diff --git a/docs/evaluation/run_KMeansAD_U.csv b/docs/evaluation/KMeansAD_U.csv
similarity index 100%
rename from docs/evaluation/run_KMeansAD_U.csv
rename to docs/evaluation/KMeansAD_U.csv
diff --git a/docs/evaluation/run_LSTMAD.csv b/docs/evaluation/LSTMAD.csv
similarity index 100%
rename from docs/evaluation/run_LSTMAD.csv
rename to docs/evaluation/LSTMAD.csv
diff --git a/docs/evaluation/POLY2.csv b/docs/evaluation/POLY.csv
similarity index 100%
rename from docs/evaluation/POLY2.csv
rename to docs/evaluation/POLY.csv
diff --git a/docs/evaluation/groupedDataframes/VUS-PR_combined_by_group.csv b/docs/evaluation/groupedDataframes/VUS-PR_combined_by_group.csv
new file mode 100644
index 0000000000000000000000000000000000000000..0fa2f2b7a089d305ee3e0c14a2b3165e2cf4b53b
--- /dev/null
+++ b/docs/evaluation/groupedDataframes/VUS-PR_combined_by_group.csv
@@ -0,0 +1,9 @@
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diff --git a/docs/evaluation/groupedDataframes/auc_pr_combined_by_group.csv b/docs/evaluation/groupedDataframes/auc_pr_combined_by_group.csv
new file mode 100644
index 0000000000000000000000000000000000000000..82cf143feef6523a961b482ac8e62eba0146a93c
--- /dev/null
+++ b/docs/evaluation/groupedDataframes/auc_pr_combined_by_group.csv
@@ -0,0 +1,9 @@
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diff --git a/docs/evaluation/run_Sub_PCA3.csv b/docs/evaluation/temp/run_Sub_PCA3.csv
similarity index 100%
rename from docs/evaluation/run_Sub_PCA3.csv
rename to docs/evaluation/temp/run_Sub_PCA3.csv
diff --git a/docs/evaluation/run_Sub_PCA4.csv b/docs/evaluation/temp/run_Sub_PCA4.csv
similarity index 100%
rename from docs/evaluation/run_Sub_PCA4.csv
rename to docs/evaluation/temp/run_Sub_PCA4.csv
diff --git a/docs/evaluation/run_Sub_PCA6.csv b/docs/evaluation/temp/run_Sub_PCA6.csv
similarity index 100%
rename from docs/evaluation/run_Sub_PCA6.csv
rename to docs/evaluation/temp/run_Sub_PCA6.csv
diff --git a/docs/evaluation/run_Sub_PCA7.csv b/docs/evaluation/temp/run_Sub_PCA7.csv
similarity index 100%
rename from docs/evaluation/run_Sub_PCA7.csv
rename to docs/evaluation/temp/run_Sub_PCA7.csv
diff --git a/src/group_evaluation/create_heatmap.ipynb b/src/group_evaluation/create_heatmap.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..2a4e81e8308ac5bf671ecc2e3560f5160f8f1dca
--- /dev/null
+++ b/src/group_evaluation/create_heatmap.ipynb
@@ -0,0 +1,41435 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import plotly.graph_objects as go\n",
+ "import pandas as pd"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>IOPS</th>\n",
+ " <th>SMAP</th>\n",
+ " <th>YAHOO</th>\n",
+ " <th>UCR</th>\n",
+ " <th>Daphnet</th>\n",
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+ " <th>NAB</th>\n",
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+ " <th>CATSv2</th>\n",
+ " <th>TAO</th>\n",
+ " <th>...</th>\n",
+ " <th>SWaT</th>\n",
+ " <th>MITDB</th>\n",
+ " <th>WSD</th>\n",
+ " <th>Exathlon</th>\n",
+ " <th>OPPORTUNITY</th>\n",
+ " <th>NEK</th>\n",
