From c8936d64ab451863073e88b4d0f6905800874c89 Mon Sep 17 00:00:00 2001
From: DesireeWyrzylala <74858504+DesireeWyrzylala@users.noreply.github.com>
Date: Thu, 20 Feb 2025 15:15:45 +0100
Subject: [PATCH] comparing differenz between results in TAO plot pearson all,
 sequence and point anomaly create all missing plots create all missing plots
 for anomaly types

---
 .../evaluation/abweichung_tao_datensatz.png   |   Bin 0 -> 52989 bytes
 bilder/evaluation/{ => gesamt}/auc-pr.png     |   Bin
 bilder/evaluation/{ => gesamt}/f1.png         |   Bin
 .../gesamt/heatmap_with_numbers_auc-pr.png    |   Bin 0 -> 148525 bytes
 .../gesamt/heatmap_with_numbers_vus-pr.png    |   Bin 0 -> 146677 bytes
 .../gesamt/heatmap_without_numbers_auc-pr.png |   Bin 0 -> 53836 bytes
 .../gesamt/heatmap_without_numbers_vus-pr.png |   Bin 0 -> 54177 bytes
 bilder/evaluation/{ => gesamt}/precision.png  |   Bin
 bilder/evaluation/{ => gesamt}/recall.png     |   Bin
 bilder/evaluation/{ => gesamt}/runtime.png    |   Bin
 bilder/evaluation/{ => gesamt}/vus-pr.png     |   Bin
 .../heatmap_with_numbers_auc-pr.png           |   Bin 150157 -> 0 bytes
 .../heatmap_with_numbers_vus-pr.png           |   Bin 147865 -> 0 bytes
 .../heatmap_without_numbers_auc-pr.png        |   Bin 55185 -> 0 bytes
 .../heatmap_without_numbers_vus-pr.png        |   Bin 55301 -> 0 bytes
 .../anomaly_ratio_to_performace_auc-pr.png    |   Bin 0 -> 248905 bytes
 .../anomaly_ratio_to_performace_vus-pr.png    |   Bin 0 -> 256076 bytes
 ...to_performace_with_dataset_size_auc_pr.png |   Bin 0 -> 288899 bytes
 ...to_performace_with_dataset_size_vus_pr.png |   Bin 0 -> 292870 bytes
 .../pearson/all/pearson_vergleich_auc-pr.png  |   Bin 0 -> 63128 bytes
 .../pearson/all/pearson_vergleich_vus-pr.png  |   Bin 0 -> 63962 bytes
 ...omaly_ratio_to_performace_auc-pr_point.png |   Bin 0 -> 136018 bytes
 ...omaly_ratio_to_performace_vus-pr_point.png |   Bin 0 -> 165429 bytes
 ...formace_with_dataset_size_auc_pr_point.png |   Bin 0 -> 188513 bytes
 ...formace_with_dataset_size_vus_pr_point.png |   Bin 0 -> 208854 bytes
 .../point/pearson_vergleich_auc-pr_point.png  |   Bin 0 -> 61647 bytes
 .../point/pearson_vergleich_vus-pr_point.png  |   Bin 0 -> 65506 bytes
 ...anomaly_ratio_to_performace_auc-pr_seq.png |   Bin 0 -> 234183 bytes
 ...anomaly_ratio_to_performace_vus-pr_seq.png |   Bin 0 -> 233958 bytes
 ...erformace_with_dataset_size_auc_pr_seq.png |   Bin 0 -> 267319 bytes
 ...erformace_with_dataset_size_vus_pr_seq.png |   Bin 0 -> 266561 bytes
 .../seq/pearson_vergleich_auc-pr_seq.png      |   Bin 0 -> 62121 bytes
 .../seq/pearson_vergleich_vus-pr_seq.png      |   Bin 0 -> 62205 bytes
 .../punktanomalien/auc-pr_point.png           |   Bin 0 -> 67119 bytes
 .../heatmap_auc_pr_no_numbers_point.png       |   Bin 0 -> 40212 bytes
 .../heatmap_auc_pr_numbers_point.png          |   Bin 0 -> 65251 bytes
 .../heatmap_vus_pr_no_umbers_point.png        |   Bin 0 -> 40098 bytes
 .../heatmap_vus_pr_numbers_point.png          |   Bin 0 -> 67680 bytes
 .../punktanomalien/vus-pr_point.png           |   Bin 0 -> 70485 bytes
 .../sequenzanomalien/auc-pr_seq.png           |   Bin 0 -> 69348 bytes
 .../heatmeap_auc-pr_no_numbers_seq.png        |   Bin 0 -> 58866 bytes
 .../heatmeap_auc-pr_numbers_seq.png           |   Bin 0 -> 136818 bytes
 .../heatmeap_vus-pr_no_umbers_seq.png         |   Bin 0 -> 58497 bytes
 .../heatmeap_vus-pr_numbers_seq.png           |   Bin 0 -> 136103 bytes
 .../sequenzanomalien/vus-pr_seq.png           |   Bin 0 -> 70233 bytes
 docs/TO DO LISTE.md                           |    34 +-
 ...der Modelle im Vergleich zu den Autoren.md |    72 +-
 docs/evaluation/Reproducibility.md            |     2 +-
 .../AUC-PR_point_combined_by_group.csv        |    15 +
 .../AUC-PR_seq_combined_by_group.csv          |    15 +
 .../VUS-PR_point_combined_by_group.csv        |    15 +
 .../VUS-PR_seq_combined_by_group.csv          |    15 +
 .../pearson_correlation_coeffizient.ipynb     |  4719 +++++
 ...ined_dataframe_with_metrics_and_plot.ipynb | 16144 +++++-----------
 src/group_evaluation/create_heatmap.ipynb     |  2133 +-
 .../generate_combined_dataframe.ipynb         |   257 +-
 src/group_evaluation/tao_barplot.ipynb        |   920 +
 57 files changed, 11473 insertions(+), 12868 deletions(-)
 create mode 100644 bilder/evaluation/abweichung_tao_datensatz.png
 rename bilder/evaluation/{ => gesamt}/auc-pr.png (100%)
 rename bilder/evaluation/{ => gesamt}/f1.png (100%)
 create mode 100644 bilder/evaluation/gesamt/heatmap_with_numbers_auc-pr.png
 create mode 100644 bilder/evaluation/gesamt/heatmap_with_numbers_vus-pr.png
 create mode 100644 bilder/evaluation/gesamt/heatmap_without_numbers_auc-pr.png
 create mode 100644 bilder/evaluation/gesamt/heatmap_without_numbers_vus-pr.png
 rename bilder/evaluation/{ => gesamt}/precision.png (100%)
 rename bilder/evaluation/{ => gesamt}/recall.png (100%)
 rename bilder/evaluation/{ => gesamt}/runtime.png (100%)
 rename bilder/evaluation/{ => gesamt}/vus-pr.png (100%)
 delete mode 100644 bilder/evaluation/heatmap_with_numbers_auc-pr.png
 delete mode 100644 bilder/evaluation/heatmap_with_numbers_vus-pr.png
 delete mode 100644 bilder/evaluation/heatmap_without_numbers_auc-pr.png
 delete mode 100644 bilder/evaluation/heatmap_without_numbers_vus-pr.png
 create mode 100644 bilder/evaluation/pearson/all/anomaly_ratio_to_performace_auc-pr.png
 create mode 100644 bilder/evaluation/pearson/all/anomaly_ratio_to_performace_vus-pr.png
 create mode 100644 bilder/evaluation/pearson/all/anomaly_ratio_to_performace_with_dataset_size_auc_pr.png
 create mode 100644 bilder/evaluation/pearson/all/anomaly_ratio_to_performace_with_dataset_size_vus_pr.png
 create mode 100644 bilder/evaluation/pearson/all/pearson_vergleich_auc-pr.png
 create mode 100644 bilder/evaluation/pearson/all/pearson_vergleich_vus-pr.png
 create mode 100644 bilder/evaluation/pearson/point/anomaly_ratio_to_performace_auc-pr_point.png
 create mode 100644 bilder/evaluation/pearson/point/anomaly_ratio_to_performace_vus-pr_point.png
 create mode 100644 bilder/evaluation/pearson/point/anomaly_ratio_to_performace_with_dataset_size_auc_pr_point.png
 create mode 100644 bilder/evaluation/pearson/point/anomaly_ratio_to_performace_with_dataset_size_vus_pr_point.png
 create mode 100644 bilder/evaluation/pearson/point/pearson_vergleich_auc-pr_point.png
 create mode 100644 bilder/evaluation/pearson/point/pearson_vergleich_vus-pr_point.png
 create mode 100644 bilder/evaluation/pearson/seq/anomaly_ratio_to_performace_auc-pr_seq.png
 create mode 100644 bilder/evaluation/pearson/seq/anomaly_ratio_to_performace_vus-pr_seq.png
 create mode 100644 bilder/evaluation/pearson/seq/anomaly_ratio_to_performace_with_dataset_size_auc_pr_seq.png
 create mode 100644 bilder/evaluation/pearson/seq/anomaly_ratio_to_performace_with_dataset_size_vus_pr_seq.png
 create mode 100644 bilder/evaluation/pearson/seq/pearson_vergleich_auc-pr_seq.png
 create mode 100644 bilder/evaluation/pearson/seq/pearson_vergleich_vus-pr_seq.png
 create mode 100644 bilder/evaluation/punktanomalien/auc-pr_point.png
 create mode 100644 bilder/evaluation/punktanomalien/heatmap_auc_pr_no_numbers_point.png
 create mode 100644 bilder/evaluation/punktanomalien/heatmap_auc_pr_numbers_point.png
 create mode 100644 bilder/evaluation/punktanomalien/heatmap_vus_pr_no_umbers_point.png
 create mode 100644 bilder/evaluation/punktanomalien/heatmap_vus_pr_numbers_point.png
 create mode 100644 bilder/evaluation/punktanomalien/vus-pr_point.png
 create mode 100644 bilder/evaluation/sequenzanomalien/auc-pr_seq.png
 create mode 100644 bilder/evaluation/sequenzanomalien/heatmeap_auc-pr_no_numbers_seq.png
 create mode 100644 bilder/evaluation/sequenzanomalien/heatmeap_auc-pr_numbers_seq.png
 create mode 100644 bilder/evaluation/sequenzanomalien/heatmeap_vus-pr_no_umbers_seq.png
 create mode 100644 bilder/evaluation/sequenzanomalien/heatmeap_vus-pr_numbers_seq.png
 create mode 100644 bilder/evaluation/sequenzanomalien/vus-pr_seq.png
 create mode 100644 docs/evaluation/groupedDataframes/AUC-PR_point_combined_by_group.csv
 create mode 100644 docs/evaluation/groupedDataframes/AUC-PR_seq_combined_by_group.csv
 create mode 100644 docs/evaluation/groupedDataframes/VUS-PR_point_combined_by_group.csv
 create mode 100644 docs/evaluation/groupedDataframes/VUS-PR_seq_combined_by_group.csv
 create mode 100644 src/datensatz/pearson_correlation_coeffizient.ipynb
 create mode 100644 src/group_evaluation/tao_barplot.ipynb

diff --git a/bilder/evaluation/abweichung_tao_datensatz.png b/bilder/evaluation/abweichung_tao_datensatz.png
new file mode 100644
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diff --git a/bilder/evaluation/auc-pr.png b/bilder/evaluation/gesamt/auc-pr.png
similarity index 100%
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diff --git a/bilder/evaluation/f1.png b/bilder/evaluation/gesamt/f1.png
similarity index 100%
rename from bilder/evaluation/f1.png
rename to bilder/evaluation/gesamt/f1.png
diff --git a/bilder/evaluation/gesamt/heatmap_with_numbers_auc-pr.png b/bilder/evaluation/gesamt/heatmap_with_numbers_auc-pr.png
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similarity index 100%
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similarity index 100%
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diff --git a/bilder/evaluation/pearson/all/anomaly_ratio_to_performace_with_dataset_size_auc_pr.png b/bilder/evaluation/pearson/all/anomaly_ratio_to_performace_with_dataset_size_auc_pr.png
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diff --git a/bilder/evaluation/pearson/all/anomaly_ratio_to_performace_with_dataset_size_vus_pr.png b/bilder/evaluation/pearson/all/anomaly_ratio_to_performace_with_dataset_size_vus_pr.png
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diff --git a/bilder/evaluation/pearson/point/anomaly_ratio_to_performace_with_dataset_size_auc_pr_point.png b/bilder/evaluation/pearson/point/anomaly_ratio_to_performace_with_dataset_size_auc_pr_point.png
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diff --git a/bilder/evaluation/pearson/point/pearson_vergleich_vus-pr_point.png b/bilder/evaluation/pearson/point/pearson_vergleich_vus-pr_point.png
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diff --git a/bilder/evaluation/pearson/seq/anomaly_ratio_to_performace_vus-pr_seq.png b/bilder/evaluation/pearson/seq/anomaly_ratio_to_performace_vus-pr_seq.png
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diff --git a/bilder/evaluation/pearson/seq/anomaly_ratio_to_performace_with_dataset_size_vus_pr_seq.png b/bilder/evaluation/pearson/seq/anomaly_ratio_to_performace_with_dataset_size_vus_pr_seq.png
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diff --git a/docs/TO DO LISTE.md b/docs/TO DO LISTE.md
index e5e7090..b35a80a 100644
--- a/docs/TO DO LISTE.md	
+++ b/docs/TO DO LISTE.md	
@@ -13,20 +13,24 @@
 8. Zwei der vier neuronalen Netze haben bessere Evaluations-metriken auf Sequenzanomalien, statt Punktanoamlien
 9. Probleme in der Reproducibility bei kShape, trotz gleicher Hyperparameter und Datensätze haben wir Fehler debuggen müssen.
 10. Probleme in der Reproducibility beim Testen der num_channels in CNN (stattdessen mit Lernrate getestet)
-11. Probleme bei der Reproducibility bei LOF: Optimale Hyperparameter stimmen nicht mit den getesten überein
+11. Probleme bei der Reproducibility bei LOF: Optimale Hyperparameter stimmen nicht mit den getesten überein $\rightarrow$ gelöst
 
 # TO DO'S
-+ Datensätze genauer untersuchen
-	+ Auffälligkeiten (Anomalien erkennbar?, Punkt- oder Sequenzanomalien, Eigenschaften)
-	+ Warum könnten bestimmte Modelle auf einigen Datensätze Vorteile haben?
-	+ Verstehen warum es allgemein eine Steigerung der Performanz gibt, mit entweder der gleichen Parameterkonstellation, wie von den Autoren vorgegeben oder bessere Kombination durch das Tuning pro Zeitserie, aber gleichzeitig eine teils deutlich schlechtere Metrik bei einzelnen Datensätzen (z.B. Tao)
+- [ ] Datensätze genauer untersuchen
+	- [x] Auffälligkeiten (Anomalien erkennbar?, Punkt- oder Sequenzanomalien, Eigenschaften)
+	- [ ] Warum könnten bestimmte Modelle auf einigen Datensätze Vorteile haben?
