ -269,7 +168,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 16,
"metadata": {},
"outputs": [
{
@@ -278,7 +177,7 @@
"dict_keys(['AUC-PR', 'AUC-ROC', 'VUS-PR', 'VUS-ROC', 'Standard-F1', 'PA-F1', 'Event-based-F1', 'R-based-F1', 'Affiliation-F', 'Precision', 'Recall'])"
]
},
- "execution_count": 8,
+ "execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}