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+ " <th>MGAB</th>\n",
+ " <th>SMD</th>\n",
+ " <th>Stock</th>\n",
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+ " <tr>\n",
+ " <th>Model</th>\n",
+ " <th></th>\n",
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+ " <th></th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>CNN</th>\n",
+ " <td>0.205826</td>\n",
+ " <td>0.336818</td>\n",
+ " <td>0.512096</td>\n",
+ " <td>0.065443</td>\n",
+ " <td>0.398456</td>\n",
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+ " <td>0.199785</td>\n",
+ " <td>0.288293</td>\n",
+ " <td>0.994270</td>\n",
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+ " <td>0.143632</td>\n",
+ " <td>0.224922</td>\n",
+ " <td>0.657757</td>\n",
+ " <td>0.632799</td>\n",
+ " <td>0.714845</td>\n",
+ " <td>0.058416</td>\n",
+ " <td>0.006988</td>\n",
+ " <td>0.545045</td>\n",
+ " <td>0.939925</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>KMeansAD_U</th>\n",
+ " <td>0.107988</td>\n",
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+ " <td>0.747945</td>\n",
+ " <td>...</td>\n",
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+ " <td>0.590892</td>\n",
+ " <td>0.173060</td>\n",
+ " <td>0.618051</td>\n",
+ " <td>0.351828</td>\n",
+ " <td>0.317927</td>\n",
+ " <td>0.895291</td>\n",
+ " <td>0.036645</td>\n",
+ " <td>0.254537</td>\n",
+ " <td>0.738198</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>LSTMAD</th>\n",
+ " <td>0.213044</td>\n",
+ " <td>0.273214</td>\n",
+ " <td>0.417416</td>\n",
+ " <td>0.023293</td>\n",
+ " <td>0.133080</td>\n",
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+ " <td>0.995044</td>\n",
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+ " <td>0.025419</td>\n",
+ " <td>0.483944</td>\n",
+ " <td>0.817960</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>OmniAnomaly</th>\n",
+ " <td>0.192837</td>\n",
+ " <td>0.417852</td>\n",
+ " <td>0.219787</td>\n",
+ " <td>0.089552</td>\n",
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+ " <td>0.282282</td>\n",
+ " <td>0.930020</td>\n",
+ " <td>...</td>\n",
+ " <td>0.366250</td>\n",
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+ " <td>0.157612</td>\n",
+ " <td>0.876372</td>\n",
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+ " <td>0.005586</td>\n",
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+ " <td>0.771658</td>\n",
+ " </tr>\n",
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+ " <th>PCA</th>\n",
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+ " <td>0.030270</td>\n",
+ " <td>0.005551</td>\n",
+ " <td>0.467257</td>\n",
+ " <td>0.825382</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>5 rows × 23 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " IOPS SMAP YAHOO UCR Daphnet TODS \\\n",
+ "Model \n",
+ "CNN 0.205826 0.336818 0.512096 0.065443 0.398456 0.575941 \n",
+ "KMeansAD_U 0.107988 0.657738 0.781824 0.486822 0.039747 0.739257 \n",
+ "LSTMAD 0.213044 0.273214 0.417416 0.023293 0.133080 0.522228 \n",