+	- [x] Verstehen warum es allgemein eine Steigerung der Performanz gibt, mit entweder der gleichen Parameterkonstellation, wie von den Autoren vorgegeben oder bessere Kombination durch das Tuning pro Zeitserie, aber gleichzeitig eine teils deutlich schlechtere Metrik bei einzelnen Datensätzen (z.B. Tao)
 		+ Evtl. weil beste Hyperparameter anhand AUC-PR getestet wurden und nicht anhand VUS-PR, wie es vermutl. bei den Autoren gemacht wurde 
-			+ Heißt vergleichen der Hyperparameter dieser Datensätze mit den optimalen Hyperparametern der Autoren
-			+ Bei Ungleichheit der Hyperparameter, diese stark abweichenden Zeitserien nochmal einzeln trainieren und Ergebnisse vergleichen
-	+ Kann man anhand des Datensatzes und der Anomalien ableiten, welche Methoden (dichte-,distanz-, neuronal basiert) am besten funktionieren könnte?
+			- [x] Heißt vergleichen der Hyperparameter dieser Datensätze mit den optimalen Hyperparametern der Autoren
+			- [x] Bei Ungleichheit der Hyperparameter, diese stark abweichenden Zeitserien nochmal einzeln trainieren und Ergebnisse vergleichen
+	- [ ] Kann man anhand des Datensatzes und der Anomalien ableiten, welche Methoden (dichte-,distanz-, neuronal basiert) am besten funktionieren könnte?
+	- [ ] Messen der Abhängigkeit der Performanz von der Anomalie-Dichte mit PCC
+		- [ ] Gesamt
+		- [ ] Für Punktanomalien
+		- [ ] Für Sequenzanomalien
 	  ![](../bilder/dataset/comparing_datasets.png)
 	+ [https://thedatumorg.github.io/TSB-AD/](https://thedatumorg.github.io/TSB-AD/ "https://thedatumorg.github.io/TSB-AD/")
-	+ insb.:
+	- [x] insb.:
 		+ UCR
 		+ TAO
 		+ TODS
@@ -34,17 +38,17 @@
 		+ MGAB
 		+ Stock
 		+ SED
-+ VUS-PR besser verstehen
+- [ ] VUS-PR besser verstehen
 	+ Woher kommen Unterschiede zu AUC-PR, insb. bei Datensätzen mit Punktanomalien (TODS, TAO und Stock) ?
 	+ Wie funktioniert das?
 	+ Liegt ein Bias der Autoren?
-+ Ranking der Modelle nach Anomalietyp
+- [x] Ranking der Modelle nach Anomalietyp
 	+ Statistisch vs. Neuronal
 	+ Dichtebasiert vs. Distanzbasiert
-+ Ranking der Modelle allgemein
+- [x] Ranking der Modelle allgemein
 	+ nach Recall, AUC-PR und VUS-PR
 	+ 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
-+ Heatmaps für Anomalie-Typen erstellen
\ No newline at end of file
+- [ ] Ausarbeiten der Algorithmen und Vorbereiten für die Hausarbeit
+- [x] 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
+- [x] Heatmaps für Anomalie-Typen erstellen
\ No newline at end of file
diff --git a/docs/evaluation/Ranking der Modelle im Vergleich zu den Autoren.md b/docs/evaluation/Ranking der Modelle im Vergleich zu den Autoren.md
index 54a132d..53f1092 100644
--- a/docs/evaluation/Ranking der Modelle im Vergleich zu den Autoren.md	
+++ b/docs/evaluation/Ranking der Modelle im Vergleich zu den Autoren.md	
@@ -1,40 +1,42 @@
 
-| Platz nach Autoren | Model       | VUS-PR nach Autoren | AUC-PR | VUS-PR | Platz nach AUC-PR | Platz nach VUS-PR |
-| ------------------ | ----------- | ------------------- | ------ | ------ | ----------------- | ----------------- |
-| 1                  | Sub-PCA     | 0.42                | 0.407  | 0.444  | 2                 | 4                 |
-| 2                  | KShapeAD    | 0.40                | 0.383  | 0.423  | 4                 | 6                 |
-| 3                  | POLY        | 0.39                | 0.394  | 0.478  | 3                 | 2                 |
-| 4                  | KMeansAD    | 0.37                | 0.444  | 0.489  | 1                 | 1                 |
-| 5                  | USAD        | 0.36                | 0.352  | 0.375  | 8                 | 8                 |
-| 6                  | Sub-KNN     | 0.35                | 0.351  | 0.431  | 9                 | 5                 |
-| 7                  | CNN         | 0.34                | 0.366  | 0.367  | 6                 | 9                 |
-| 8                  | LSTMAD      | 0.33                | 0.313  | 0.320  | 11                | 13                |
-| 9                  | IForest     | 0.30                | 0.304  | 0.341  | 12                | 11                |
-| 10                 | OmniAnomaly | 0.29                | 0.354  | 0.377  | 7                 | 7                 |
-| 11                 | Sub-LOF     | 0.25                | 0.374  | 0.473  | 5                 | 3                 |
-| 12                 | Sub-OCSVM   | 0.23                | 0.275  | 0.339  | 13                | 12                |
-| 13                 | Sub-iForest | 0.22                | 0.326  | 0.362  | 10                | 10                |
-| 14                 | LOF         | 0.17                | 0.156  | 0.185  | 14                | 14                |
+| Platz nach Autoren | Model       | TYP            | VUS-PR nach Autoren | AUC-PR | VUS-PR | Platz nach AUC-PR | Platz nach VUS-PR |
+| ------------------ | ----------- | -------------- | ------------------- | ------ | ------ | ----------------- | ----------------- |
+| 4                  | KMeansAD    | Distanzbasiert | 0.37                | 0.444  | 0.489  | 1                 | 1                 |
+| 3                  | POLY        | Dichtebasiert  | 0.39                | 0.394  | 0.478  | 3                 | 2                 |
+| 11                 | Sub-LOF     | Distanzbasiert | 0.25                | 0.374  | 0.473  | 5                 | 3                 |
+| 1                  | Sub-PCA     | Dichtebasiert  | 0.42                | 0.407  | 0.444  | 2                 | 4                 |
+| 6                  | Sub-KNN     | Distanzbasiert | 0.35                | 0.351  | 0.431  | 9                 | 5                 |
+| 2                  | KShapeAD    | Distanzbasiert | 0.40                | 0.383  | 0.423  | 4                 | 6                 |
+| 10                 | OmniAnomaly | NN             | 0.29                | 0.354  | 0.377  | 7                 | 7                 |
+| 5                  | USAD        | NN             | 0.36                | 0.352  | 0.375  | 8                 | 8                 |
+| 7                  | CNN         | NN             | 0.34                | 0.366  | 0.367  | 6                 | 9                 |
+| 13                 | Sub-iForest | Dichtebasiert  | 0.22                | 0.326  | 0.362  | 10                | 10                |
+| 9                  | IForest     | Dichtebasiert  | 0.30                | 0.304  | 0.341  | 12                | 11                |
+| 12                 | Sub-OCSVM   | Dichtebasiert  | 0.23                | 0.275  | 0.339  | 13                | 12                |
+| 8                  | LSTMAD      | NN             | 0.33                | 0.313  | 0.320  | 11                | 13                |
+| 14                 | LOF         | Distanzbasiert | 0.17                | 0.156  | 0.185  | 14                | 14                |
 
 # Verbesserungen der Modelle
 
-| Model            | AUC-PR lt. Paper | AUC-PR | + AUC-PR \[%\]  | VUS-PR lt. Paper | VUS-PR | + VUS-PR \[%\]  |
-| ---------------- | ---------------- | ------ | --------------- | ---------------- | ------ | --------------- |
-| Sub-PCA          | 0.37             | 0.407  | 10              | 0.42             | 0.444  | 5.7             |
-| KShapeAD         | 0.35             | 0.383  | 9.4             | 0.40             | 0.423  | 5.8             |
-| POLY             | 0.31             | 0.394  | 27.1            | 0.39             | 0.478  | 22.6            |
-| KMeansAD         | 0.32             | 0.444  | 38.8            | 0.37             | 0.489  | 32.2            |
-| USAD             | 0.32             | 0.352  | 9.4             | 0.36             | 0.375  | 4.2             |
-| Sub-KNN V2       | 0.27             | 0.351  | 30              | 0.35             | 0.431  | 23.1            |
-| CNN              | 0.33             | 0.366  | 10.9            | 0.34             | 0.367  | 7.9             |
-| LSTMAD           | 0.31             | 0.313  | -               | 0.33             | 0.320  | 3.0             |
-| IForest          | 0.29             | 0.304  | 4.8             | 0.30             | 0.341  | 13.7            |
-| Sub-iForest      | 0.16             | 0.326  | 103.8           | 0.22             | 0.362  | 64.5            |
-| OmniAnomaly      | 0.27             | 0.354  | 31.1            | 0.29             | 0.377  | 30              |
-| Sub-LOF          | 0.16             | 0.374  | 133.8           | 0.25             | 0.473  | 89.2            |
-| Sub-OCSVM        | 0.16             | 0.275  | 71.9            | 0.23             | 0.339  | 47.4            |
-| LOF              | 0.14             | 0.156  | 11.4            | 0.17             | 0.185  | 8.8             |
-| **Durchschnitt** |                  |        | $\approx$ 35.17 |                  |        | $\approx$ 25.58 |
-| **Range**        |                  | 0.288  | 133.8           |                  | 0.304  | 86.2            |
-| *Alt*: Sub-KNN   | 0.27             | 0.292  | 8.15            | 0.35             | 0.384  | 9.7             |
+| Model       | Typ            | AUC-PR lt. Paper | AUC-PR | + AUC-PR \[%\] | VUS-PR lt. Paper | VUS-PR | + VUS-PR \[%\] |
+| ----------- | -------------- | ---------------- | ------ | -------------- | ---------------- | ------ | -------------- |
+| LSTMAD      | NN             | 0.31             | 0.313  | -              | 0.33             | 0.320  | 3.0            |
+| IForest     | Dichtebasiert  | 0.29             | 0.304  | 4.8            | 0.30             | 0.341  | 13.7           |
+| KShapeAD    | Distanzbasiert | 0.35             | 0.383  | 9.4            | 0.40             | 0.423  | 5.8            |
+| USAD        | NN             | 0.32             | 0.352  | 9.4            | 0.36             | 0.375  | 4.2            |
+| Sub-PCA     | Dichtebasiert  | 0.37             | 0.407  | 10             | 0.42             | 0.444  | 5.7            |
+| CNN         | NN             | 0.33             | 0.366  | 10.9           | 0.34             | 0.367  | 7.9            |
+| LOF         | Distanzbasiert | 0.14             | 0.156  | 11.4           | 0.17             | 0.185  | 8.8            |
+| POLY        | Dichtebasiert  | 0.31             | 0.394  | 27.1           | 0.39             | 0.478  | 22.6           |
+| Sub-KNN V2  | Distanzbasiert | 0.27             | 0.351  | 30             | 0.35             | 0.431  | 23.1           |
+| OmniAnomaly | NN             | 0.27             | 0.354  | 31.1           | 0.29             | 0.377  | 30             |
+| KMeansAD    | Distanzbasiert | 0.32             | 0.444  | 38.8           | 0.37             | 0.489  | 32.2           |
+| Sub-OCSVM   | Dichtebasiert  | 0.16             | 0.275  | 71.9           | 0.23             | 0.339  | 47.4           |
+| Sub-iForest | Dichtebasiert  | 0.16             | 0.326  | 103.8          | 0.22             | 0.362  | 64.5           |
+| Sub-LOF     | Distanzbasiert | 0.16             | 0.374  | 133.8          | 0.25             | 0.473  | 89.2           |
 
+| Model            | Typ | AUC-PR lt. Paper | AUC-PR | + AUC-PR \[%\]  | VUS-PR lt. Paper | VUS-PR | + VUS-PR \[%\]  |
+| ---------------- | --- | ---------------- | ------ | --------------- | ---------------- | ------ | --------------- |
+| **Durchschnitt** |     |                  |        | $\approx$ 35.17 |                  |        | $\approx$ 25.58 |
+| **Range**        |     |                  | 0.288  | 133.8           |                  | 0.304  | 86.2            |
+| *Alt*: Sub-KNN   |     | 0.27             | 0.292  | 8.15            | 0.35             | 0.384  | 9.7             |
diff --git a/docs/evaluation/Reproducibility.md b/docs/evaluation/Reproducibility.md
index 53ab3f4..b5d56e5 100644
--- a/docs/evaluation/Reproducibility.md
+++ b/docs/evaluation/Reproducibility.md
@@ -15,7 +15,7 @@
 - Insgesamt ließen sich die meisten Modelle sehr gut nachbauen
 - Probleme:
 	- KShapeAD: trotz gleicher Hyperparameter und Datensätze haben wir Fehler debuggen müssen, weil es bei der Erstellung von Subsequenzen zu leeren Ein-Dimensionalen Arrays kam anstelle Zwei-Dimensionaler Matrizen
-	- LOF: Optimale Hyperparameter stimmen nicht mit den getesteten überein im git der Autoren
+	- LOF: Optimale Hyperparameter stimmen nicht mit den getesteten überein im git der Autoren $\rightarrow$ Anpassung erfolgte am 19.0.2.2025
 	- CNN: Beim testen der von den Autoren vorgegebenen Hyperparameter traten Fehler beim Erstellen der Layer auf. Dabei diente ``num_channels`` als Parameter für die Anzahl an Neuronen pro Schicht. Dabei definiert die Länge des Arrays die Anzahl der Schichte. Stattdessen wurde die Lernrate optimiert.
