From 9d71fc0b957d1bd71ea76a3ec118b290d1e5ea26 Mon Sep 17 00:00:00 2001
From: DesireeWyrzylala <74858504+DesireeWyrzylala@users.noreply.github.com>
Date: Thu, 6 Feb 2025 12:16:52 +0100
Subject: [PATCH] adjust colors and check lstmad results start cnn run
---
src/models/desi/desi_evaluate_groups.ipynb | 5430 +++++++++++++++++---
src/models/sofia_modelle/call_cnn.py | 7 +-
2 files changed, 4648 insertions(+), 789 deletions(-)
diff --git a/src/models/desi/desi_evaluate_groups.ipynb b/src/models/desi/desi_evaluate_groups.ipynb
index c0b16ed..bf1a83d 100644
--- a/src/models/desi/desi_evaluate_groups.ipynb
+++ b/src/models/desi/desi_evaluate_groups.ipynb
@@ -12,7 +12,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 2,
"metadata": {},
"outputs": [
{
@@ -51,102 +51,242 @@
" <th>Event-based-F1</th>\n",
" <th>R-based-F1</th>\n",
" <th>Affiliation-F</th>\n",
+ " <th>Recall</th>\n",
+ " <th>Precision</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
- " <td>{'periodicity': 3, 'power': 4}</td>\n",
+ " <td>{'window_size': 150, 'lr': 0.0004}</td>\n",
" <td>001_NAB_id_1_Facility_tr_1007_1st_2014.csv</td>\n",
- " <td>1.578039</td>\n",
+ " <td>3.095788</td>\n",
" <td>NAB</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
- " <td>0.423304</td>\n",
- " <td>0.680252</td>\n",
- " <td>0.424395</td>\n",
- " <td>0.684142</td>\n",
- " <td>0.478923</td>\n",
+ " <td>0.111481</td>\n",
+ " <td>0.500314</td>\n",
+ " <td>0.100945</td>\n",
+ " <td>0.505591</td>\n",
+ " <td>0.159277</td>\n",
+ " <td>0.994203</td>\n",
+ " <td>0.818182</td>\n",
+ " <td>0.794531</td>\n",
+ " <td>0.884825</td>\n",
+ " <td>0.023324</td>\n",
" <td>1.000000</td>\n",
- " <td>1.000000</td>\n",
- " <td>0.537912</td>\n",
- " <td>0.966155</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
- " <td>{'periodicity': 1, 'power': 4}</td>\n",
+ " <td>{'window_size': 150, 'lr': 0.0004}</td>\n",
" <td>002_NAB_id_2_WebService_tr_1500_1st_4106.csv</td>\n",
- " <td>3.787581</td>\n",
+ " <td>4.893926</td>\n",
" <td>NAB</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
- " <td>0.623451</td>\n",
- " <td>0.825962</td>\n",
- " <td>0.625950</td>\n",
- " <td>0.829796</td>\n",
- " <td>0.659626</td>\n",
+ " <td>0.127749</td>\n",
+ " <td>0.416057</td>\n",
+ " <td>0.128109</td>\n",
+ " <td>0.441723</td>\n",
+ " <td>0.192068</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
- " <td>0.572999</td>\n",
- " <td>0.994413</td>\n",
+ " <td>0.336815</td>\n",
+ " <td>0.988203</td>\n",
+ " <td>0.031397</td>\n",
+ " <td>0.833333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
- " <td>{'periodicity': 3, 'power': 1}</td>\n",
+ " <td>{'window_size': 150, 'lr': 0.0004}</td>\n",
+ " <td>003_NAB_id_3_WebService_tr_1362_1st_1462.csv</td>\n",
+ " <td>3.208956</td>\n",
+ " <td>NAB</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>0.121635</td>\n",
+ " <td>0.490010</td>\n",
+ " <td>0.117158</td>\n",
+ " <td>0.496531</td>\n",
+ " <td>0.175308</td>\n",
+ " <td>0.999672</td>\n",
+ " <td>0.933333</td>\n",
+ " <td>0.327012</td>\n",
+ " <td>0.945860</td>\n",
+ " <td>0.009192</td>\n",
+ " <td>0.700000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>{'window_size': 150, 'lr': 0.0004}</td>\n",
+ " <td>004_NAB_id_4_Facility_tr_1007_1st_1437.csv</td>\n",
+ " <td>3.034332</td>\n",
+ " <td>NAB</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>0.182674</td>\n",
+ " <td>0.665474</td>\n",
+ " <td>0.180166</td>\n",
+ " <td>0.681682</td>\n",
+ " <td>0.274909</td>\n",
+ " <td>1.000000</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",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>{'window_size': 150, 'lr': 0.0004}</td>\n",
" <td>005_NAB_id_5_Traffic_tr_594_1st_1645.csv</td>\n",