+ "OmniAnomaly 0.192837 0.417852 0.219787 0.089552 0.123026 0.538194 \n",
+ "PCA 0.260372 0.566842 0.163729 0.152678 0.441568 0.552877 \n",
+ "\n",
+ " NAB SVDB CATSv2 TAO ... SWaT MITDB \\\n",
+ "Model ... \n",
+ "CNN 0.248091 0.199785 0.288293 0.994270 ... 0.680748 0.143632 \n",
+ "KMeansAD_U 0.427477 0.811385 0.229268 0.747945 ... 0.111519 0.590892 \n",
+ "LSTMAD 0.173732 0.127489 0.329056 0.995044 ... 0.669824 0.112398 \n",
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+ "\n",
+ " SMD Stock \n",
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+ ],
+ [
+ 0.5,
+ "#f7f7f7"
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+ [
+ 0.6,
+ "#e6f5d0"
+ ],
+ [
+ 0.7,
+ "#b8e186"
+ ],
+ [
+ 0.8,
+ "#7fbc41"
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+ [
+ 0.9,
+ "#4d9221"
+ ],
+ [
+ 1,
+ "#276419"
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+ ],
+ "sequential": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
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+ [
+ 0.3333333333333333,
+ "#9c179e"
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+ [
+ 0.4444444444444444,
+ "#bd3786"
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+ [
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+ 0.6666666666666666,
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+ "sequentialminus": [
+ [
+ 0,
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+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ]
+ },
+ "colorway": [
+ "#636efa",
+ "#EF553B",
+ "#00cc96",
+ "#ab63fa",
+ "#FFA15A",
+ "#19d3f3",
+ "#FF6692",
+ "#B6E880",
+ "#FF97FF",
+ "#FECB52"
+ ],
+ "font": {
+ "color": "#2a3f5f"
+ },
+ "geo": {
+ "bgcolor": "white",
+ "lakecolor": "white",
+ "landcolor": "#E5ECF6",
+ "showlakes": true,
+ "showland": true,
+ "subunitcolor": "white"
+ },
+ "hoverlabel": {
+ "align": "left"
+ },
+ "hovermode": "closest",
+ "mapbox": {
+ "style": "light"
+ },
+ "paper_bgcolor": "white",
+ "plot_bgcolor": "#E5ECF6",
+ "polar": {
+ "angularaxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "bgcolor": "#E5ECF6",
+ "radialaxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ }
+ },
+ "scene": {
+ "xaxis": {
+ "backgroundcolor": "#E5ECF6",
+ "gridcolor": "white",
+ "gridwidth": 2,
+ "linecolor": "white",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "white"
+ },
+ "yaxis": {
+ "backgroundcolor": "#E5ECF6",
+ "gridcolor": "white",
+ "gridwidth": 2,
+ "linecolor": "white",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "white"
+ },
+ "zaxis": {
+ "backgroundcolor": "#E5ECF6",
+ "gridcolor": "white",
+ "gridwidth": 2,
+ "linecolor": "white",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "white"
+ }
+ },
+ "shapedefaults": {
+ "line": {
+ "color": "#2a3f5f"
+ }
+ },
+ "ternary": {
+ "aaxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "baxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "bgcolor": "#E5ECF6",
+ "caxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ }
+ },
+ "title": {
+ "x": 0.05
+ },
+ "xaxis": {