 
 ### KShapeAD
diff --git a/docs/evaluation/groupedDataframes/AUC-PR_point_combined_by_group.csv b/docs/evaluation/groupedDataframes/AUC-PR_point_combined_by_group.csv
new file mode 100644
index 0000000..4c8ceb5
--- /dev/null
+++ b/docs/evaluation/groupedDataframes/AUC-PR_point_combined_by_group.csv
@@ -0,0 +1,15 @@
+,Model,UCR,TODS,YAHOO,Stock,WSD,IOPS,TAO
+0,CNN,0.000497723378635251,0.531847090272557,0.5870487483775556,0.9293442608636605,0.3429894916355166,0.2101571992823134,0.9934571481066872
+0,IForest,0.00013941436533938914,0.06926400176873043,0.0073118752517932,0.14393995693030376,0.00050124286842085,0.0118602696562059,0.1267629937143397
+0,KMeansAD_U,0.002842613470859922,0.08398913571818727,0.7425780755471775,0.11447566194398832,0.0030725693375156,0.0668858385771705,0.12190425607038209
+0,KShapeAD,0.0008537993370073427,0.0819347138465908,0.46443869003880583,0.09533344926608749,0.0011193602262089002,0.14848311466172803,0.10153367346721354
+0,LOF,0.0006526030557155189,0.07195861495361193,0.42251956376869465,0.11206072684081272,0.0038785364308283497,0.17671177891367676,0.12897242801673095
+0,LSTMAD,0.00022057345566046433,0.36733390067808275,0.4551026071964122,0.5192324559661586,0.3346376592791491,0.19708386202492015,0.9942179575616394
+0,OCSVM,0.0017944577746446659,0.07062529159246794,0.04008717142227338,0.09734473830824779,0.0021591481266237,0.02470129102881105,0.1104462272783257
+0,OmniAnomaly,0.00017818769461548108,0.06464966892067286,0.12257414572551374,0.13364632394088924,0.4072796148411686,0.01695232862278645,0.3368287431556052
+0,PCA,0.00019577781877805727,0.0672294969636395,0.060498290847143886,0.14842805522832,0.20665464316354404,0.036186030072219294,0.1604279183019481
+0,POLY,0.0002569736761072575,0.06186559240740803,0.02416109787000448,0.14659009081554358,0.16706583714438436,0.14165700182043814,0.1483558026065612
+0,Sub-IForest,0.0003518220525187502,0.06926400176873043,0.044400580493474395,0.14444613432568612,0.0005890488593567,0.01530736821825305,0.10525985700475156
+0,Sub-KNN,0.0030464039525634375,0.0794846514945064,0.20357487244711653,0.10329311049357655,0.00094523422250225,0.02422077926418335,0.11422736786977505
+0,Sub-LOF,0.04092722519239887,0.07777475979921483,0.1419869930251015,0.1150331890223664,0.3279579963301088,0.1216834122931069,0.14521154047355012
+0,USAD,0.00017806617844029428,0.06580947116928397,0.08766307603715748,0.13357031995417967,0.2857124052710794,0.016978599999277002,0.3300859310665809
diff --git a/docs/evaluation/groupedDataframes/AUC-PR_seq_combined_by_group.csv b/docs/evaluation/groupedDataframes/AUC-PR_seq_combined_by_group.csv
new file mode 100644
index 0000000..2510cce
--- /dev/null
+++ b/docs/evaluation/groupedDataframes/AUC-PR_seq_combined_by_group.csv
@@ -0,0 +1,15 @@
+,Model,MITDB,SWaT,NEK,IOPS,LTDB,Power,CATSv2,MSL,Daphnet,WSD,SED,YAHOO,SMD,SVDB,NAB,TODS,MGAB,OPPORTUNITY,SMAP,Exathlon
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diff --git a/docs/evaluation/groupedDataframes/VUS-PR_point_combined_by_group.csv b/docs/evaluation/groupedDataframes/VUS-PR_point_combined_by_group.csv
new file mode 100644
index 0000000..706b9ad
--- /dev/null
+++ b/docs/evaluation/groupedDataframes/VUS-PR_point_combined_by_group.csv
@@ -0,0 +1,15 @@
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diff --git a/docs/evaluation/groupedDataframes/VUS-PR_seq_combined_by_group.csv b/docs/evaluation/groupedDataframes/VUS-PR_seq_combined_by_group.csv
new file mode 100644
index 0000000..2af17bc
--- /dev/null
+++ b/docs/evaluation/groupedDataframes/VUS-PR_seq_combined_by_group.csv
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diff --git a/src/datensatz/pearson_correlation_coeffizient.ipynb b/src/datensatz/pearson_correlation_coeffizient.ipynb
new file mode 100644
index 0000000..20e1a01
--- /dev/null
+++ b/src/datensatz/pearson_correlation_coeffizient.ipynb
@@ -0,0 +1,4719 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import plotly.express as px\n",
+    "import os\n",
+    "import numpy as np\n",
+    "import plotly.graph_objects as go\n",
+    "import plotly.subplots as sp\n",
+    "from scipy.stats import pearsonr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Read all data and create dataframe\n",
+    "#Including\n",
+    "# Model Performanz\n",
+    "# Density of Anomaly per Dataset\n",
+    "# Dataset#\n",
+    "\n",
+    "data = {\n",
+    "\t'dataset': ['TAO','TODS','Stock','UCR','SMD','YAHOO','Exathlon','NAB','OPPORTUNITY','WSD','SVDB','SMAP','IOPS','MGAB',\n",
+    "\t\t\t 'MSL','NEK','LTDB','MITDB','SED','Daphnet','SWaT','Power','CATSv2'],\n",
+    "    'size': [3,15,10,76,38,35,32,28,28,25,20,19,17,9,9,9,9,8,3,1,1,1,1],\n",
+    "\t'anomalies': [0.094,0.088,0.064,0.003, 0.019, 0.009,0.109, 0.102,0.048, 0.005,0.037, 0.029,0.015,0.002,\n",
+    "\t\t\t    0.040,0.080,0.188,0.042,0.040,0.059,0.121,0.086,0.049 ]\n",
+    "}\n",
+    "\n",
+    "df = pd.DataFrame(data)\n",
+    "df.set_index('dataset', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 188,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>size</th>\n",
+       "      <th>anomalies</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dataset</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>TAO</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0.094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TODS</th>\n",
+       "      <td>15</td>\n",
+       "      <td>0.088</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Stock</th>\n",
+       "      <td>10</td>\n",
+       "      <td>0.064</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UCR</th>\n",
+       "      <td>76</td>\n",
+       "      <td>0.003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SMD</th>\n",
+       "      <td>38</td>\n",
+       "      <td>0.019</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         size  anomalies\n",
+       "dataset                 \n",
+       "TAO         3      0.094\n",
+       "TODS       15      0.088\n",
+       "Stock      10      0.064\n",
+       "UCR        76      0.003\n",
+       "SMD        38      0.019"
+      ]
+     },
+     "execution_count": 188,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 189,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#select evaluation metric and if not total dataset choose anomaly type\n",
+    "metric = 'AUC-PR'\n",
+    "anoType = 'seq'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 190,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found:  CNN.csv\n",
+      "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",
+      "Not correct file type:  Reproducibility.md\n",
+      "Found:  Sub-IForest.csv\n",
+      "Found:  Sub-KNN.csv\n",
+      "Found:  Sub-LOF.csv\n",
+      "Not correct file type:  temp\n",
+      "Found:  USAD.csv\n",
+      "Not correct file type:  Vergleich der Ergebnisse.md\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Insert Model Performanz for each Dataset\n",
+    "path = '../../docs/evaluation/'\n",
+    "filePath = '../../data/train/'\n",
+    "for file in os.listdir(path):\n",
+    "    suffix = file.split('.')\n",
+    "    if len(suffix) < 2 or suffix[1] != 'csv':\n",
+    "        print('Not correct file type: ', file)\n",
+    "        continue\n",
+    "    else:\n",
+    "        print('Found: ', file)\n",
+    "        df_tmp = pd.read_csv(path+file)\n",
+    "        #filter dataframe if special anomaly types are selected\n",
+    "        if anoType == 'point' or anoType == 'seq':\n",
+    "            testPath = os.path.join(filePath, anoType+'/')\n",
+    "            df_tmp = df_tmp[df_tmp['file_name'].apply(lambda x: os.path.isfile(os.path.join(testPath,x)))]\n",
+    "        df_tmp = df_tmp[['group',metric]]\n",
+    "        model = suffix[0]\n",
+    "        df[model] = np.nan\n",
+    "        #Group Dataframe and get mean for each group\n",
+    "        groupedMeans = df_tmp.groupby('group').mean()\n",
+    "        #add mean performanz for each dataset for each Model\n",
+    "        for key in df.index:\n",
+    "            if key in groupedMeans.index:\n",
+    "                value = groupedMeans.loc[key, metric]\n",
+    "                df.at[key, model] = value  \n",
+    "df.dropna(axis=0, inplace=True)      "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 191,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>size</th>\n",
+       "      <th>anomalies</th>\n",
+       "      <th>CNN</th>\n",
+       "      <th>IForest</th>\n",
+       "      <th>KMeansAD_U</th>\n",
+       "      <th>KShapeAD</th>\n",
+       "      <th>LOF</th>\n",
+       "      <th>LSTMAD</th>\n",
+       "      <th>OCSVM</th>\n",
+       "      <th>OmniAnomaly</th>\n",
+       "      <th>PCA</th>\n",
+       "      <th>POLY</th>\n",
+       "      <th>Sub-IForest</th>\n",
+       "      <th>Sub-KNN</th>\n",
+       "      <th>Sub-LOF</th>\n",
+       "      <th>USAD</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dataset</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>TODS</th>\n",
+       "      <td>15</td>\n",
+       "      <td>0.088</td>\n",
+       "      <td>0.206602</td>\n",
+       "      <td>0.071679</td>\n",
+       "      <td>0.592072</td>\n",
+       "      <td>0.627511</td>\n",
+       "      <td>0.081599</td>\n",
+       "      <td>0.170148</td>\n",
+       "      <td>0.557978</td>\n",
+       "      <td>0.191109</td>\n",
+       "      <td>0.149989</td>\n",
+       "      <td>0.281649</td>\n",
+       "      <td>0.079671</td>\n",
+       "      <td>0.561582</td>\n",
+       "      <td>0.240756</td>\n",
+       "      <td>0.184682</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SMD</th>\n",
+       "      <td>38</td>\n",
+       "      <td>0.019</td>\n",
+       "      <td>0.505749</td>\n",
+       "      <td>0.336128</td>\n",
+       "      <td>0.095816</td>\n",
+       "      <td>0.077411</td>\n",
+       "      <td>0.163313</td>\n",
+       "      <td>0.495482</td>\n",
+       "      <td>0.077154</td>\n",
+       "      <td>0.460181</td>\n",
+       "      <td>0.459753</td>\n",
+       "      <td>0.439343</td>\n",
+       "      <td>0.347267</td>\n",
+       "      <td>0.050575</td>\n",
+       "      <td>0.339639</td>\n",
+       "      <td>0.561216</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>YAHOO</th>\n",
+       "      <td>35</td>\n",
+       "      <td>0.009</td>\n",
+       "      <td>0.345384</td>\n",
+       "      <td>0.981990</td>\n",
+       "      <td>0.462352</td>\n",
+       "      <td>0.240316</td>\n",
+       "      <td>0.316546</td>\n",
+       "      <td>0.344485</td>\n",
+       "      <td>0.498439</td>\n",
+       "      <td>0.349465</td>\n",
+       "      <td>0.690088</td>\n",
+       "      <td>0.571428</td>\n",
+       "      <td>0.988424</td>\n",
+       "      <td>0.395078</td>\n",
+       "      <td>0.242005</td>\n",
+       "      <td>0.349449</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Exathlon</th>\n",
+       "      <td>32</td>\n",
+       "      <td>0.109</td>\n",
+       "      <td>0.654310</td>\n",
+       "      <td>0.962806</td>\n",
+       "      <td>0.617690</td>\n",
+       "      <td>0.420990</td>\n",
+       "      <td>0.245106</td>\n",
+       "      <td>0.733875</td>\n",
+       "      <td>0.556786</td>\n",
+       "      <td>0.874519</td>\n",
+       "      <td>0.958102</td>\n",
+       "      <td>0.844513</td>\n",
+       "      <td>0.959666</td>\n",
+       "      <td>0.596532</td>\n",
+       "      <td>0.257618</td>\n",
+       "      <td>0.862358</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NAB</th>\n",
+       "      <td>28</td>\n",
+       "      <td>0.102</td>\n",
+       "      <td>0.209869</td>\n",
+       "      <td>0.251317</td>\n",
+       "      <td>0.230029</td>\n",
+       "      <td>0.251877</td>\n",
+       "      <td>0.154619</td>\n",
+       "      <td>0.162982</td>\n",
+       "      <td>0.245686</td>\n",
+       "      <td>0.335713</td>\n",
+       "      <td>0.439685</td>\n",
+       "      <td>0.402974</td>\n",
+       "      <td>0.273290</td>\n",
+       "      <td>0.213866</td>\n",
+       "      <td>0.457316</td>\n",
+       "      <td>0.272908</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          size  anomalies       CNN   IForest  KMeansAD_U  KShapeAD       LOF  \\\n",
+       "dataset                                                                         \n",
+       "TODS        15      0.088  0.206602  0.071679    0.592072  0.627511  0.081599   \n",
+       "SMD         38      0.019  0.505749  0.336128    0.095816  0.077411  0.163313   \n",
+       "YAHOO       35      0.009  0.345384  0.981990    0.462352  0.240316  0.316546   \n",
+       "Exathlon    32      0.109  0.654310  0.962806    0.617690  0.420990  0.245106   \n",
+       "NAB         28      0.102  0.209869  0.251317    0.230029  0.251877  0.154619   \n",
+       "\n",
+       "            LSTMAD     OCSVM  OmniAnomaly       PCA      POLY  Sub-IForest  \\\n",
+       "dataset                                                                      \n",
+       "TODS      0.170148  0.557978     0.191109  0.149989  0.281649     0.079671   \n",
+       "SMD       0.495482  0.077154     0.460181  0.459753  0.439343     0.347267   \n",
+       "YAHOO     0.344485  0.498439     0.349465  0.690088  0.571428     0.988424   \n",
+       "Exathlon  0.733875  0.556786     0.874519  0.958102  0.844513     0.959666   \n",
+       "NAB       0.162982  0.245686     0.335713  0.439685  0.402974     0.273290   \n",
+       "\n",
+       "           Sub-KNN   Sub-LOF      USAD  \n",
+       "dataset                                 \n",
+       "TODS      0.561582  0.240756  0.184682  \n",
+       "SMD       0.050575  0.339639  0.561216  \n",
+       "YAHOO     0.395078  0.242005  0.349449  \n",
+       "Exathlon  0.596532  0.257618  0.862358  \n",
+       "NAB       0.213866  0.457316  0.272908  "
+      ]
+     },
+     "execution_count": 191,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Pearson-Korrelation\n",
+    "$r=\\frac{\\sum(x−xˉ)^2∑(y−yˉ​)^2}{​\\sum(x−xˉ)(y−yˉ​)}​$ mit\n",
+    "x = Anomaliedichte\n",
+    "y = Modellleistung\n",
+    "\n",
+    "**pearson_corr:**\n",
+    "+ Werte nahe 1 $\\rightarrow$ Starker positiver Zusammenhang (mehr Anomalien $\\rightarrow$ bessere Leistung).\n",
+    "+ Werte nahe -1 $\\rightarrow$ Starker negativer Zusammenhang (mehr Anomalien $\\rightarrow$ schlechtere Leistung).\n",
+    "+ Werte nahe 0 $\\rightarrow$ Kein Zusammenhang zwischen Anomaliedichte und Modellleistung.\n",
+    "\n",
+    "**p_value:**\n",
+    "+ p < 0.05 $\\rightarrow$ Der Zusammenhang ist statistisch signifikant.\n",
+    "+ p ≥ 0.05 $\\rightarrow$ Kein signifikanter Zusammenhang (der Effekt könnte zufällig sein)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 192,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Model': ['CNN', 'IForest', 'KMeansAD_U', 'KShapeAD', 'LOF', 'LSTMAD', 'OCSVM', 'OmniAnomaly', 'PCA', 'POLY', 'Sub-IForest', 'Sub-KNN', 'Sub-LOF', 'USAD'], 'Value': [0.2893924279234257, 0.21087664492090677, 0.24390101674459852, 0.39458773218844717, 0.15919844280822867, 0.27312506558292604, 0.3791185999506049, 0.24664471674994579, 0.19772443705906273, 0.03867569816887934, 0.22141826491204536, 0.18044056829081395, -0.21509439060351984, 0.21995644172523504]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "correlations = {\n",
