diff --git a/src/models/desi/desi_evaluate_groups.ipynb b/src/models/desi/desi_evaluate_groups.ipynb
index bf1a83d341c939b12ef0437180bf4a9841c830bf..18e5989b6870a67095b367d6731b4141b3f67f97 100644
--- a/src/models/desi/desi_evaluate_groups.ipynb
+++ b/src/models/desi/desi_evaluate_groups.ipynb
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+ "hovertemplate": "<b>%{hovertext}</b><br><br>group=%{customdata[0]}<br>anomaly=%{x}<br>VUS-PR=%{y}<extra></extra>",
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+ "hovertemplate": "<b>%{hovertext}</b><br><br>group=%{customdata[0]}<br>anomaly=%{x}<br>VUS-PR=%{y}<extra></extra>",
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"xaxis": "x",
"y": [
- 0.9718491015673606,
- 0.4945030819575546,
- 0.0111357415519349,
- 0.0125630075305213,
- 0.9178179727951572,
- 0.9593104589769668,
- 0.625724963271805,
- 0.0186878621532751,
- 0.914291235318628,
- 0.0190607721577046,
- 0.5187870275682767,
- 0.8095612578614829,
- 0.843748197767318
+ 0.9708542809813376,
+ 0.5100187118489438,
+ 0.0126849291325253,
+ 0.0135167882658251,
+ 0.9653797242887892,
+ 0.9745536964616012,
+ 0.6931910397490794,
+ 0.0206942972017477,
+ 0.9477772157758838,
+ 0.0201098826188157,
+ 0.5220683014942072,
+ 0.8490685089085376,
+ 0.8717597131009235
],
"y0": " ",
"yaxis": "y"
@@ -5435,7 +5433,7 @@
1
],
"title": {
- "text": "AUC-PR"
+ "text": "VUS-PR"
}
}
}
@@ -5447,7 +5445,7 @@
],
"source": [
"#Boxplot for anomalies\n",
- "metric = 'AUC-PR'\n",
+ "metric = 'VUS-PR'\n",
"fig = px.box(df_anomaly, x = 'anomaly',y=metric, color='group',color_discrete_map= colors,hover_name=\"group\", hover_data=[\"group\"])\n",
"fig.update_layout( \n",
" template = 'ggplot2',\n",
@@ -5469,6 +5467,122 @@
"fig.show()"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " duration AUC-PR AUC-ROC VUS-PR VUS-ROC Standard-F1 \\\n",
+ "group \n",
+ "CATSv2 70.652527 0.358071 0.671672 0.329056 0.694311 0.426442 \n",
+ "Daphnet 26.989239 0.132234 0.418213 0.133080 0.427408 0.236743 \n",
+ "Exathlon 26.784657 0.733875 0.839358 0.735253 0.844099 0.759256 \n",
+ "IOPS 28.707300 0.320195 0.764905 0.213044 0.838156 0.389598 \n",
+ "LTDB 63.727441 0.276735 0.569772 0.329828 0.658579 0.341555 \n",
+ "MGAB 36.815743 0.045860 0.571003 0.025419 0.780171 0.120355 \n",
+ "MITDB 240.941847 0.113066 0.575368 0.112398 0.648662 0.156766 \n",
+ "MSL 2.516762 0.237442 0.623682 0.305128 0.702196 0.346157 \n",
+ "NAB 4.075609 0.153306 0.504177 0.173732 0.561732 0.240700 \n",
+ "NEK 0.899027 0.667421 0.845031 0.689697 0.898101 0.683098 \n",
+ "OPPORTUNITY 6.970732 0.545535 0.678946 0.563772 0.683432 0.624304 \n",
+ "Power 27.608061 0.064788 0.359705 0.067250 0.373181 0.157935 \n",
+ "SED 19.483207 0.030497 0.366075 0.056562 0.546872 0.083031 \n",
+ "SMAP 3.499126 0.278827 0.662888 0.273214 0.717289 0.350976 \n",
+ "SMD 6.188577 0.539890 0.821128 0.483944 0.858948 0.582395 \n",
+ "SVDB 61.223802 0.129879 0.633003 0.127489 0.702458 0.178644 \n",
+ "SWaT 128.076170 0.737695 0.828700 0.669824 0.820193 0.790613 \n",
+ "Stock 295.751233 0.519232 0.488192 0.817960 0.906313 0.653335 \n",
+ "TAO 163.842410 0.993397 0.996664 0.995044 0.999209 0.994867 \n",
+ "TODS 10.916495 0.205671 0.532069 0.522228 0.816347 0.257129 \n",
+ "UCR 37.781606 0.028041 0.605540 0.023293 0.679216 0.055109 \n",
+ "WSD 10.785602 0.209492 0.697083 0.130270 0.823629 0.273168 \n",
+ "YAHOO 1.763641 0.443957 0.728624 0.417416 0.871581 0.473548 \n",
+ "\n",
+ " PA-F1 Event-based-F1 R-based-F1 Affiliation-F Recall \\\n",