+ "automargin": true,
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": "",
+ "title": {
+ "standoff": 15
+ },
+ "zerolinecolor": "white",
+ "zerolinewidth": 2
+ },
+ "yaxis": {
+ "automargin": true,
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": "",
+ "title": {
+ "standoff": 15
+ },
+ "zerolinecolor": "white",
+ "zerolinewidth": 2
+ }
+ }
+ },
+ "title": {
+ "text": "Darkmint Heatmap der VUS-PRvon den einzelnen Modelle auf den jeweiligen Datensätzen"
+ },
+ "xaxis": {
+ "title": {
+ "text": "Datensätze"
+ }
+ },
+ "yaxis": {
+ "title": {
+ "text": "Modelle"
+ }
+ }
+ }
+ }
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "color_scales = [\n",
+ " \"Viridis\", \"Cividis\",\n",
+ " \"Blues\", \"BuGn\", \"BuPu\", \"GnBu\", \"Greens\", \"Greys\", \"Oranges\", \"OrRd\",\n",
+ " \"PuBu\", \"PuBuGn\", \"PuRd\", \"Purples\", \"RdPu\", \"Reds\", \"YlGn\", \"YlGnBu\",\n",
+ " \"YlOrBr\", \"YlOrRd\", \"Aggrnyl\", \"Agsunset\", \"Solar\", \"Teal\", \"Tealgrn\",\n",
+ " \"Temps\", \"Sunset\", \"Sunsetdark\", \"Mint\", \"Emrld\", \"Darkmint\",\n",
+ "]\n",
+ "\n",
+ "for scale in color_scales:\n",
+ " fig = go.Figure(go.Heatmap(\n",
+ " z= df.values,\n",
+ " x= df.columns, \n",
+ " y= df.index,\n",
+ " colorscale=scale,#'Viridis',\n",
+ " text= df.values,\n",
+ " texttemplate=\"%{text:.2f}\",\n",
+ " textfont=dict(size=12)\n",
+ " ))\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",
+ " yaxis=dict(title='Modelle')\n",
+ " )\n",
+ "\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
+}
diff --git a/src/group_evaluation/generate_combined_dataframe.ipynb b/src/group_evaluation/generate_combined_dataframe.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..fbd8b790bbcb3490b36564cf10e509cb7b08c519
--- /dev/null
+++ b/src/group_evaluation/generate_combined_dataframe.ipynb
@@ -0,0 +1,589 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 57,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import os"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 64,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "path = '../../docs/evaluation/'\n",
+ "#get list of all groups\n",
+ "temp_df = pd.read_csv(path + 'PCA.csv')\n",
+ "keys = list(set(list(temp_df['group'])))\n",
+ "keys.insert(0,'Model')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "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>IOPS</th>\n",
+ " <th>SMAP</th>\n",
+ " <th>YAHOO</th>\n",
+ " <th>UCR</th>\n",
+ " <th>Daphnet</th>\n",
+ " <th>TODS</th>\n",
+ " <th>NAB</th>\n",
+ " <th>SVDB</th>\n",
+ " <th>CATSv2</th>\n",
+ " <th>...</th>\n",
+ " <th>SWaT</th>\n",
+ " <th>MITDB</th>\n",
+ " <th>WSD</th>\n",
+ " <th>Exathlon</th>\n",
+ " <th>OPPORTUNITY</th>\n",
+ " <th>NEK</th>\n",
+ " <th>SED</th>\n",
+ " <th>MGAB</th>\n",
+ " <th>SMD</th>\n",
+ " <th>Stock</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, IOPS, SMAP, YAHOO, UCR, Daphnet, TODS, NAB, SVDB, CATSv2, TAO, LTDB, MSL, Power, SWaT, MITDB, WSD, Exathlon, OPPORTUNITY, NEK, SED, MGAB, SMD, Stock]\n",
+ "Index: []\n",