+    "    'Model':[],\n",
+    "    'Value': []\n",
+    "}\n",
+    "for col in df.columns:\n",
+    "    if col != 'anomalies' and col != 'size':\n",
+    "        corr, _ = pearsonr(df['anomalies'], df[col])\n",
+    "        #model\n",
+    "        correlations['Model'].append(col)\n",
+    "        #value\n",
+    "        correlations['Value'].append(corr)\n",
+    "\n",
+    "print(correlations)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 193,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>CNN</td>\n",
+       "      <td>0.289392</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>IForest</td>\n",
+       "      <td>0.210877</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>KMeansAD_U</td>\n",
+       "      <td>0.243901</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>KShapeAD</td>\n",
+       "      <td>0.394588</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>LOF</td>\n",
+       "      <td>0.159198</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Model     Value\n",
+       "0         CNN  0.289392\n",
+       "1     IForest  0.210877\n",
+       "2  KMeansAD_U  0.243901\n",
+       "3    KShapeAD  0.394588\n",
+       "4         LOF  0.159198"
+      ]
+     },
+     "execution_count": 193,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_corr = pd.DataFrame(correlations)\n",
+    "df_corr.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 194,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colors = {\n",
+    "    'CNN': 'steelblue',\n",
+    "    'IForest':'darkmagenta' ,\n",
+    "    'KMeansAD_U': 'darkseagreen' ,\n",
+    "    'LOF':'darkgoldenrod',\n",
+    "    'LSTMAD': 'blueviolet' ,\n",
+    "    'OmniAnomaly': 'slateblue',\n",
+    "    'PCA': 'yellowgreen',\n",
+    "    'POLY': 'lightskyblue',\n",
+    "    'Sub-KNN': 'firebrick',#'gold' ,\n",
+    "    'USAD': 'mediumpurple',\n",
+    "    'Sub-LOF':'dodgerblue' ,\n",
+    "    'KShapeAD': 'darksalmon' ,\n",
+    "    'OCSVM': 'darkorange',\n",
+    "    'Sub-IForest': 'green'\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 195,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\plotly\\express\\_core.py:1985: FutureWarning:\n",
+      "\n",
+      "When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "alignmentgroup": "True",
+         "hovertemplate": "Model=%{x}<br>Value=%{y}<extra></extra>",
+         "legendgroup": "Sub-LOF",
+         "marker": {
+          "color": "dodgerblue",
+          "opacity": 0.8,
+          "pattern": {
+           "shape": ""
+          }
+         },
+         "name": "Sub-LOF",
+         "offsetgroup": "Sub-LOF",
+         "orientation": "v",
+         "showlegend": true,
+         "textposition": "auto",
+         "type": "bar",
+         "x": [
+          "Sub-LOF"
+         ],
+         "xaxis": "x",
+         "y": [
+          -0.21509439060351984
+         ],
+         "yaxis": "y"
+        },
+        {
+         "alignmentgroup": "True",
+         "hovertemplate": "Model=%{x}<br>Value=%{y}<extra></extra>",
+         "legendgroup": "POLY",
+         "marker": {
+          "color": "lightskyblue",
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+            "ticks": "outside"
+           }
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "showgrid": true,
+           "tickcolor": "rgb(51,51,51)",
+           "ticks": "outside",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white"
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "showgrid": true,
+           "tickcolor": "rgb(51,51,51)",
+           "ticks": "outside",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white"
+          }
+         }
+        },
+        "title": {
+         "text": "<b> Abhängigkeit der Modelleistung von der Anomaliedichte anhand des Pearson Korrelations Koeffizieten </b>",
+         "x": 0.5,
+         "xanchor": "center",
+         "y": 0.94,
+         "yanchor": "top"
+        },
+        "width": 1000,
+        "xaxis": {
+         "anchor": "y",
+         "categoryarray": [
+          "Sub-LOF",
+          "POLY",
+          "LOF",
+          "Sub-KNN",
+          "PCA",
+          "IForest",
+          "USAD",
+          "Sub-IForest",
+          "KMeansAD_U",
+          "OmniAnomaly",
+          "LSTMAD",
+          "CNN",
+          "OCSVM",
+          "KShapeAD"
+         ],
+         "categoryorder": "array",
+         "domain": [
+          0,
+          1
+         ],
+         "dtick": 1,
+         "tickangle": -20,
+         "title": {
+          "text": "Model"
+         }
+        },
+        "yaxis": {
+         "anchor": "x",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "Pearson Korrelations Koeffiziet"
+         }
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = px.bar(df_corr.sort_values(by='Value',ascending=True), x='Model', y='Value', color='Model',  color_discrete_map=colors,opacity=0.8)\n",
+    "fig.update_layout( legend=dict(\n",
+    "    x=-0.05,\n",
+    "    y=-0.15,\n",
+    "    orientation=\"h\",\n",
+    "    title_font_family=\"Times New Roman\",\n",
+    "    font=dict(\n",
+    "    family=\"Courier\",\n",
+    "    size=12,\n",
+    "    color=\"black\"\n",
+    "    ),\n",
+    "    title_text='Model',\n",
+    "    bgcolor=\"white\",\n",
+    "    bordercolor=\"lightgrey\",\n",
+    "    borderwidth=2,\n",
+    "    ),\n",
+    "    showlegend=False,\n",
+    "    template = 'ggplot2',\n",
+    "    title={\n",
+    "            'text': '<b> Abhängigkeit der Modelleistung von der Anomaliedichte anhand des Pearson Korrelations Koeffizieten </b>',\n",
+    "            'x':0.5,\n",
+    "            'y':0.94, \n",
+    "            'xanchor':'center',\n",
+    "            'yanchor':'top'},\n",
+    "    height=700,width=1000,\n",
+    "    )\n",
+    "fig.update_traces(marker=dict(size=12,\n",
+    "                    opacity=0.8,\n",
+    "                    line=dict(width=2,\n",
+    "                                    color='DarkSlateGrey'),\n",
+    "                            ),\n",
+    "            selector=dict(mode='markers'))\n",
+    "fig.update_xaxes( dtick = 1,tickangle = 340)\n",
+    "fig.update_yaxes( title='Pearson Korrelations Koeffiziet')\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Comparing performance with anomaly ration for each model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+             "size": 10,
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+            "type": "scatter3d"
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+          ],
+          "scattercarpet": [
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+            "type": "surface"
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+          "table": [
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+            "cells": {
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+              "color": "rgb(237,237,237)"
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+             "fill": {
+              "color": "rgb(217,217,217)"
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+             "line": {
+              "color": "white"
+             }
+            },
+            "type": "table"
+           }
+          ]
+         },
+         "layout": {
+          "annotationdefaults": {
+           "arrowhead": 0,
+           "arrowwidth": 1
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+          "autotypenumbers": "strict",
+          "coloraxis": {
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+            "tickcolor": "rgb(237,237,237)",
+            "ticklen": 6,
+            "ticks": "inside"
+           }
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+           "sequential": [
+            [
+             0,
+             "rgb(20,44,66)"
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+            [
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+             "rgb(90,179,244)"
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+          "colorway": [
+           "#F8766D",
+           "#A3A500",
+           "#00BF7D",
+           "#00B0F6",
+           "#E76BF3"
+          ],
+          "font": {
+           "color": "rgb(51,51,51)"
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+          "geo": {
+           "bgcolor": "white",
+           "lakecolor": "white",
+           "landcolor": "rgb(237,237,237)",
+           "showlakes": true,
+           "showland": true,
+           "subunitcolor": "white"
+          },
+          "hoverlabel": {
+           "align": "left"
+          },
+          "hovermode": "closest",
+          "paper_bgcolor": "white",
+          "plot_bgcolor": "rgb(237,237,237)",
+          "polar": {
+           "angularaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "showgrid": true,
+            "tickcolor": "rgb(51,51,51)",
+            "ticks": "outside"
+           },
+           "bgcolor": "rgb(237,237,237)",
+           "radialaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "showgrid": true,
+            "tickcolor": "rgb(51,51,51)",
+            "ticks": "outside"
+           }
+          },
+          "scene": {
+           "xaxis": {
+            "backgroundcolor": "rgb(237,237,237)",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "showgrid": true,
+            "tickcolor": "rgb(51,51,51)",
+            "ticks": "outside",
+            "zerolinecolor": "white"
+           },
+           "yaxis": {
+            "backgroundcolor": "rgb(237,237,237)",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "showgrid": true,
+            "tickcolor": "rgb(51,51,51)",
+            "ticks": "outside",
+            "zerolinecolor": "white"
+           },
+           "zaxis": {
+            "backgroundcolor": "rgb(237,237,237)",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "showgrid": true,
+            "tickcolor": "rgb(51,51,51)",
+            "ticks": "outside",
+            "zerolinecolor": "white"
+           }
+          },
+          "shapedefaults": {
+           "fillcolor": "black",
+           "line": {
+            "width": 0
+           },
+           "opacity": 0.3
+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "showgrid": true,
+            "tickcolor": "rgb(51,51,51)",
+            "ticks": "outside"
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "showgrid": true,
+            "tickcolor": "rgb(51,51,51)",
+            "ticks": "outside"
+           },
+           "bgcolor": "rgb(237,237,237)",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "showgrid": true,
+            "tickcolor": "rgb(51,51,51)",
+            "ticks": "outside"
+           }
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "showgrid": true,
+           "tickcolor": "rgb(51,51,51)",
+           "ticks": "outside",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white"
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "showgrid": true,
+           "tickcolor": "rgb(51,51,51)",
+           "ticks": "outside",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white"
+          }
+         }
+        },
+        "title": {
+         "text": "<b> Abhängigkeit der Modelleistung von der Anomaliedichte </b>",
+         "x": 0.5,
+         "xanchor": "center",
+         "y": 0.94,
+         "yanchor": "top"
+        },
+        "width": 1000,
+        "xaxis": {
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+         "domain": [
+          0,
+          0.32666666666666666
+         ],
+         "matches": "x13",
+         "showticklabels": false,
+         "tickfont": {
+          "family": "Arial",
+          "size": 12
+         },
+         "title": {
+          "font": {
+           "family": "Arial",
+           "size": 14
+          }
+         }
+        },
+        "xaxis10": {
+         "anchor": "y10",
+         "domain": [
+          0,
+          0.32666666666666666
+         ],
+         "matches": "x13",
+         "showticklabels": false
+        },
+        "xaxis11": {
+         "anchor": "y11",
+         "domain": [
+          0.33666666666666667,
+          0.6633333333333333
+         ],
+         "matches": "x14",
+         "showticklabels": false
+        },
+        "xaxis12": {
+         "anchor": "y12",
+         "domain": [
+          0.6733333333333333,
+          1
+         ],
+         "matches": "x15",
+         "showticklabels": false
+        },
+        "xaxis13": {
+         "anchor": "y13",
+         "domain": [
+          0,
+          0.32666666666666666
+         ]
+        },
+        "xaxis14": {
+         "anchor": "y14",
+         "domain": [
+          0.33666666666666667,
+          0.6633333333333333
+         ],
+         "title": {
+          "text": "Anomaliedichte"
+         }
+        },
+        "xaxis15": {
+         "anchor": "y15",
+         "domain": [
+          0.6733333333333333,
+          1
+         ]
+        },
+        "xaxis2": {
+         "anchor": "y2",
+         "domain": [
+          0.33666666666666667,
+          0.6633333333333333
+         ],
+         "matches": "x14",
+         "showticklabels": false
+        },
+        "xaxis3": {
+         "anchor": "y3",
+         "domain": [
+          0.6733333333333333,
+          1
+         ],
+         "matches": "x15",
+         "showticklabels": false
+        },
+        "xaxis4": {
+         "anchor": "y4",
+         "domain": [
+          0,
+          0.32666666666666666
+         ],
+         "matches": "x13",
+         "showticklabels": false
+        },
+        "xaxis5": {
+         "anchor": "y5",
+         "domain": [
+          0.33666666666666667,
+          0.6633333333333333
+         ],
+         "matches": "x14",
+         "showticklabels": false
+        },
+        "xaxis6": {
+         "anchor": "y6",
+         "domain": [
+          0.6733333333333333,
+          1
+         ],
+         "matches": "x15",
+         "showticklabels": false
+        },
+        "xaxis7": {
+         "anchor": "y7",
+         "domain": [
+          0,
+          0.32666666666666666
+         ],
+         "matches": "x13",
+         "showticklabels": false
+        },
+        "xaxis8": {
+         "anchor": "y8",
+         "domain": [
+          0.33666666666666667,
+          0.6633333333333333
+         ],
+         "matches": "x14",
+         "showticklabels": false
+        },
+        "xaxis9": {
+         "anchor": "y9",
+         "domain": [
+          0.6733333333333333,
+          1
+         ],
+         "matches": "x15",
+         "showticklabels": false
+        },
+        "yaxis": {
+         "anchor": "x",
+         "domain": [
+          0.82,
+          1
+         ],
+         "tickfont": {
+          "family": "Arial",
+          "size": 12
+         },
+         "title": {
+          "font": {
+           "family": "Arial",
+           "size": 14
+          }
+         }
+        },
+        "yaxis10": {
+         "anchor": "x10",
+         "domain": [
+          0.205,
+          0.385
+         ]
+        },
+        "yaxis11": {
+         "anchor": "x11",
+         "domain": [
+          0.205,
+          0.385
+         ],
+         "matches": "y10",
+         "showticklabels": false
+        },
+        "yaxis12": {
+         "anchor": "x12",
+         "domain": [
+          0.205,
+          0.385
+         ],
+         "matches": "y10",