+ "group \n",
+ "CATSv2 0.505051 0.100000 0.030394 0.677806 0.010608 \n",
+ "Daphnet 0.992255 0.778325 0.309125 0.923358 0.061145 \n",
+ "Exathlon 0.953842 0.921167 0.460834 0.960718 0.148109 \n",
+ "IOPS 0.713355 0.640416 0.390190 0.856602 0.216378 \n",
+ "LTDB 0.843457 0.640913 0.281689 0.813421 0.034194 \n",
+ "MGAB 0.501671 0.368894 0.183225 0.765091 0.108175 \n",
+ "MITDB 0.865574 0.510863 0.192051 0.817963 0.050266 \n",
+ "MSL 0.821691 0.679834 0.386690 0.895687 0.088714 \n",
+ "NAB 0.935755 0.721129 0.413728 0.859832 0.029048 \n",
+ "NEK 0.985692 0.952862 0.470029 0.949782 0.164960 \n",
+ "OPPORTUNITY 0.732896 0.673800 0.414382 0.915580 0.353342 \n",
+ "Power 0.776197 0.157921 0.240411 0.678766 0.000667 \n",
+ "SED 0.078859 0.078859 0.015015 0.669603 0.000000 \n",
+ "SMAP 0.828653 0.808116 0.381508 0.948423 0.129299 \n",
+ "SMD 0.976127 0.941175 0.439749 0.956396 0.311674 \n",
+ "SVDB 0.913646 0.690670 0.245988 0.853885 0.071883 \n",
+ "SWaT 0.937086 0.793675 0.239082 0.719987 0.654887 \n",
+ "Stock 0.691291 0.672879 0.656718 0.687735 0.242005 \n",
+ "TAO 0.994872 0.994897 0.995514 0.996286 0.451029 \n",
+ "TODS 0.548961 0.377087 0.359258 0.724617 0.047110 \n",
+ "UCR 0.413236 0.181246 0.109963 0.802254 0.055210 \n",
+ "WSD 0.581095 0.509569 0.288492 0.863097 0.201021 \n",
+ "YAHOO 0.481749 0.479901 0.489514 0.868692 0.454894 \n",
+ "\n",
+ " Precision \n",
+ "group \n",
+ "CATSv2 1.000000 \n",
+ "Daphnet 0.582645 \n",
+ "Exathlon 0.834452 \n",
+ "IOPS 0.494514 \n",
+ "LTDB 0.420027 \n",
+ "MGAB 0.022300 \n",
+ "MITDB 0.388797 \n",
+ "MSL 0.497302 \n",
+ "NAB 0.402316 \n",
+ "NEK 1.000000 \n",
+ "OPPORTUNITY 0.422735 \n",
+ "Power 0.028571 \n",
+ "SED 0.000000 \n",
+ "SMAP 0.566717 \n",
+ "SMD 0.871311 \n",
+ "SVDB 0.413539 \n",
+ "SWaT 0.996935 \n",
+ "Stock 0.693638 \n",
+ "TAO 0.663512 \n",
+ "TODS 0.420484 \n",
+ "UCR 0.014105 \n",
+ "WSD 0.415571 \n",
+ "YAHOO 0.265364 \n"
+ ]
+ }
+ ],
+ "source": [
+ "# Mean pro gruppe\n",
+ "numerical_df = df.select_dtypes(include=['number'])\n",
+ "mean_groups = df.groupby('group')[numerical_df.columns].mean()\n",
+ "print(mean_groups)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Gesamtdauer Training in s: 13171.054, in min: 219.518, in h: 36.586\n",
+ "Durchschnittliches Training pro Datei in s: 33.17645774320031\n"
+ ]
+ }
+ ],
+ "source": [
+ "duration = df['duration'].sum()\n",
+ "print(f'Gesamtdauer Training in s: {duration:.3f}, in min: {(duration/60):.3f}, in h: {(duration/360):.3f}')\n",
+ "print(f'Durchschnittliches Training pro Datei in s: {(duration / df.shape[0]):.3f}')"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
diff --git a/src/template_evaluate_groups.ipynb b/src/template_evaluate_groups.ipynb
index 4a78777cc0c33c7e78355e4eca4870ea415ef827..d1ab6bf63329d3340d1333c2330aa7a507460d0d 100644
--- a/src/template_evaluate_groups.ipynb
+++ b/src/template_evaluate_groups.ipynb
@@ -5430,7 +5430,23 @@
"execution_count": null,
"metadata": {},
"outputs": [],
- "source": []
+ "source": [
+ "duration = df['duration'].sum()\n",
+ "print(f'Gesamtdauer Training in s: {duration:.3f}, in min: {(duration/60):.3f}, in h: {(duration/360):.3f}')\n",
+ "print(f'Durchschnittliches Training pro Datei in s: {(duration / df.shape[0]):.3f}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Mean der Metriken pro gruppe\n",
+ "numerical_df = df.select_dtypes(include=['number'])\n",
+ "mean_groups = df.groupby('group')[numerical_df.columns].mean()\n",
+ "print(mean_groups)"
+ ]
}
],
"metadata": {