+ "\n",
+ "[0 rows x 24 columns]"
+ ]
+ },
+ "execution_count": 65,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#create enpty dataframes with dataset group as column\n",
+ "data = {key: [] for key in keys}\n",
+ "df = pd.DataFrame(data)\n",
+ "df.set_index('Model')\n",
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Found: CNN.csv\n",
+ "Model\n",
+ "IOPS\n",
+ "SMAP\n",
+ "YAHOO\n",
+ "UCR\n",
+ "Daphnet\n",
+ "TODS\n",
+ "NAB\n",
+ "SVDB\n",
+ "CATSv2\n",
+ "TAO\n",
+ "LTDB\n",
+ "MSL\n",
+ "Power\n",
+ "SWaT\n",
+ "MITDB\n",
+ "WSD\n",
+ "Exathlon\n",
+ "OPPORTUNITY\n",
+ "NEK\n",
+ "SED\n",
+ "MGAB\n",
+ "SMD\n",
+ "Stock\n",
+ "Not correct file type: groupedDataframes\n",
+ "Found: KMeansAD_U.csv\n",
+ "Model\n",
+ "IOPS\n",
+ "SMAP\n",
+ "YAHOO\n",
+ "UCR\n",
+ "Daphnet\n",
+ "TODS\n",
+ "NAB\n",
+ "SVDB\n",
+ "CATSv2\n",
+ "TAO\n",
+ "LTDB\n",
+ "MSL\n",
+ "Power\n",
+ "SWaT\n",
+ "MITDB\n",
+ "WSD\n",
+ "Exathlon\n",
+ "OPPORTUNITY\n",
+ "NEK\n",
+ "SED\n",
+ "MGAB\n",
+ "SMD\n",
+ "Stock\n",
+ "Found: LSTMAD.csv\n",
+ "Model\n",
+ "IOPS\n",
+ "SMAP\n",
+ "YAHOO\n",
+ "UCR\n",
+ "Daphnet\n",
+ "TODS\n",
+ "NAB\n",
+ "SVDB\n",
+ "CATSv2\n",
+ "TAO\n",
+ "LTDB\n",
+ "MSL\n",
+ "Power\n",
+ "SWaT\n",
+ "MITDB\n",
+ "WSD\n",
+ "Exathlon\n",
+ "OPPORTUNITY\n",
+ "NEK\n",
+ "SED\n",
+ "MGAB\n",
+ "SMD\n",
+ "Stock\n",
+ "Found: OmniAnomaly.csv\n",
+ "Model\n",
+ "IOPS\n",
+ "SMAP\n",
+ "YAHOO\n",
+ "UCR\n",
+ "Daphnet\n",
+ "TODS\n",
+ "NAB\n",
+ "SVDB\n",
+ "CATSv2\n",
+ "TAO\n",
+ "LTDB\n",
+ "MSL\n",
+ "Power\n",
+ "SWaT\n",
+ "MITDB\n",
+ "WSD\n",
+ "Exathlon\n",
+ "OPPORTUNITY\n",
+ "NEK\n",
+ "SED\n",
+ "MGAB\n",
+ "SMD\n",
+ "Stock\n",
+ "Found: PCA.csv\n",
+ "Model\n",
+ "IOPS\n",
+ "SMAP\n",
+ "YAHOO\n",
+ "UCR\n",
+ "Daphnet\n",
+ "TODS\n",
+ "NAB\n",
+ "SVDB\n",
+ "CATSv2\n",
+ "TAO\n",
+ "LTDB\n",
+ "MSL\n",
+ "Power\n",
+ "SWaT\n",
+ "MITDB\n",
+ "WSD\n",
+ "Exathlon\n",
+ "OPPORTUNITY\n",
+ "NEK\n",
+ "SED\n",
+ "MGAB\n",
+ "SMD\n",
+ "Stock\n",
+ "Found: POLY.csv\n",
+ "Model\n",
+ "IOPS\n",
+ "SMAP\n",
+ "YAHOO\n",
+ "UCR\n",
+ "Daphnet\n",
+ "TODS\n",
+ "NAB\n",
+ "SVDB\n",
+ "CATSv2\n",
+ "TAO\n",
+ "LTDB\n",
+ "MSL\n",
+ "Power\n",
+ "SWaT\n",
+ "MITDB\n",
+ "WSD\n",
+ "Exathlon\n",
+ "OPPORTUNITY\n",
+ "NEK\n",
+ "SED\n",
+ "MGAB\n",
+ "SMD\n",
+ "Stock\n",
+ "Found: Sub_KNN.csv\n",
+ "Model\n",
+ "IOPS\n",
+ "SMAP\n",
+ "YAHOO\n",
+ "UCR\n",
+ "Daphnet\n",
+ "TODS\n",
+ "NAB\n",
+ "SVDB\n",
+ "CATSv2\n",
+ "TAO\n",
+ "LTDB\n",
+ "MSL\n",
+ "Power\n",
+ "SWaT\n",
+ "MITDB\n",
+ "WSD\n",
+ "Exathlon\n",
+ "OPPORTUNITY\n",
+ "NEK\n",
+ "SED\n",
+ "MGAB\n",
+ "SMD\n",
+ "Stock\n",
+ "Not correct file type: temp\n",
+ "Found: USAD.csv\n",
+ "Model\n",
+ "IOPS\n",
+ "SMAP\n",
+ "YAHOO\n",
+ "UCR\n",
+ "Daphnet\n",
+ "TODS\n",
+ "NAB\n",
+ "SVDB\n",
+ "CATSv2\n",
+ "TAO\n",
+ "LTDB\n",
+ "MSL\n",
+ "Power\n",
+ "SWaT\n",
+ "MITDB\n",
+ "WSD\n",
+ "Exathlon\n",
+ "OPPORTUNITY\n",
+ "NEK\n",
+ "SED\n",
+ "MGAB\n",
+ "SMD\n",