+         "showticklabels": false
+        },
+        "yaxis13": {
+         "anchor": "x13",
+         "domain": [
+          0,
+          0.18
+         ]
+        },
+        "yaxis14": {
+         "anchor": "x14",
+         "domain": [
+          0,
+          0.18
+         ],
+         "matches": "y13",
+         "showticklabels": false
+        },
+        "yaxis15": {
+         "anchor": "x15",
+         "domain": [
+          0,
+          0.18
+         ],
+         "matches": "y13",
+         "showticklabels": false
+        },
+        "yaxis2": {
+         "anchor": "x2",
+         "domain": [
+          0.82,
+          1
+         ],
+         "matches": "y",
+         "showticklabels": false
+        },
+        "yaxis3": {
+         "anchor": "x3",
+         "domain": [
+          0.82,
+          1
+         ],
+         "matches": "y",
+         "showticklabels": false
+        },
+        "yaxis4": {
+         "anchor": "x4",
+         "domain": [
+          0.615,
+          0.7949999999999999
+         ]
+        },
+        "yaxis5": {
+         "anchor": "x5",
+         "domain": [
+          0.615,
+          0.7949999999999999
+         ],
+         "matches": "y4",
+         "showticklabels": false
+        },
+        "yaxis6": {
+         "anchor": "x6",
+         "domain": [
+          0.615,
+          0.7949999999999999
+         ],
+         "matches": "y4",
+         "showticklabels": false
+        },
+        "yaxis7": {
+         "anchor": "x7",
+         "domain": [
+          0.41,
+          0.59
+         ],
+         "title": {
+          "text": "Modellleistung"
+         }
+        },
+        "yaxis8": {
+         "anchor": "x8",
+         "domain": [
+          0.41,
+          0.59
+         ],
+         "matches": "y7",
+         "showticklabels": false
+        },
+        "yaxis9": {
+         "anchor": "x9",
+         "domain": [
+          0.41,
+          0.59
+         ],
+         "matches": "y7",
+         "showticklabels": false
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# list of models\n",
+    "models = [col for col in df.columns if col != 'anomalies' and col != 'size']\n",
+    "\n",
+    "# create plot with subplots for each model\n",
+    "fig = sp.make_subplots(rows=5, cols=3, subplot_titles=models,shared_xaxes=True, shared_yaxes=True, vertical_spacing=0.025, horizontal_spacing=0.01)\n",
+    "\n",
+    "row, col = 1, 1\n",
+    "for model in models:\n",
+    "    # Berechnung der Pearson-Korrelation\n",
+    "    pearson_corr, p = pearsonr(df['anomalies'], df[model])\n",
+    "\n",
+    "    # Scatterplot hinzufügen\n",
+    "    fig.add_trace(\n",
+    "        go.Scatter(x=df['anomalies'], y=df[model], mode='markers', name=model,marker=dict(color=colors.get(model, \"gray\"),size=df['size'])),\n",
+    "        row=row, col=col\n",
+    "    )\n",
+    "    \n",
+    "    #insert trendline\n",
+    "    m, b = np.polyfit(df['anomalies'], df[model], 1)\n",
+    "    fig.add_trace(\n",
+    "        go.Scatter(x=df['anomalies'], y=m * df['anomalies'] + b, mode='lines', name=f'Regression {model}',\n",
+    "                   line=dict(color='black')),\n",
+    "                    row=row, col=col\n",
+    "    )\n",
+    "    \n",
+    "    #Insert annotation for pearson correlation\n",
+    "    fig.add_annotation(\n",
+    "        text=f'PCC = {pearson_corr:.2f}',\n",
+    "        xref=f'x{row * 3 + col - 3}', yref=f'y{row * 3 + col - 3}',\n",
+    "        x=0, y=1,\n",
+    "        showarrow=False, font=dict(size=14, color='black')\n",
+    "    )\n",
+    "    fig.add_annotation(\n",
+    "        text=f'p-Wert = {p:.2f}',\n",
+    "        xref=f'x{row * 3 + col - 3}', yref=f'y{row * 3 + col - 3}',\n",
+    "        x=0, y=0.9,\n",
+    "        showarrow=False, font=dict(size=14, color='black')\n",
+    "    )\n",
+    "\n",
+    "    # Spalten und Zeilen inkrementieren\n",
+    "    col += 1\n",
+    "    if col > 3:\n",
+    "        col = 1\n",
+    "        row += 1\n",
+    "\n",
+    "# Achsentitel setzen\n",
+    "fig.update_xaxes(title_text=\"Anomaliedichte\", row=5, col=2)  # X-Achse zentriert\n",
+    "fig.update_yaxes(title_text=\"Modellleistung\", row=3, col=1)  # Y-Achse zent\n",
+    "fig.update_layout( showlegend=False,\n",
+    "    template = 'ggplot2',\n",
+    "    title={\n",
+    "            'text': '<b> Abhängigkeit der Modelleistung von der Anomaliedichte </b>',\n",
+    "            'x':0.5,\n",
+    "            'y':0.94, \n",
+    "            'xanchor':'center',\n",
+    "            'yanchor':'top'},\n",
+    "    height=1200,width=1000,font=dict(size=12),\n",
+    "    xaxis=dict(title_font=dict(size=14, family=\"Arial\"), tickfont=dict(size=12, family=\"Arial\")),\n",
+    "    yaxis=dict(title_font=dict(size=14, family=\"Arial\"), tickfont=dict(size=12, family=\"Arial\")),\n",
+    "    margin=dict(l=30, r=30, t=100, b=30),  # Links, Rechts, Oben, Unten (Padding um den ganzen Grid)\n",
+    ")\n",
+    "fig.update_traces(marker=dict(size=12,\n",
+    "                    opacity=0.8,\n",
+    "                    line=dict(width=2,\n",
+    "                                    color='DarkSlateGrey'),\n",
+    "                            ),\n",
+    "            selector=dict(mode='markers'))\n",
+    "fig.update_annotations(font_size=12)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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 8ee9c23..5fb55f9 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": 9,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13,7 +13,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metric = 'AUC-PR'\n",
+    "anoType = 'seq'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
     {
@@ -68,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
@@ -135,27 +145,6 @@
        "      <td>CNN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>{'window_size': 150, 'lr': 0.0008}</td>\n",
-       "      <td>002_NAB_id_2_WebService_tr_1500_1st_4106.csv</td>\n",
-       "      <td>3.803895</td>\n",
-       "      <td>NAB</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.281681</td>\n",
-       "      <td>0.677980</td>\n",
-       "      <td>0.284299</td>\n",
-       "      <td>0.697935</td>\n",
-       "      <td>0.378868</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.336235</td>\n",
-       "      <td>0.988203</td>\n",
-       "      <td>0.028257</td>\n",
-       "      <td>0.818182</td>\n",
-       "      <td>CNN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>{'window_size': 150, 'lr': 5e-05}</td>\n",
        "      <td>003_NAB_id_3_WebService_tr_1362_1st_1462.csv</td>\n",
@@ -177,45 +166,66 @@
        "      <td>CNN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>{'window_size': 150, 'lr': 0.0005}</td>\n",
-       "      <td>004_NAB_id_4_Facility_tr_1007_1st_1437.csv</td>\n",
-       "      <td>2.326613</td>\n",
+       "      <th>5</th>\n",
+       "      <td>{'window_size': 100, 'lr': 0.0008}</td>\n",
+       "      <td>006_NAB_id_6_Traffic_tr_2579_1st_5839.csv</td>\n",
+       "      <td>5.253244</td>\n",
        "      <td>NAB</td>\n",
        "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.112469</td>\n",
+       "      <td>0.488888</td>\n",
+       "      <td>0.123092</td>\n",
+       "      <td>0.568001</td>\n",
+       "      <td>0.183966</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>0.223525</td>\n",
+       "      <td>0.712592</td>\n",
+       "      <td>0.021359</td>\n",
+       "      <td>0.431373</td>\n",
+       "      <td>CNN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>{'window_size': 50, 'lr': 0.001}</td>\n",
+       "      <td>007_NAB_id_7_Traffic_tr_624_1st_2087.csv</td>\n",
+       "      <td>1.850910</td>\n",
+       "      <td>NAB</td>\n",
        "      <td>False</td>\n",
-       "      <td>0.503726</td>\n",
-       "      <td>0.717905</td>\n",
-       "      <td>0.506238</td>\n",
-       "      <td>0.730650</td>\n",
-       "      <td>0.507038</td>\n",
-       "      <td>1.000000</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.239679</td>\n",
+       "      <td>0.614680</td>\n",
+       "      <td>0.274140</td>\n",
+       "      <td>0.716271</td>\n",
+       "      <td>0.268976</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.337469</td>\n",
-       "      <td>0.975070</td>\n",
-       "      <td>0.007463</td>\n",
        "      <td>1.000000</td>\n",
+       "      <td>0.348135</td>\n",
+       "      <td>0.922899</td>\n",
+       "      <td>0.048387</td>\n",
+       "      <td>0.750000</td>\n",
        "      <td>CNN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>{'window_size': 100, 'lr': 0.001}</td>\n",
-       "      <td>005_NAB_id_5_Traffic_tr_594_1st_1645.csv</td>\n",
-       "      <td>0.951704</td>\n",
+       "      <th>9</th>\n",
+       "      <td>{'window_size': 100, 'lr': 5e-05}</td>\n",
+       "      <td>010_NAB_id_10_WebService_tr_500_1st_271.csv</td>\n",
+       "      <td>1.675401</td>\n",
        "      <td>NAB</td>\n",
        "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.109550</td>\n",
-       "      <td>0.484873</td>\n",
-       "      <td>0.109302</td>\n",
-       "      <td>0.492552</td>\n",
-       "      <td>0.185858</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.290453</td>\n",
+       "      <td>0.657428</td>\n",
+       "      <td>0.454923</td>\n",
+       "      <td>0.832305</td>\n",
+       "      <td>0.333330</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.337989</td>\n",
-       "      <td>0.974568</td>\n",
-       "      <td>0.042017</td>\n",
-       "      <td>0.232558</td>\n",
+       "      <td>0.369861</td>\n",
+       "      <td>0.965715</td>\n",
+       "      <td>0.040541</td>\n",
+       "      <td>0.857143</td>\n",
        "      <td>CNN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -225,61 +235,64 @@
       "text/plain": [
        "                               params  \\\n",
        "0   {'window_size': 50, 'lr': 0.0008}   \n",
-       "1  {'window_size': 150, 'lr': 0.0008}   \n",
        "2   {'window_size': 150, 'lr': 5e-05}   \n",
-       "3  {'window_size': 150, 'lr': 0.0005}   \n",
-       "4   {'window_size': 100, 'lr': 0.001}   \n",
+       "5  {'window_size': 100, 'lr': 0.0008}   \n",
+       "6    {'window_size': 50, 'lr': 0.001}   \n",
+       "9   {'window_size': 100, 'lr': 5e-05}   \n",
        "\n",
-       "                                      file_name  duration group  \\\n",
-       "0    001_NAB_id_1_Facility_tr_1007_1st_2014.csv  1.413541   NAB   \n",
-       "1  002_NAB_id_2_WebService_tr_1500_1st_4106.csv  3.803895   NAB   \n",
-       "2  003_NAB_id_3_WebService_tr_1362_1st_1462.csv  3.613276   NAB   \n",
-       "3    004_NAB_id_4_Facility_tr_1007_1st_1437.csv  2.326613   NAB   \n",
-       "4      005_NAB_id_5_Traffic_tr_594_1st_1645.csv  0.951704   NAB   \n",
+       "                                      file_name  duration group point anomaly  \\\n",
+       "0    001_NAB_id_1_Facility_tr_1007_1st_2014.csv  1.413541   NAB         False   \n",
+       "2  003_NAB_id_3_WebService_tr_1362_1st_1462.csv  3.613276   NAB         False   \n",
+       "5     006_NAB_id_6_Traffic_tr_2579_1st_5839.csv  5.253244   NAB         False   \n",
+       "6      007_NAB_id_7_Traffic_tr_624_1st_2087.csv  1.850910   NAB         False   \n",
+       "9   010_NAB_id_10_WebService_tr_500_1st_271.csv  1.675401   NAB         False   \n",
        "\n",
-       "   point anomaly  seq anomaly    AUC-PR   AUC-ROC    VUS-PR   VUS-ROC  \\\n",
-       "0          False         True  0.132832  0.486090  0.128123  0.490971   \n",
-       "1          False        False  0.281681  0.677980  0.284299  0.697935   \n",
-       "2          False         True  0.150401  0.637695  0.148660  0.643420   \n",
-       "3          False        False  0.503726  0.717905  0.506238  0.730650   \n",
-       "4          False        False  0.109550  0.484873  0.109302  0.492552   \n",
+       "  seq anomaly    AUC-PR   AUC-ROC    VUS-PR   VUS-ROC  Standard-F1     PA-F1  \\\n",
+       "0        True  0.132832  0.486090  0.128123  0.490971     0.156834  1.000000   \n",
+       "2        True  0.150401  0.637695  0.148660  0.643420     0.284398  0.999672   \n",
+       "5        True  0.112469  0.488888  0.123092  0.568001     0.183966  0.750000   \n",
+       "6        True  0.239679  0.614680  0.274140  0.716271     0.268976  1.000000   \n",
+       "9        True  0.290453  0.657428  0.454923  0.832305     0.333330  1.000000   \n",
        "\n",
-       "   Standard-F1     PA-F1  Event-based-F1  R-based-F1  Affiliation-F    Recall  \\\n",
-       "0     0.156834  1.000000        1.000000    0.359576       0.975131  0.026239   \n",
-       "1     0.378868  1.000000        1.000000    0.336235       0.988203  0.028257   \n",
-       "2     0.284398  0.999672        0.933333    0.327012       0.945860  0.009192   \n",
-       "3     0.507038  1.000000        1.000000    0.337469       0.975070  0.007463   \n",
-       "4     0.185858  1.000000        1.000000    0.337989       0.974568  0.042017   \n",
-       "\n",
-       "   Precision Model  \n",
-       "0   1.000000   CNN  \n",
-       "1   0.818182   CNN  \n",
-       "2   0.700000   CNN  \n",
-       "3   1.000000   CNN  \n",
-       "4   0.232558   CNN  "
+       "   Event-based-F1  R-based-F1  Affiliation-F    Recall  Precision Model  \n",
+       "0        1.000000    0.359576       0.975131  0.026239   1.000000   CNN  \n",
+       "2        0.933333    0.327012       0.945860  0.009192   0.700000   CNN  \n",
+       "5        0.750000    0.223525       0.712592  0.021359   0.431373   CNN  \n",
+       "6        1.000000    0.348135       0.922899  0.048387   0.750000   CNN  \n",
+       "9        1.000000    0.369861       0.965715  0.040541   0.857143   CNN  "
       ]
      },
-     "execution_count": 11,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "title = 'Vergleich der Performanz der Modelle anhand '+ metric\n",
+    "if anoType != '':\n",
+    "    #check if selected type is point anomaly if check if time series only includes point anomalies\n",
+    "    if anoType == 'point':\n",
+    "        df = df.where(df['point anomaly'] == True).dropna()\n",
+    "        title += ' für Punktanomalien'\n",
+    "    elif anoType == 'seq':\n",
+    "        df = df.where(df['seq anomaly'] == True).dropna()\n",
+    "        title += ' für Sequenzanomalien'\n",
+    "\n",
     "df.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(5558, 18)"
+       "(2212, 18)"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -290,31 +303,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Model\n",
-       "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",
-       "Sub-IForest    397\n",
-       "Sub-KNN        397\n",
-       "Sub-LOF        397\n",
-       "USAD           397\n",
+       "CNN            158\n",
+       "IForest        158\n",
+       "KMeansAD_U     158\n",
+       "KShapeAD       158\n",
+       "LOF            158\n",
+       "LSTMAD         158\n",
+       "OCSVM          158\n",
+       "OmniAnomaly    158\n",
+       "PCA            158\n",
+       "POLY           158\n",
+       "Sub-IForest    158\n",
+       "Sub-KNN        158\n",
+       "Sub-LOF        158\n",
+       "USAD           158\n",