- " <td>1.826716</td>\n",
+ " <td>0.940658</td>\n",
" <td>NAB</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
- " <td>0.286365</td>\n",
- " <td>0.755784</td>\n",
- " <td>0.294678</td>\n",
- " <td>0.759568</td>\n",
- " <td>0.447757</td>\n",
- " <td>0.520788</td>\n",
- " <td>0.471014</td>\n",
- " <td>0.452990</td>\n",
- " <td>0.889674</td>\n",
+ " <td>0.102740</td>\n",
+ " <td>0.440686</td>\n",
+ " <td>0.105448</td>\n",
+ " <td>0.456966</td>\n",
+ " <td>0.183499</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>0.337989</td>\n",
+ " <td>0.974568</td>\n",
+ " <td>0.050420</td>\n",
+ " <td>0.214286</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
- " params \\\n",
- "0 {'periodicity': 3, 'power': 4} \n",
- "1 {'periodicity': 1, 'power': 4} \n",
- "2 {'periodicity': 3, 'power': 1} \n",
+ " params \\\n",
+ "0 {'window_size': 150, 'lr': 0.0004} \n",
+ "1 {'window_size': 150, 'lr': 0.0004} \n",
+ "2 {'window_size': 150, 'lr': 0.0004} \n",
+ "3 {'window_size': 150, 'lr': 0.0004} \n",
+ "4 {'window_size': 150, 'lr': 0.0004} \n",
"\n",
" file_name duration group \\\n",
- "0 001_NAB_id_1_Facility_tr_1007_1st_2014.csv 1.578039 NAB \n",
- "1 002_NAB_id_2_WebService_tr_1500_1st_4106.csv 3.787581 NAB \n",
- "2 005_NAB_id_5_Traffic_tr_594_1st_1645.csv 1.826716 NAB \n",
+ "0 001_NAB_id_1_Facility_tr_1007_1st_2014.csv 3.095788 NAB \n",
+ "1 002_NAB_id_2_WebService_tr_1500_1st_4106.csv 4.893926 NAB \n",
+ "2 003_NAB_id_3_WebService_tr_1362_1st_1462.csv 3.208956 NAB \n",
+ "3 004_NAB_id_4_Facility_tr_1007_1st_1437.csv 3.034332 NAB \n",
+ "4 005_NAB_id_5_Traffic_tr_594_1st_1645.csv 0.940658 NAB \n",
"\n",
" point anomaly seq anomaly AUC-PR AUC-ROC VUS-PR VUS-ROC \\\n",
- "0 False True 0.423304 0.680252 0.424395 0.684142 \n",
- "1 False False 0.623451 0.825962 0.625950 0.829796 \n",
- "2 False False 0.286365 0.755784 0.294678 0.759568 \n",
+ "0 False True 0.111481 0.500314 0.100945 0.505591 \n",
+ "1 False False 0.127749 0.416057 0.128109 0.441723 \n",
+ "2 False True 0.121635 0.490010 0.117158 0.496531 \n",
+ "3 False False 0.182674 0.665474 0.180166 0.681682 \n",
+ "4 False False 0.102740 0.440686 0.105448 0.456966 \n",
"\n",
- " Standard-F1 PA-F1 Event-based-F1 R-based-F1 Affiliation-F \n",
- "0 0.478923 1.000000 1.000000 0.537912 0.966155 \n",
- "1 0.659626 1.000000 1.000000 0.572999 0.994413 \n",
- "2 0.447757 0.520788 0.471014 0.452990 0.889674 "
+ " Standard-F1 PA-F1 Event-based-F1 R-based-F1 Affiliation-F Recall \\\n",
+ "0 0.159277 0.994203 0.818182 0.794531 0.884825 0.023324 \n",
+ "1 0.192068 1.000000 1.000000 0.336815 0.988203 0.031397 \n",
+ "2 0.175308 0.999672 0.933333 0.327012 0.945860 0.009192 \n",
+ "3 0.274909 1.000000 1.000000 0.337469 0.975070 0.007463 \n",
+ "4 0.183499 1.000000 1.000000 0.337989 0.974568 0.050420 \n",
+ "\n",
+ " Precision \n",
+ "0 1.000000 \n",
+ "1 0.833333 \n",
+ "2 0.700000 \n",
+ "3 1.000000 \n",
+ "4 0.214286 "
]
},
- "execution_count": 4,
+ "execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Dataframe erstellen\n",
- "model = 'POLY'\n",
+ "model = 'run_LSTMAD'\n",
"path = '../../../docs/evaluation/'+model+'.csv'\n",
"df = pd.read_csv(path)\n",
"df.head()"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "group\n",
+ "UCR 76\n",
+ "SMD 38\n",
+ "YAHOO 35\n",
+ "Exathlon 32\n",
+ "NAB 28\n",
+ "OPPORTUNITY 28\n",
+ "WSD 25\n",
+ "SVDB 20\n",
+ "SMAP 19\n",
+ "IOPS 17\n",
+ "TODS 15\n",
+ "Stock 10\n",
+ "MGAB 9\n",
+ "MSL 9\n",
+ "NEK 9\n",
+ "LTDB 9\n",
+ "MITDB 8\n",
+ "TAO 3\n",
+ "SED 3\n",
+ "Daphnet 1\n",
+ "SWaT 1\n",
+ "Power 1\n",
+ "CATSv2 1\n",
+ "Name: count, dtype: int64"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df['group'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "colors = {\n",