+ "Stock\n",
+ "Not correct file type: Vergleich der Ergebnisse.md\n"
+ ]
+ }
+ ],
+ "source": [
+ "col = 'VUS-PR'\n",
+ "\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",
+ " #read Data\n",
+ " df_tmp = pd.read_csv(path+file)\n",
+ " #select cols\n",
+ " df_tmp = df_tmp[['group',col]]\n",
+ " #Group Dataframe and get mean for each group\n",
+ " groupedMeans = df_tmp.groupby('group').mean()\n",
+ " #turn group col from index into col\n",
+ " groupedMeans = groupedMeans.reset_index()\n",
+ " #merge key value paires for dict\n",
+ " keys_temp = data.copy()\n",
+ " for key in keys_temp:\n",
+ " print(key)\n",
+ " if key in groupedMeans['group'].values:\n",
+ " value = groupedMeans.loc[groupedMeans['group'] == key, col].values[0]\n",
+ " keys_temp[key] = [value]\n",
+ " else:\n",
+ " keys_temp[key] = []\n",
+ " keys_temp['Model'] = suffix[0]\n",
+ " df_tmp = pd.DataFrame(keys_temp)\n",
+ " df = pd.concat([df,df_tmp])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "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>IOPS</th>\n",
+ " <th>SMAP</th>\n",
+ " <th>YAHOO</th>\n",
+ " <th>UCR</th>\n",
+ " <th>Daphnet</th>\n",
+ " <th>TODS</th>\n",
+ " <th>NAB</th>\n",
+ " <th>SVDB</th>\n",
+ " <th>CATSv2</th>\n",
+ " <th>...</th>\n",
+ " <th>SWaT</th>\n",
+ " <th>MITDB</th>\n",
+ " <th>WSD</th>\n",
+ " <th>Exathlon</th>\n",
+ " <th>OPPORTUNITY</th>\n",
+ " <th>NEK</th>\n",
+ " <th>SED</th>\n",
+ " <th>MGAB</th>\n",
+ " <th>SMD</th>\n",
+ " <th>Stock</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>CNN</td>\n",
+ " <td>0.205826</td>\n",
+ " <td>0.336818</td>\n",
+ " <td>0.512096</td>\n",
+ " <td>0.065443</td>\n",
+ " <td>0.398456</td>\n",
+ " <td>0.575941</td>\n",
+ " <td>0.248091</td>\n",
+ " <td>0.199785</td>\n",
+ " <td>0.288293</td>\n",
+ " <td>...</td>\n",
+ " <td>0.680748</td>\n",
+ " <td>0.143632</td>\n",
+ " <td>0.224922</td>\n",
+ " <td>0.657757</td>\n",
+ " <td>0.632799</td>\n",
+ " <td>0.714845</td>\n",
+ " <td>0.058416</td>\n",
+ " <td>0.006988</td>\n",
+ " <td>0.545045</td>\n",
+ " <td>0.939925</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>KMeansAD_U</td>\n",
+ " <td>0.107988</td>\n",
+ " <td>0.657738</td>\n",
+ " <td>0.781824</td>\n",
+ " <td>0.486822</td>\n",
+ " <td>0.039747</td>\n",
+ " <td>0.739257</td>\n",
+ " <td>0.427477</td>\n",
+ " <td>0.811385</td>\n",
+ " <td>0.229268</td>\n",
+ " <td>...</td>\n",
+ " <td>0.111519</td>\n",
+ " <td>0.590892</td>\n",
+ " <td>0.173060</td>\n",
+ " <td>0.618051</td>\n",
+ " <td>0.351828</td>\n",
+ " <td>0.317927</td>\n",
+ " <td>0.895291</td>\n",
+ " <td>0.036645</td>\n",
+ " <td>0.254537</td>\n",
+ " <td>0.738198</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>LSTMAD</td>\n",
+ " <td>0.213044</td>\n",
+ " <td>0.273214</td>\n",
+ " <td>0.417416</td>\n",
+ " <td>0.023293</td>\n",
+ " <td>0.133080</td>\n",
+ " <td>0.522228</td>\n",
+ " <td>0.173732</td>\n",
+ " <td>0.127489</td>\n",
+ " <td>0.329056</td>\n",
+ " <td>...</td>\n",
+ " <td>0.669824</td>\n",
+ " <td>0.112398</td>\n",