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -325,7 +338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -349,7 +362,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
@@ -371,10865 +384,2384 @@
        "data": [
         {
          "alignmentgroup": "True",
-         "hovertemplate": "Model=%{x}<br>Precision=%{y}<extra></extra>",
-         "legendgroup": "CNN",
+         "hovertemplate": "Model=%{x}<br>AUC-PR=%{y}<extra></extra>",
+         "legendgroup": "PCA",
          "marker": {
-          "color": "steelblue"
+          "color": "yellowgreen"
          },
-         "name": "CNN",
+         "name": "PCA",
          "notched": false,
-         "offsetgroup": "CNN",
+         "offsetgroup": "PCA",
+         "orientation": "v",
+         "showlegend": true,
+         "type": "box",
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         },
         {
          "alignmentgroup": "True",
          "hovertemplate": "Model=%{x}<br>duration=%{y}<extra></extra>",
-         "legendgroup": "POLY",
+         "legendgroup": "USAD",
          "marker": {
-          "color": "lightskyblue",
+          "color": "mediumpurple",
           "opacity": 0.8,
           "pattern": {
            "shape": ""
           }
          },
-         "name": "POLY",
-         "offsetgroup": "POLY",
+         "name": "USAD",
+         "offsetgroup": "USAD",
          "orientation": "v",
          "showlegend": true,
          "textposition": "auto",
          "type": "bar",
          "x": [
-          "POLY"
+          "USAD"
          ],
          "xaxis": "x",
          "y": [
-          30.973502482815412
+          47.7148148123222
          ],
          "yaxis": "y"
         },
         {
          "alignmentgroup": "True",
          "hovertemplate": "Model=%{x}<br>duration=%{y}<extra></extra>",
-         "legendgroup": "OmniAnomaly",
+         "legendgroup": "IForest",
          "marker": {
-          "color": "slateblue",
+          "color": "darkmagenta",
           "opacity": 0.8,
           "pattern": {
            "shape": ""
           }
          },
-         "name": "OmniAnomaly",
-         "offsetgroup": "OmniAnomaly",
+         "name": "IForest",
+         "offsetgroup": "IForest",
          "orientation": "v",
          "showlegend": true,
          "textposition": "auto",
          "type": "bar",
          "x": [
-          "OmniAnomaly"
+          "IForest"
          ],
          "xaxis": "x",
          "y": [
-          31.020189434815414
+          50.97673821147484
          ],
          "yaxis": "y"
         },
         {
          "alignmentgroup": "True",
          "hovertemplate": "Model=%{x}<br>duration=%{y}<extra></extra>",
-         "legendgroup": "IForest",
+         "legendgroup": "Sub-IForest",
          "marker": {
-          "color": "darkmagenta",
+          "color": "green",
           "opacity": 0.8,
           "pattern": {
            "shape": ""
           }
          },
-         "name": "IForest",
-         "offsetgroup": "IForest",
+         "name": "Sub-IForest",
+         "offsetgroup": "Sub-IForest",
          "orientation": "v",
          "showlegend": true,
          "textposition": "auto",
          "type": "bar",
          "x": [
-          "IForest"
+          "Sub-IForest"
          ],
          "xaxis": "x",
          "y": [
-          31.21474815135639
+          51.266125943087324
          ],
          "yaxis": "y"
         },
@@ -12702,33 +6022,33 @@
          ],
          "xaxis": "x",
          "y": [
-          32.13738634904626
+          57.0281253902218
          ],
          "yaxis": "y"
         },
         {
          "alignmentgroup": "True",
          "hovertemplate": "Model=%{x}<br>duration=%{y}<extra></extra>",
-         "legendgroup": "LSTMAD",
+         "legendgroup": "POLY",
          "marker": {
-          "color": "blueviolet",
+          "color": "lightskyblue",
           "opacity": 0.8,
           "pattern": {
            "shape": ""
           }
          },
-         "name": "LSTMAD",
-         "offsetgroup": "LSTMAD",
+         "name": "POLY",
+         "offsetgroup": "POLY",
          "orientation": "v",
          "showlegend": true,
          "textposition": "auto",
          "type": "bar",
          "x": [
-          "LSTMAD"
+          "POLY"
          ],
          "xaxis": "x",
          "y": [
-          33.17645774320031
+          61.07144836836223
          ],
          "yaxis": "y"
         },
@@ -12754,7 +6074,7 @@
          ],
          "xaxis": "x",
          "y": [
-          38.65838090358513
+          74.58672633050364
          ],
          "yaxis": "y"
         },
@@ -12780,7 +6100,7 @@
          ],
          "xaxis": "x",
          "y": [
-          46.61300139823548
+          91.55315207529672
          ],
          "yaxis": "y"
         },
@@ -12806,7 +6126,7 @@
          ],
          "xaxis": "x",
          "y": [
-          50.93969483459627
+          96.83244684074498
          ],
          "yaxis": "y"
         },
@@ -12832,7 +6152,7 @@
          ],
          "xaxis": "x",
          "y": [
-          52.58865696116719
+          101.4624824644644
          ],
          "yaxis": "y"
         },
@@ -12858,7 +6178,7 @@
          ],
          "xaxis": "x",
          "y": [
-          104.21769456779326
+          165.97131169445908
          ],
          "yaxis": "y"
         }
@@ -13464,13 +6784,13 @@
          "categoryarray": [
           "LOF",
           "CNN",
-          "USAD",
-          "Sub-IForest",
-          "POLY",
+          "LSTMAD",
           "OmniAnomaly",
+          "USAD",
           "IForest",
+          "Sub-IForest",
           "KShapeAD",
-          "LSTMAD",
+          "POLY",
           "PCA",
           "Sub-LOF",
           "KMeansAD_U",
diff --git a/src/group_evaluation/create_heatmap.ipynb b/src/group_evaluation/create_heatmap.ipynb
index cc245be..feb6655 100644
--- a/src/group_evaluation/create_heatmap.ipynb
+++ b/src/group_evaluation/create_heatmap.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -36,27 +36,26 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>UCR</th>\n",
-       "      <th>WSD</th>\n",
-       "      <th>SMAP</th>\n",
+       "      <th>MITDB</th>\n",
+       "      <th>SWaT</th>\n",
+       "      <th>NEK</th>\n",
        "      <th>IOPS</th>\n",
-       "      <th>Stock</th>\n",
-       "      <th>MSL</th>\n",
+       "      <th>LTDB</th>\n",
        "      <th>Power</th>\n",
-       "      <th>SWaT</th>\n",
+       "      <th>CATSv2</th>\n",
+       "      <th>MSL</th>\n",
        "      <th>Daphnet</th>\n",
-       "      <th>MGAB</th>\n",
-       "      <th>...</th>\n",
+       "      <th>WSD</th>\n",
        "      <th>SED</th>\n",
-       "      <th>TAO</th>\n",
-       "      <th>Exathlon</th>\n",
-       "      <th>MITDB</th>\n",
-       "      <th>NEK</th>\n",
-       "      <th>OPPORTUNITY</th>\n",
-       "      <th>LTDB</th>\n",
+       "      <th>YAHOO</th>\n",
        "      <th>SMD</th>\n",
        "      <th>SVDB</th>\n",
-       "      <th>YAHOO</th>\n",
+       "      <th>NAB</th>\n",
+       "      <th>TODS</th>\n",
+       "      <th>MGAB</th>\n",
+       "      <th>OPPORTUNITY</th>\n",
+       "      <th>SMAP</th>\n",
+       "      <th>Exathlon</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Model</th>\n",
@@ -80,180 +79,175 @@
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
-       "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CNN</th>\n",
-       "      <td>0.069477</td>\n",
-       "      <td>0.303128</td>\n",
-       "      <td>0.329931</td>\n",
-       "      <td>0.265889</td>\n",
-       "      <td>0.929344</td>\n",
-       "      <td>0.307033</td>\n",
-       "      <td>0.072400</td>\n",
-       "      <td>0.738718</td>\n",
-       "      <td>0.381499</td>\n",
-       "      <td>0.003855</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.029325</td>\n",
-       "      <td>0.994857</td>\n",
-       "      <td>0.654310</td>\n",
-       "      <td>0.160153</td>\n",
-       "      <td>0.685573</td>\n",
-       "      <td>0.619160</td>\n",
-       "      <td>0.354462</td>\n",
-       "      <td>0.565370</td>\n",
-       "      <td>0.197103</td>\n",
-       "      <td>0.561562</td>\n",
+       "      <td>0.143632</td>\n",
+       "      <td>0.680748</td>\n",
+       "      <td>0.756555</td>\n",
+       "      <td>0.312286</td>\n",
+       "      <td>0.406171</td>\n",
+       "      <td>0.075487</td>\n",
+       "      <td>0.288293</td>\n",
+       "      <td>0.326973</td>\n",
+       "      <td>0.398456</td>\n",
+       "      <td>0.206990</td>\n",
+       "      <td>0.058416</td>\n",
+       "      <td>0.351064</td>\n",
+       "      <td>0.503535</td>\n",
+       "      <td>0.206479</td>\n",
+       "      <td>0.238264</td>\n",
+       "      <td>0.331028</td>\n",
+       "      <td>0.006988</td>\n",
+       "      <td>0.741769</td>\n",
+       "      <td>0.237156</td>\n",
+       "      <td>0.657757</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>IForest</th>\n",
-       "      <td>0.059389</td>\n",
-       "      <td>0.040993</td>\n",
-       "      <td>0.299302</td>\n",
-       "      <td>0.231569</td>\n",
-       "      <td>0.143940</td>\n",
-       "      <td>0.386085</td>\n",
-       "      <td>0.073981</td>\n",
-       "      <td>0.702432</td>\n",
-       "      <td>0.496579</td>\n",
-       "      <td>0.004336</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.691484</td>\n",
-       "      <td>0.125817</td>\n",
-       "      <td>0.962806</td>\n",
-       "      <td>0.169557</td>\n",
-       "      <td>0.630339</td>\n",
-       "      <td>0.733153</td>\n",
-       "      <td>0.521341</td>\n",
-       "      <td>0.369237</td>\n",
-       "      <td>0.311936</td>\n",
-       "      <td>0.092730</td>\n",
+       "      <td>0.159042</td>\n",
+       "      <td>0.368230</td>\n",
+       "      <td>0.678337</td>\n",
+       "      <td>0.267117</td>\n",
+       "      <td>0.566359</td>\n",
+       "      <td>0.076373</td>\n",
+       "      <td>0.105175</td>\n",
+       "      <td>0.514630</td>\n",
+       "      <td>0.518905</td>\n",
+       "      <td>0.038526</td>\n",
+       "      <td>0.807209</td>\n",
+       "      <td>0.936245</td>\n",
+       "      <td>0.322146</td>\n",
+       "      <td>0.314567</td>\n",
+       "      <td>0.285635</td>\n",
+       "      <td>0.174752</td>\n",
+       "      <td>0.004379</td>\n",
+       "      <td>0.868003</td>\n",
+       "      <td>0.487664</td>\n",
+       "      <td>0.964836</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>KMeansAD_U</th>\n",
-       "      <td>0.487206</td>\n",
-       "      <td>0.146372</td>\n",
-       "      <td>0.648913</td>\n",
-       "      <td>0.068715</td>\n",
-       "      <td>0.114476</td>\n",
-       "      <td>0.371438</td>\n",
-       "      <td>0.507629</td>\n",
-       "      <td>0.111152</td>\n",
-       "      <td>0.036445</td>\n",
-       "      <td>0.085299</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.710575</td>\n",
-       "      <td>0.129147</td>\n",
-       "      <td>0.617690</td>\n",
-       "      <td>0.583808</td>\n",
-       "      <td>0.346973</td>\n",
-       "      <td>0.365381</td>\n",
-       "      <td>0.765819</td>\n",
-       "      <td>0.260908</td>\n",
-       "      <td>0.809036</td>\n",
-       "      <td>0.699444</td>\n",
+       "      <td>0.590892</td>\n",
+       "      <td>0.111519</td>\n",
+       "      <td>0.298379</td>\n",
+       "      <td>0.112229</td>\n",
+       "      <td>0.835553</td>\n",
+       "      <td>0.524166</td>\n",
+       "      <td>0.229268</td>\n",
+       "      <td>0.405174</td>\n",
+       "      <td>0.039747</td>\n",
+       "      <td>0.293116</td>\n",
+       "      <td>0.895291</td>\n",
+       "      <td>0.477510</td>\n",
+       "      <td>0.092843</td>\n",
+       "      <td>0.804472</td>\n",
+       "      <td>0.272775</td>\n",
+       "      <td>0.689051</td>\n",
+       "      <td>0.036645</td>\n",
+       "      <td>0.255118</td>\n",
+       "      <td>0.946525</td>\n",
+       "      <td>0.618051</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>KShapeAD</th>\n",
-       "      <td>0.465144</td>\n",
-       "      <td>0.082639</td>\n",
-       "      <td>0.601232</td>\n",
-       "      <td>0.068365</td>\n",
-       "      <td>0.095333</td>\n",
-       "      <td>0.409835</td>\n",
-       "      <td>0.496250</td>\n",
-       "      <td>0.458896</td>\n",
-       "      <td>0.033420</td>\n",
-       "      <td>0.084411</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.722020</td>\n",
-       "      <td>0.103834</td>\n",
-       "      <td>0.420990</td>\n",
-       "      <td>0.594424</td>\n",
-       "      <td>0.379153</td>\n",
-       "      <td>0.366866</td>\n",
-       "      <td>0.743642</td>\n",
-       "      <td>0.131250</td>\n",
-       "      <td>0.798760</td>\n",
-       "      <td>0.435473</td>\n",
+       "      <td>0.606652</td>\n",
+       "      <td>0.381310</td>\n",
+       "      <td>0.282706</td>\n",
+       "      <td>0.090598</td>\n",
+       "      <td>0.811737</td>\n",
+       "      <td>0.510537</td>\n",
+       "      <td>0.230658</td>\n",
+       "      <td>0.290049</td>\n",
+       "      <td>0.035690</td>\n",
+       "      <td>0.192884</td>\n",
+       "      <td>0.907086</td>\n",
+       "      <td>0.276917</td>\n",
+       "      <td>0.070547</td>\n",
+       "      <td>0.804276</td>\n",
+       "      <td>0.282421</td>\n",
+       "      <td>0.719779</td>\n",
+       "      <td>0.069759</td>\n",
+       "      <td>0.253277</td>\n",
+       "      <td>0.861769</td>\n",
+       "      <td>0.420845</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LOF</th>\n",
-       "      <td>0.048985</td>\n",
-       "      <td>0.141918</td>\n",
-       "      <td>0.180797</td>\n",
-       "      <td>0.116787</td>\n",
-       "      <td>0.112061</td>\n",
-       "      <td>0.151228</td>\n",
-       "      <td>0.086245</td>\n",
-       "      <td>0.120683</td>\n",
-       "      <td>0.129509</td>\n",
-       "      <td>0.002209</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.082198</td>\n",
-       "      <td>0.167995</td>\n",
-       "      <td>0.245106</td>\n",
-       "      <td>0.051628</td>\n",
-       "      <td>0.363561</td>\n",
-       "      <td>0.153396</td>\n",
-       "      <td>0.203241</td>\n",
-       "      <td>0.182353</td>\n",
-       "      <td>0.044940</td>\n",
-       "      <td>0.409909</td>\n",
+       "      <td>0.053749</td>\n",
+       "      <td>0.121168</td>\n",
+       "      <td>0.392335</td>\n",
+       "      <td>0.302989</td>\n",
+       "      <td>0.256447</td>\n",
+       "      <td>0.090503</td>\n",
+       "      <td>0.063826</td>\n",
+       "      <td>0.327778</td>\n",
+       "      <td>0.134804</td>\n",
+       "      <td>0.073377</td>\n",
+       "      <td>0.111105</td>\n",
+       "      <td>0.304093</td>\n",
+       "      <td>0.137760</td>\n",
+       "      <td>0.052218</td>\n",
+       "      <td>0.183564</td>\n",
+       "      <td>0.198760</td>\n",
+       "      <td>0.004639</td>\n",
+       "      <td>0.152842</td>\n",
+       "      <td>0.046142</td>\n",
+       "      <td>0.240338</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5 rows × 23 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "                 UCR       WSD      SMAP      IOPS     Stock       MSL  \\\n",