+ " 'UCR': 'deepskyblue',\n",
+ " 'SMD': 'darksalmon',\n",
+ " 'YAHOO': 'darkseagreen',\n",
+ " 'Exathlon': 'darkolivegreen',\n",
+ " 'NAB': 'darkgoldenrod',\n",
+ " 'OPPORTUNITY': 'darkgray',\n",
+ " 'WSD': 'gold',\n",
+ " 'SVDB': 'darkviolet',\n",
+ " 'SMAP': 'lightskyblue',\n",
+ " 'IOPS': 'blueviolet',\n",
+ " 'TODS': 'steelblue',\n",
+ " 'Stock': 'darkorange',\n",
+ " 'MGAB': 'rosybrown',\n",
+ " 'MSL': 'orange',\n",
+ " 'NEK': 'mediumpurple',\n",
+ " 'LTDB': 'firebrick',\n",
+ " 'MITDB': 'lightsalmon',\n",
+ " 'TAO': 'darkmagenta',\n",
+ " 'SED': 'deeppink',\n",
+ " 'Daphnet': 'coral',\n",
+ " 'SWaT': 'yellowgreen',\n",
+ " 'Power': 'dodgerblue',\n",
+ " 'CATSv2': 'slateblue'\n",
+ " }"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -156,9 +296,19 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 48,
"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": {
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@@ -168,10 +318,40 @@
"data": [
{
"alignmentgroup": "True",
- "hovertemplate": "group=%{x}<br>AUC-PR=%{y}<extra></extra>",
+ "hovertemplate": "<b>%{hovertext}</b><br><br>group=%{x}<br>AUC-PR=%{y}<extra></extra>",
+ "hovertext": [
+ "NAB",
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@@ -180,6 +360,31 @@
"showlegend": true,
"type": "box",
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+ "title": {
+ "text": "<b> Vergleich der Metriken zwischen den Gruppen für run_LSTMAD </b>",
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+ "source": [
+ "#Boxplot\n",
+ "metric = 'AUC-PR'\n",
+ "fig = px.box(df, x = 'group',y=metric, color='group',color_discrete_map= colors,hover_name=\"group\", hover_data=[\"group\"])\n",
+ "fig.update_layout( \n",
+ " template = 'ggplot2',\n",
+ " title={\n",
+ " 'text': '<b> Vergleich der Metriken zwischen den Gruppen für '+model+' </b>',\n",
+ " 'y':0.95, \n",
+ " 'xanchor':'center',\n",
+ " 'yanchor':'top'},\n",
+ " height=600,width=900\n",
+ "\n",
+ " )\n",
+ "fig.update_xaxes(title_text= 'Gruppen')\n",
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+ " opacity=0.8,\n",
+ " line=dict(width=2,\n",
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+ " ),\n",
+ " selector=dict(mode='markers'))\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Vergleich der Punk- und Sequenzanomalien pro Gruppe"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {},
+ "outputs": [
+ {
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+ "output_type": "stream",
+ "text": [
+ "(397, 17)\n"
+ ]
+ },
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+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>params</th>\n",
+ " <th>file_name</th>\n",
+ " <th>duration</th>\n",
+ " <th>group</th>\n",
+ " <th>point anomaly</th>\n",
+ " <th>seq anomaly</th>\n",
+ " <th>AUC-PR</th>\n",
+ " <th>AUC-ROC</th>\n",
+ " <th>VUS-PR</th>\n",
+ " <th>VUS-ROC</th>\n",
+ " <th>Standard-F1</th>\n",
+ " <th>PA-F1</th>\n",
+ " <th>Event-based-F1</th>\n",
+ " <th>R-based-F1</th>\n",
+ " <th>Affiliation-F</th>\n",
+ " <th>Recall</th>\n",
+ " <th>Precision</th>\n",
+ " <th>anomaly</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>{'window_size': 150, 'lr': 0.0004}</td>\n",
+ " <td>001_NAB_id_1_Facility_tr_1007_1st_2014.csv</td>\n",
+ " <td>3.095788</td>\n",
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+ " <td>Sequenzeanomalie</td>\n",
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+ " <th>2</th>\n",
+ " <td>{'window_size': 150, 'lr': 0.0004}</td>\n",
+ " <td>003_NAB_id_3_WebService_tr_1362_1st_1462.csv</td>\n",
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+ " <td>Sequenzeanomalie</td>\n",