+ " <td>0.130270</td>\n",
+ " <td>0.735253</td>\n",
+ " <td>0.563772</td>\n",
+ " <td>0.689697</td>\n",
+ " <td>0.056562</td>\n",
+ " <td>0.025419</td>\n",
+ " <td>0.483944</td>\n",
+ " <td>0.817960</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>OmniAnomaly</td>\n",
+ " <td>0.192837</td>\n",
+ " <td>0.417852</td>\n",
+ " <td>0.219787</td>\n",
+ " <td>0.089552</td>\n",
+ " <td>0.123026</td>\n",
+ " <td>0.538194</td>\n",
+ " <td>0.342007</td>\n",
+ " <td>0.445289</td>\n",
+ " <td>0.282282</td>\n",
+ " <td>...</td>\n",
+ " <td>0.366250</td>\n",
+ " <td>0.163465</td>\n",
+ " <td>0.157612</td>\n",
+ " <td>0.876372</td>\n",
+ " <td>0.649931</td>\n",
+ " <td>0.858349</td>\n",
+ " <td>0.055985</td>\n",
+ " <td>0.005586</td>\n",
+ " <td>0.478742</td>\n",
+ " <td>0.771658</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>PCA</td>\n",
+ " <td>0.260372</td>\n",
+ " <td>0.566842</td>\n",
+ " <td>0.163729</td>\n",
+ " <td>0.152678</td>\n",
+ " <td>0.441568</td>\n",
+ " <td>0.552877</td>\n",
+ " <td>0.511398</td>\n",
+ " <td>0.523984</td>\n",
+ " <td>0.232417</td>\n",
+ " <td>...</td>\n",
+ " <td>0.375512</td>\n",
+ " <td>0.323526</td>\n",
+ " <td>0.155366</td>\n",
+ " <td>0.959173</td>\n",
+ " <td>0.924516</td>\n",
+ " <td>0.929171</td>\n",
+ " <td>0.030270</td>\n",
+ " <td>0.005551</td>\n",
+ " <td>0.467257</td>\n",
+ " <td>0.825382</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>5 rows × 24 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Model IOPS SMAP YAHOO UCR Daphnet TODS \\\n",
+ "0 CNN 0.205826 0.336818 0.512096 0.065443 0.398456 0.575941 \n",
+ "0 KMeansAD_U 0.107988 0.657738 0.781824 0.486822 0.039747 0.739257 \n",
+ "0 LSTMAD 0.213044 0.273214 0.417416 0.023293 0.133080 0.522228 \n",
+ "0 OmniAnomaly 0.192837 0.417852 0.219787 0.089552 0.123026 0.538194 \n",
+ "0 PCA 0.260372 0.566842 0.163729 0.152678 0.441568 0.552877 \n",
+ "\n",
+ " NAB SVDB CATSv2 ... SWaT MITDB WSD Exathlon \\\n",
+ "0 0.248091 0.199785 0.288293 ... 0.680748 0.143632 0.224922 0.657757 \n",
+ "0 0.427477 0.811385 0.229268 ... 0.111519 0.590892 0.173060 0.618051 \n",
+ "0 0.173732 0.127489 0.329056 ... 0.669824 0.112398 0.130270 0.735253 \n",
+ "0 0.342007 0.445289 0.282282 ... 0.366250 0.163465 0.157612 0.876372 \n",
+ "0 0.511398 0.523984 0.232417 ... 0.375512 0.323526 0.155366 0.959173 \n",
+ "\n",
+ " OPPORTUNITY NEK SED MGAB SMD Stock \n",
+ "0 0.632799 0.714845 0.058416 0.006988 0.545045 0.939925 \n",
+ "0 0.351828 0.317927 0.895291 0.036645 0.254537 0.738198 \n",
+ "0 0.563772 0.689697 0.056562 0.025419 0.483944 0.817960 \n",
+ "0 0.649931 0.858349 0.055985 0.005586 0.478742 0.771658 \n",
+ "0 0.924516 0.929171 0.030270 0.005551 0.467257 0.825382 \n",
+ "\n",
+ "[5 rows x 24 columns]"
+ ]
+ },
+ "execution_count": 67,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#df.to_csv(path+'/groupedDataframes/'+col+'_combined_by_group.csv')"
+ ]
+ }
+ ],
+ "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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