+       "               MITDB      SWaT       NEK      IOPS      LTDB     Power  \\\n",
        "Model                                                                    \n",
-       "CNN         0.069477  0.303128  0.329931  0.265889  0.929344  0.307033   \n",
-       "IForest     0.059389  0.040993  0.299302  0.231569  0.143940  0.386085   \n",
-       "KMeansAD_U  0.487206  0.146372  0.648913  0.068715  0.114476  0.371438   \n",
-       "KShapeAD    0.465144  0.082639  0.601232  0.068365  0.095333  0.409835   \n",
-       "LOF         0.048985  0.141918  0.180797  0.116787  0.112061  0.151228   \n",
+       "CNN         0.143632  0.680748  0.756555  0.312286  0.406171  0.075487   \n",
+       "IForest     0.159042  0.368230  0.678337  0.267117  0.566359  0.076373   \n",
+       "KMeansAD_U  0.590892  0.111519  0.298379  0.112229  0.835553  0.524166   \n",
+       "KShapeAD    0.606652  0.381310  0.282706  0.090598  0.811737  0.510537   \n",
+       "LOF         0.053749  0.121168  0.392335  0.302989  0.256447  0.090503   \n",
        "\n",
-       "               Power      SWaT   Daphnet      MGAB  ...       SED       TAO  \\\n",
-       "Model                                               ...                       \n",
-       "CNN         0.072400  0.738718  0.381499  0.003855  ...  0.029325  0.994857   \n",
-       "IForest     0.073981  0.702432  0.496579  0.004336  ...  0.691484  0.125817   \n",
-       "KMeansAD_U  0.507629  0.111152  0.036445  0.085299  ...  0.710575  0.129147   \n",
-       "KShapeAD    0.496250  0.458896  0.033420  0.084411  ...  0.722020  0.103834   \n",
-       "LOF         0.086245  0.120683  0.129509  0.002209  ...  0.082198  0.167995   \n",
+       "              CATSv2       MSL   Daphnet       WSD       SED     YAHOO  \\\n",
+       "Model                                                                    \n",
+       "CNN         0.288293  0.326973  0.398456  0.206990  0.058416  0.351064   \n",
+       "IForest     0.105175  0.514630  0.518905  0.038526  0.807209  0.936245   \n",
+       "KMeansAD_U  0.229268  0.405174  0.039747  0.293116  0.895291  0.477510   \n",
+       "KShapeAD    0.230658  0.290049  0.035690  0.192884  0.907086  0.276917   \n",
+       "LOF         0.063826  0.327778  0.134804  0.073377  0.111105  0.304093   \n",
        "\n",
-       "            Exathlon     MITDB       NEK  OPPORTUNITY      LTDB       SMD  \\\n",
+       "                 SMD      SVDB       NAB      TODS      MGAB  OPPORTUNITY  \\\n",
        "Model                                                                       \n",
-       "CNN         0.654310  0.160153  0.685573     0.619160  0.354462  0.565370   \n",
-       "IForest     0.962806  0.169557  0.630339     0.733153  0.521341  0.369237   \n",
-       "KMeansAD_U  0.617690  0.583808  0.346973     0.365381  0.765819  0.260908   \n",
-       "KShapeAD    0.420990  0.594424  0.379153     0.366866  0.743642  0.131250   \n",
-       "LOF         0.245106  0.051628  0.363561     0.153396  0.203241  0.182353   \n",
+       "CNN         0.503535  0.206479  0.238264  0.331028  0.006988     0.741769   \n",
+       "IForest     0.322146  0.314567  0.285635  0.174752  0.004379     0.868003   \n",
+       "KMeansAD_U  0.092843  0.804472  0.272775  0.689051  0.036645     0.255118   \n",
+       "KShapeAD    0.070547  0.804276  0.282421  0.719779  0.069759     0.253277   \n",
+       "LOF         0.137760  0.052218  0.183564  0.198760  0.004639     0.152842   \n",
        "\n",
-       "                SVDB     YAHOO  \n",
+       "                SMAP  Exathlon  \n",
        "Model                           \n",
-       "CNN         0.197103  0.561562  \n",
-       "IForest     0.311936  0.092730  \n",
-       "KMeansAD_U  0.809036  0.699444  \n",
-       "KShapeAD    0.798760  0.435473  \n",
-       "LOF         0.044940  0.409909  \n",
-       "\n",
-       "[5 rows x 23 columns]"
+       "CNN         0.237156  0.657757  \n",
+       "IForest     0.487664  0.964836  \n",
+       "KMeansAD_U  0.946525  0.618051  \n",
+       "KShapeAD    0.861769  0.420845  \n",
+       "LOF         0.046142  0.240338  "
       ]
      },
-     "execution_count": 10,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "path = '../../docs/evaluation/groupedDataframes/'\n",
-    "metric = 'AUC-PR'\n",
-    "file = metric +'_combined_by_group.csv'\n",
+    "metric = 'VUS-PR'\n",
+    "anoType = 'seq'\n",
+    "if anoType != '':\n",
+    "    file = metric + '_'+anoType+'_combined_by_group.csv'\n",
+    "else: \n",
+    "    file = metric +'_combined_by_group.csv'\n",
     "file_path = path + file\n",
     "df = pd.read_csv(file_path, index_col='Model')\n",
     "df = df.drop(columns=['Unnamed: 0'])\n",
@@ -262,7 +256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -313,354 +307,312 @@
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-           "style": "light"
-          },
           "paper_bgcolor": "white",
-          "plot_bgcolor": "#E5ECF6",
+          "plot_bgcolor": "rgb(237,237,237)",
           "polar": {
            "angularaxis": {
             "gridcolor": "white",
             "linecolor": "white",
-            "ticks": ""
+            "showgrid": true,
+            "tickcolor": "rgb(51,51,51)",
+            "ticks": "outside"
            },
-           "bgcolor": "#E5ECF6",
+           "bgcolor": "rgb(237,237,237)",
            "radialaxis": {
             "gridcolor": "white",
             "linecolor": "white",
-            "ticks": ""
+            "showgrid": true,
+            "tickcolor": "rgb(51,51,51)",
+            "ticks": "outside"
            }
           },
           "scene": {
            "xaxis": {
-            "backgroundcolor": "#E5ECF6",
+            "backgroundcolor": "rgb(237,237,237)",
             "gridcolor": "white",
             "gridwidth": 2,
             "linecolor": "white",
             "showbackground": true,
-            "ticks": "",
+            "showgrid": true,
+            "tickcolor": "rgb(51,51,51)",
+            "ticks": "outside",
             "zerolinecolor": "white"
            },
            "yaxis": {
-            "backgroundcolor": "#E5ECF6",
+            "backgroundcolor": "rgb(237,237,237)",
             "gridcolor": "white",
             "gridwidth": 2,
             "linecolor": "white",
             "showbackground": true,
-            "ticks": "",
+            "showgrid": true,
+            "tickcolor": "rgb(51,51,51)",
+            "ticks": "outside",
             "zerolinecolor": "white"
            },
            "zaxis": {
-            "backgroundcolor": "#E5ECF6",
+            "backgroundcolor": "rgb(237,237,237)",
             "gridcolor": "white",
             "gridwidth": 2,
             "linecolor": "white",
             "showbackground": true,
-            "ticks": "",
+            "showgrid": true,
+            "tickcolor": "rgb(51,51,51)",
+            "ticks": "outside",
             "zerolinecolor": "white"
            }
           },
           "shapedefaults": {
+           "fillcolor": "black",
            "line": {
-            "color": "#2a3f5f"
-           }
+            "width": 0
+           },
+           "opacity": 0.3
           },
           "ternary": {
            "aaxis": {
             "gridcolor": "white",
             "linecolor": "white",
-            "ticks": ""
+            "showgrid": true,
+            "tickcolor": "rgb(51,51,51)",
+            "ticks": "outside"
            },
            "baxis": {
             "gridcolor": "white",
             "linecolor": "white",
-            "ticks": ""
+            "showgrid": true,
+            "tickcolor": "rgb(51,51,51)",
+            "ticks": "outside"
            },
-           "bgcolor": "#E5ECF6",
+           "bgcolor": "rgb(237,237,237)",
            "caxis": {
             "gridcolor": "white",
             "linecolor": "white",
-            "ticks": ""
+            "showgrid": true,
+            "tickcolor": "rgb(51,51,51)",
+            "ticks": "outside"
            }
           },
-          "title": {
-           "x": 0.05
-          },
           "xaxis": {
            "automargin": true,
            "gridcolor": "white",
            "linecolor": "white",
-           "ticks": "",
+           "showgrid": true,
+           "tickcolor": "rgb(51,51,51)",
+           "ticks": "outside",
            "title": {
             "standoff": 15
            },
-           "zerolinecolor": "white",
-           "zerolinewidth": 2
+           "zerolinecolor": "white"
           },
           "yaxis": {
            "automargin": true,
            "gridcolor": "white",
            "linecolor": "white",
-           "ticks": "",
+           "showgrid": true,
+           "tickcolor": "rgb(51,51,51)",
+           "ticks": "outside",
            "title": {
             "standoff": 15
            },
-           "zerolinecolor": "white",
-           "zerolinewidth": 2
+           "zerolinecolor": "white"
           }
          }
         },
         "title": {
-         "text": "Oranges Heatmap der AUC-PR von den einzelnen Modelle auf den jeweiligen Datensätzen"
+         "text": "<b> Vergleich der VUS-PR aller Modelle auf den jeweiligen Datensätzen </b>",
+         "xanchor": "center",
+         "y": 0.9,
+         "yanchor": "top"
         },
+        "width": 950,
         "xaxis": {
          "title": {
-          "text": "Datensätze"
+          "text": "Datensätze mit Sequenzanomalien"
          }
         },
         "yaxis": {
@@ -1922,8 +1580,14 @@
     "))\n",
     "\n",
     "fig.update_layout(\n",
-    "    title= scale+' Heatmap der ' +metric+ ' von den einzelnen Modelle auf den jeweiligen Datensätzen',\n",
-    "    xaxis=dict(title='Datensätze'),\n",
+    "    template = 'ggplot2',\n",
+    "        title={\n",
+    "                'text': '<b> Vergleich der ' +metric+ ' aller Modelle auf den jeweiligen Datensätzen </b>',\n",
+    "                'y':0.90, \n",
+    "                'xanchor':'center',\n",
+    "                'yanchor':'top'},\n",
+    "        height=600,width=950,\n",
+    "    xaxis=dict(title='Datensätze mit Sequenzanomalien'),\n",
     "    yaxis=dict(title='Modelle')\n",
     ")\n",
     "\n",
@@ -1932,37 +1596,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "UCR: 0.213\n",
-      "WSD: 0.198\n",
-      "SMAP: 0.423\n",
-      "IOPS: 0.225\n",
-      "Stock: 0.210\n",
-      "MSL: 0.361\n",
-      "Power: 0.179\n",
-      "SWaT: 0.453\n",
-      "Daphnet: 0.247\n",
-      "MGAB: 0.088\n",
-      "CATSv2: 0.378\n",
-      "TODS: 0.241\n",
-      "NAB: 0.346\n",
-      "SED: 0.275\n",
-      "TAO: 0.284\n",
-      "Exathlon: 0.682\n",
-      "MITDB: 0.305\n",
-      "NEK: 0.590\n",
-      "OPPORTUNITY: 0.488\n",
-      "LTDB: 0.490\n",
-      "SMD: 0.398\n",
-      "SVDB: 0.436\n",
-      "YAHOO: 0.263\n",
-      "Gesamtdurchschnitt 0.338\n"
+      "MITDB: 0.293\n",
+      "SWaT: 0.309\n",
+      "NEK: 0.601\n",
+      "IOPS: 0.268\n",
+      "LTDB: 0.549\n",
+      "Power: 0.178\n",
+      "CATSv2: 0.257\n",
+      "MSL: 0.384\n",
+      "Daphnet: 0.240\n",
+      "WSD: 0.178\n",
+      "SED: 0.358\n",
+      "YAHOO: 0.462\n",
+      "SMD: 0.310\n",
+      "SVDB: 0.429\n",
+      "NAB: 0.311\n",
+      "TODS: 0.431\n",
+      "MGAB: 0.080\n",
+      "OPPORTUNITY: 0.512\n",
+      "SMAP: 0.670\n",
+      "Exathlon: 0.683\n",
+      "Gesamtdurchschnitt 0.375\n"
      ]
     }
    ],
@@ -1978,7 +1639,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -1990,7 +1651,7 @@
        "      dtype='object', name='Model')"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/src/group_evaluation/generate_combined_dataframe.ipynb b/src/group_evaluation/generate_combined_dataframe.ipynb
index b43dbd9..a8b75f1 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": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -12,20 +12,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "col = 'AUC-PR'\n",
+    "anoType = 'point'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
     "path = '../../docs/evaluation/'\n",
     "#get list of all groups\n",
     "temp_df = pd.read_csv(path + 'PCA.csv')\n",
+    "#reduce dataframe if anomaly type is selected\n",
+    "if anoType != '':\n",
+    "    if anoType == 'point':\n",
+    "        temp_df = temp_df.where(temp_df['point anomaly'] == True).dropna()\n",
+    "    elif anoType == 'seq':\n",
+    "        temp_df = temp_df.where(temp_df['seq anomaly'] == True).dropna()\n",
     "keys = list(set(list(temp_df['group'])))\n",
     "keys.insert(0,'Model')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -51,42 +67,26 @@
        "      <th></th>\n",
        "      <th>Model</th>\n",
        "      <th>UCR</th>\n",
+       "      <th>TODS</th>\n",
+       "      <th>YAHOO</th>\n",
+       "      <th>Stock</th>\n",
        "      <th>WSD</th>\n",
-       "      <th>SMAP</th>\n",
        "      <th>IOPS</th>\n",
-       "      <th>Stock</th>\n",
-       "      <th>MSL</th>\n",
-       "      <th>Power</th>\n",
-       "      <th>SWaT</th>\n",
-       "      <th>Daphnet</th>\n",
-       "      <th>...</th>\n",
-       "      <th>SED</th>\n",
        "      <th>TAO</th>\n",
-       "      <th>Exathlon</th>\n",
-       "      <th>MITDB</th>\n",
-       "      <th>NEK</th>\n",
-       "      <th>OPPORTUNITY</th>\n",
-       "      <th>LTDB</th>\n",
-       "      <th>SMD</th>\n",
-       "      <th>SVDB</th>\n",
-       "      <th>YAHOO</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>0 rows × 24 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
        "Empty DataFrame\n",
-       "Columns: [Model, UCR, WSD, SMAP, IOPS, Stock, MSL, Power, SWaT, Daphnet, MGAB, CATSv2, TODS, NAB, SED, TAO, Exathlon, MITDB, NEK, OPPORTUNITY, LTDB, SMD, SVDB, YAHOO]\n",
-       "Index: []\n",
-       "\n",
-       "[0 rows x 24 columns]"
+       "Columns: [Model, UCR, TODS, YAHOO, Stock, WSD, IOPS, TAO]\n",
+       "Index: []"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -101,7 +101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -131,7 +131,7 @@
     }
    ],
    "source": [
-    "col = 'VUS-PR'\n",
+    "\n",
     "\n",
     "for file in os.listdir(path):\n",
     "    suffix = file.split('.')\n",
@@ -142,6 +142,13 @@
     "        print('Found: ', file)\n",
     "        #read Data\n",
     "        df_tmp = pd.read_csv(path+file)\n",
+    "        \n",
+    "        if anoType != '':\n",
+    "            #check if selected type is point anomaly if check if time series only includes point anomalies\n",
+    "            if anoType == 'point':\n",
+    "                df_tmp = df_tmp.where(df_tmp['point anomaly'] == True).dropna()\n",
+    "            elif anoType == 'seq':\n",
+    "                df_tmp = df_tmp.where(df_tmp['seq anomaly'] == True).dropna()\n",
     "        #select cols\n",
     "        df_tmp = df_tmp[['group',col]]\n",
     "        #Group Dataframe and get mean for each group\n",
@@ -163,7 +170,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -189,179 +196,91 @@
        "      <th></th>\n",
        "      <th>Model</th>\n",
        "      <th>UCR</th>\n",
+       "      <th>TODS</th>\n",
+       "      <th>YAHOO</th>\n",
+       "      <th>Stock</th>\n",
        "      <th>WSD</th>\n",
-       "      <th>SMAP</th>\n",
        "      <th>IOPS</th>\n",
-       "      <th>Stock</th>\n",
-       "      <th>MSL</th>\n",
-       "      <th>Power</th>\n",
-       "      <th>SWaT</th>\n",
-       "      <th>Daphnet</th>\n",
-       "      <th>...</th>\n",
-       "      <th>SED</th>\n",
        "      <th>TAO</th>\n",
-       "      <th>Exathlon</th>\n",
-       "      <th>MITDB</th>\n",
-       "      <th>NEK</th>\n",
-       "      <th>OPPORTUNITY</th>\n",
-       "      <th>LTDB</th>\n",
-       "      <th>SMD</th>\n",
-       "      <th>SVDB</th>\n",
-       "      <th>YAHOO</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>CNN</td>\n",
-       "      <td>0.065443</td>\n",
-       "      <td>0.224922</td>\n",
-       "      <td>0.336818</td>\n",
-       "      <td>0.205826</td>\n",
-       "      <td>0.939925</td>\n",
-       "      <td>0.346350</td>\n",
-       "      <td>0.075487</td>\n",
-       "      <td>0.680748</td>\n",
-       "      <td>0.398456</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.058416</td>\n",
-       "      <td>0.994270</td>\n",
-       "      <td>0.657757</td>\n",
-       "      <td>0.143632</td>\n",
-       "      <td>0.714845</td>\n",
-       "      <td>0.632799</td>\n",
-       "      <td>0.406171</td>\n",
-       "      <td>0.545045</td>\n",
-       "      <td>0.199785</td>\n",
-       "      <td>0.512096</td>\n",
+       "      <td>0.000498</td>\n",
+       "      <td>0.531847</td>\n",
+       "      <td>0.587049</td>\n",
+       "      <td>0.929344</td>\n",
+       "      <td>0.342989</td>\n",
+       "      <td>0.210157</td>\n",
+       "      <td>0.993457</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>IForest</td>\n",
-       "      <td>0.058773</td>\n",
-       "      <td>0.027487</td>\n",
-       "      <td>0.327727</td>\n",
-       "      <td>0.134518</td>\n",
-       "      <td>0.735886</td>\n",
-       "      <td>0.448709</td>\n",
-       "      <td>0.076373</td>\n",
-       "      <td>0.368230</td>\n",
-       "      <td>0.518905</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.807209</td>\n",
-       "      <td>0.728970</td>\n",
-       "      <td>0.964836</td>\n",
-       "      <td>0.159042</td>\n",
-       "      <td>0.649735</td>\n",
-       "      <td>0.736264</td>\n",
-       "      <td>0.566359</td>\n",
-       "      <td>0.342406</td>\n",
-       "      <td>0.307322</td>\n",
-       "      <td>0.135664</td>\n",
+       "      <td>0.000139</td>\n",
+       "      <td>0.069264</td>\n",
+       "      <td>0.007312</td>\n",
+       "      <td>0.143940</td>\n",
+       "      <td>0.000501</td>\n",
+       "      <td>0.011860</td>\n",
+       "      <td>0.126763</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>KMeansAD_U</td>\n",
-       "      <td>0.486822</td>\n",
-       "      <td>0.173060</td>\n",
-       "      <td>0.657738</td>\n",
-       "      <td>0.107988</td>\n",
-       "      <td>0.738198</td>\n",
-       "      <td>0.441543</td>\n",
-       "      <td>0.524166</td>\n",
-       "      <td>0.111519</td>\n",
-       "      <td>0.039747</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.895291</td>\n",
-       "      <td>0.747945</td>\n",
-       "      <td>0.618051</td>\n",
-       "      <td>0.590892</td>\n",
-       "      <td>0.317927</td>\n",
-       "      <td>0.351828</td>\n",
-       "      <td>0.835553</td>\n",
-       "      <td>0.254537</td>\n",
-       "      <td>0.811385</td>\n",
-       "      <td>0.781824</td>\n",
+       "      <td>0.002843</td>\n",
+       "      <td>0.083989</td>\n",
+       "      <td>0.742578</td>\n",
+       "      <td>0.114476</td>\n",
+       "      <td>0.003073</td>\n",
+       "      <td>0.066886</td>\n",
+       "      <td>0.121904</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>KShapeAD</td>\n",
-       "      <td>0.460106</td>\n",
-       "      <td>0.112541</td>\n",
-       "      <td>0.609905</td>\n",
-       "      <td>0.064609</td>\n",
-       "      <td>0.717890</td>\n",
-       "      <td>0.459063</td>\n",
-       "      <td>0.510537</td>\n",
-       "      <td>0.381310</td>\n",
-       "      <td>0.035690</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.907086</td>\n",
-       "      <td>0.721004</td>\n",
-       "      <td>0.420845</td>\n",
-       "      <td>0.606652</td>\n",
-       "      <td>0.357818</td>\n",
-       "      <td>0.347881</td>\n",
-       "      <td>0.811737</td>\n",
-       "      <td>0.122593</td>\n",
-       "      <td>0.810962</td>\n",
-       "      <td>0.491670</td>\n",
+       "      <td>0.000854</td>\n",
+       "      <td>0.081935</td>\n",
+       "      <td>0.464439</td>\n",
+       "      <td>0.095333</td>\n",
+       "      <td>0.001119</td>\n",
+       "      <td>0.148483</td>\n",
+       "      <td>0.101534</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>LOF</td>\n",
-       "      <td>0.051489</td>\n",
-       "      <td>0.102346</td>\n",
-       "      <td>0.172499</td>\n",
-       "      <td>0.123657</td>\n",
-       "      <td>0.710487</td>\n",
-       "      <td>0.186700</td>\n",
-       "      <td>0.090503</td>\n",
-       "      <td>0.121168</td>\n",
-       "      <td>0.134804</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.111105</td>\n",
-       "      <td>0.710584</td>\n",
-       "      <td>0.240338</td>\n",
-       "      <td>0.053749</td>\n",
-       "      <td>0.395130</td>\n",
-       "      <td>0.159200</td>\n",
-       "      <td>0.256447</td>\n",
-       "      <td>0.134985</td>\n",
-       "      <td>0.050189</td>\n",
-       "      <td>0.381508</td>\n",
+       "      <td>0.000653</td>\n",
+       "      <td>0.071959</td>\n",
+       "      <td>0.422520</td>\n",
+       "      <td>0.112061</td>\n",
+       "      <td>0.003879</td>\n",
+       "      <td>0.176712</td>\n",
+       "      <td>0.128972</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5 rows × 24 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "        Model       UCR       WSD      SMAP      IOPS     Stock       MSL  \\\n",
-       "0         CNN  0.065443  0.224922  0.336818  0.205826  0.939925  0.346350   \n",
-       "0     IForest  0.058773  0.027487  0.327727  0.134518  0.735886  0.448709   \n",
-       "0  KMeansAD_U  0.486822  0.173060  0.657738  0.107988  0.738198  0.441543   \n",
-       "0    KShapeAD  0.460106  0.112541  0.609905  0.064609  0.717890  0.459063   \n",
-       "0         LOF  0.051489  0.102346  0.172499  0.123657  0.710487  0.186700   \n",
-       "\n",
-       "      Power      SWaT   Daphnet  ...       SED       TAO  Exathlon     MITDB  \\\n",
-       "0  0.075487  0.680748  0.398456  ...  0.058416  0.994270  0.657757  0.143632   \n",
-       "0  0.076373  0.368230  0.518905  ...  0.807209  0.728970  0.964836  0.159042   \n",
-       "0  0.524166  0.111519  0.039747  ...  0.895291  0.747945  0.618051  0.590892   \n",
-       "0  0.510537  0.381310  0.035690  ...  0.907086  0.721004  0.420845  0.606652   \n",
-       "0  0.090503  0.121168  0.134804  ...  0.111105  0.710584  0.240338  0.053749   \n",
-       "\n",
-       "        NEK  OPPORTUNITY      LTDB       SMD      SVDB     YAHOO  \n",
-       "0  0.714845     0.632799  0.406171  0.545045  0.199785  0.512096  \n",
-       "0  0.649735     0.736264  0.566359  0.342406  0.307322  0.135664  \n",
-       "0  0.317927     0.351828  0.835553  0.254537  0.811385  0.781824  \n",
-       "0  0.357818     0.347881  0.811737  0.122593  0.810962  0.491670  \n",
-       "0  0.395130     0.159200  0.256447  0.134985  0.050189  0.381508  \n",
+       "        Model       UCR      TODS     YAHOO     Stock       WSD      IOPS  \\\n",
+       "0         CNN  0.000498  0.531847  0.587049  0.929344  0.342989  0.210157   \n",
+       "0     IForest  0.000139  0.069264  0.007312  0.143940  0.000501  0.011860   \n",
+       "0  KMeansAD_U  0.002843  0.083989  0.742578  0.114476  0.003073  0.066886   \n",
+       "0    KShapeAD  0.000854  0.081935  0.464439  0.095333  0.001119  0.148483   \n",
+       "0         LOF  0.000653  0.071959  0.422520  0.112061  0.003879  0.176712   \n",
        "\n",
-       "[5 rows x 24 columns]"
+       "        TAO  \n",
+       "0  0.993457  \n",
+       "0  0.126763  \n",
+       "0  0.121904  \n",
+       "0  0.101534  \n",
+       "0  0.128972  "
       ]
      },
-     "execution_count": 13,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -372,11 +291,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
-    "df.to_csv(path+'/groupedDataframes/'+col+'_combined_by_group.csv')"
+    "df.to_csv(path+'/groupedDataframes/'+col+'_'+anoType+'_combined_by_group.csv')"
    ]
   }
  ],
diff --git a/src/group_evaluation/tao_barplot.ipynb b/src/group_evaluation/tao_barplot.ipynb
new file mode 100644
index 0000000..08a4dca
--- /dev/null
+++ b/src/group_evaluation/tao_barplot.ipynb
@@ -0,0 +1,920 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import plotly.express as px"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Model</th>\n",
+       "      <th>VUS-PR</th>\n",
+       "      <th>Gruppe</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>PCA</td>\n",
+       "      <td>0.84</td>\n",
+       "      <td>Gemessen</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>PCA</td>\n",
+       "      <td>0.93</td>\n",
+       "      <td>Autoren</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>KShapeAD</td>\n",
+       "      <td>0.72</td>\n",
+       "      <td>Gemessen</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>KShapeAD</td>\n",
+       "      <td>0.91</td>\n",
+       "      <td>Autoren</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>POLY</td>\n",
+       "      <td>0.81</td>\n",
+       "      <td>Gemessen</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Model  VUS-PR    Gruppe\n",
+       "0       PCA    0.84  Gemessen\n",
+       "1       PCA    0.93   Autoren\n",
+       "2  KShapeAD    0.72  Gemessen\n",
+       "3  KShapeAD    0.91   Autoren\n",
+       "4      POLY    0.81  Gemessen"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = {\n",
+    "    'Model': ['PCA', 'PCA', 'KShapeAD', 'KShapeAD','POLY','POLY','KMeansAD_U','KMeansAD_U','IForest','IForest','Sub-IForest','Sub-IForest','LOF','LOF','Sub-LOF','Sub-LOF','OCSVM','OCSVM','Sub-KNN','Sub-KNN'],\n",
+    "    'VUS-PR': [0.84, 0.93,0.72,0.91, 0.81, 0.92,0.75, 0.92, 0.73, 0.99, 0.73, 0.90, 0.71, 0.91, 0.81, 0.92,0.72, 0.92, 0.75, 0.92 ],\n",
+    "    'Gruppe': ['Gemessen','Autoren','Gemessen','Autoren','Gemessen','Autoren','Gemessen','Autoren','Gemessen','Autoren','Gemessen','Autoren','Gemessen','Autoren','Gemessen','Autoren','Gemessen','Autoren','Gemessen','Autoren'],\n",
+    "}\n",
+    "\n",
+    "df = pd.DataFrame(data)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colors = {\n",
+    "    'Gemessen': 'steelblue',\n",
+    "    'Autoren': 'firebrick',#'gold' ,\n",
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+   "metadata": {},
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+     "text": [
+      "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\plotly\\express\\_core.py:1985: FutureWarning:\n",
+      "\n",
+      "When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
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+           "radialaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "showgrid": true,
+            "tickcolor": "rgb(51,51,51)",
+            "ticks": "outside"
+           }
+          },
+          "scene": {
+           "xaxis": {
+            "backgroundcolor": "rgb(237,237,237)",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "showgrid": true,
+            "tickcolor": "rgb(51,51,51)",
+            "ticks": "outside",
+            "zerolinecolor": "white"
+           },
+           "yaxis": {
+            "backgroundcolor": "rgb(237,237,237)",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "showgrid": true,
+            "tickcolor": "rgb(51,51,51)",
+            "ticks": "outside",
+            "zerolinecolor": "white"
+           },
+           "zaxis": {
+            "backgroundcolor": "rgb(237,237,237)",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "showgrid": true,
+            "tickcolor": "rgb(51,51,51)",
+            "ticks": "outside",
+            "zerolinecolor": "white"
+           }
+          },
+          "shapedefaults": {
+           "fillcolor": "black",
+           "line": {
+            "width": 0
+           },
+           "opacity": 0.3
+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "showgrid": true,
+            "tickcolor": "rgb(51,51,51)",
+            "ticks": "outside"
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "showgrid": true,
+            "tickcolor": "rgb(51,51,51)",
+            "ticks": "outside"
+           },
+           "bgcolor": "rgb(237,237,237)",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "showgrid": true,
+            "tickcolor": "rgb(51,51,51)",
+            "ticks": "outside"
+           }
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "showgrid": true,
+           "tickcolor": "rgb(51,51,51)",
+           "ticks": "outside",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white"
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "showgrid": true,
+           "tickcolor": "rgb(51,51,51)",
+           "ticks": "outside",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white"
+          }
+         }
+        },
+        "title": {
+         "text": "<b> Vergleich der Performanz der abweichendem Modelle auf dem TAO Datensatz </b>",
+         "x": 0.5,
+         "xanchor": "center",
+         "y": 0.96,
+         "yanchor": "top"
+        },
+        "width": 1000,
+        "xaxis": {
+         "anchor": "y",
+         "domain": [
+          0,
+          1
+         ],
+         "dtick": 1,
+         "tickangle": 0,
+         "title": {
+          "text": "Model"
+         }
+        },
+        "yaxis": {
+         "anchor": "x",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "VUS-PR"
+         }
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "fig = px.bar(df, x='Model', y='VUS-PR', color='Gruppe', barmode='group', color_discrete_map=colors,opacity=0.8, text_auto=True)\n",
+    "fig.update_layout( legend=dict(\n",
+    "    x= 0.67,\n",
+    "    y= 1,\n",
+    "    orientation = 'h',\n",
+    "    title_font_family=\"Times New Roman\",\n",
+    "    font=dict(\n",
+    "    family=\"Courier\",\n",
+    "    size=14,\n",
+    "    color=\"black\"\n",
+    "    ),\n",
+    "    title_text='Model',\n",
+    "    bgcolor=\"white\",\n",
+    "    bordercolor=\"lightgrey\",\n",
+    "    borderwidth=2,\n",
+    "    ),\n",
+    "    template = 'ggplot2',\n",
+    "    title={\n",
+    "            'text': '<b> Vergleich der Performanz der abweichendem Modelle auf dem TAO Datensatz </b>',\n",
+    "            'x':0.5,\n",
+    "            'y':0.96, \n",
+    "            'xanchor':'center',\n",
+    "            'yanchor':'top'},\n",
+    "    height=700,width=1000,\n",
+    "    )\n",
+    "fig.update_traces(marker=dict(size=12,\n",
+    "                    opacity=0.8,\n",
+    "                    line=dict(width=2,\n",
+    "                                    color='DarkSlateGrey'),\n",
+    "                            ),\n",
+    "            selector=dict(mode='markers'))\n",
+    "fig.update_xaxes( dtick = 1,tickangle = 360)\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
-- 
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