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+ " <td>{'window_size': 150, 'lr': 0.0004}</td>\n",
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+ " <th>9</th>\n",
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+ "text/plain": [
+ " params \\\n",
+ "0 {'window_size': 150, 'lr': 0.0004} \n",
+ "2 {'window_size': 150, 'lr': 0.0004} \n",
+ "5 {'window_size': 150, 'lr': 0.0004} \n",
+ "6 {'window_size': 100, 'lr': 0.0008} \n",
+ "9 {'window_size': 100, 'lr': 0.0008} \n",
+ "\n",
+ " file_name duration group point anomaly \\\n",
+ "0 001_NAB_id_1_Facility_tr_1007_1st_2014.csv 3.095788 NAB False \n",
+ "2 003_NAB_id_3_WebService_tr_1362_1st_1462.csv 3.208956 NAB False \n",
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+ "9 010_NAB_id_10_WebService_tr_500_1st_271.csv 1.678742 NAB False \n",
+ "\n",
+ " seq anomaly AUC-PR AUC-ROC VUS-PR VUS-ROC Standard-F1 PA-F1 \\\n",
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+ "9 True 0.226605 0.471617 0.327570 0.655625 0.259647 1.000000 \n",
+ "\n",
+ " Event-based-F1 R-based-F1 Affiliation-F Recall Precision \\\n",
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+ "\n",
+ " anomaly \n",
+ "0 Sequenzeanomalie \n",
+ "2 Sequenzeanomalie \n",
+ "5 Sequenzeanomalie \n",
+ "6 Sequenzeanomalie \n",
+ "9 Sequenzeanomalie "
+ ]
+ },
+ "execution_count": 49,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Redesign Datafram and drop rows which have nether point nor sequence anomalies\n",
+ "df_anomaly = df.where(df['point anomaly'] | df['seq anomaly']).dropna(axis=0,how='all')\n",
+ "print(df.shape)\n",
+ "df_anomaly['anomaly'] = df_anomaly['point anomaly'].apply(lambda x: 'Punktanomalie' if x else 'Sequenzeanomalie')\n",
+ "df_anomaly.head()"
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+ "\n",
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- " params file_name \n",
- "0 {'periodicity': 3, 'power': 4} 001_NAB_id_1_Facility_tr_1007_1st_2014.csv \\\n",
- "\n",
- " duration group point anomaly seq anomaly AUC-PR AUC-ROC VUS-PR \n",
- "0 1.578039 NAB False True 0.423304 0.680252 0.424395 \\\n",
- "\n",
- " VUS-ROC Standard-F1 PA-F1 Event-based-F1 R-based-F1 Affiliation-F \n",
- "0 0.684142 0.478923 1.0 1.0 0.537912 0.966155 \\\n",
- "\n",
- " anomaly \n",
- "0 Sequenzeanomalie "
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Redesign Datafram and drop rows which have nether point nor sequence anomalies\n",
- "df_anomaly = df.where(df['point anomaly'] | df['seq anomaly']).dropna(axis=0,how='all')\n",
- "print(df.shape)\n",
- "df_anomaly['anomaly'] = df_anomaly['point anomaly'].apply(lambda x: 'Punktanomalie' if x else 'Sequenzeanomalie')\n",
- "df_anomaly.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.plotly.v1+json": {
- "config": {
- "plotlyServerURL": "https://plot.ly"
- },
- "data": [
+ "hovertemplate": "<b>%{hovertext}</b><br><br>group=%{customdata[0]}<br>anomaly=%{x}<br>AUC-PR=%{y}<extra></extra>",
+ "hovertext": [
+ "Exathlon",
+ "Exathlon",
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+ ],
+ "legendgroup": "Exathlon",
+ "marker": {
+ "color": "darkolivegreen"
+ },
+ "name": "Exathlon",
+ "notched": false,
+ "offsetgroup": "Exathlon",
+ "orientation": "v",
+ "showlegend": true,
+ "type": "box",
+ "x": [
+ "Sequenzeanomalie",
+ "Sequenzeanomalie",
+ "Sequenzeanomalie",
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+ "Sequenzeanomalie",
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+ ],
+ "x0": " ",
+ "xaxis": "x",
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+ "yaxis": "y"
+ },
{
"alignmentgroup": "True",
- "hovertemplate": "group=NAB<br>anomaly=%{x}<br>AUC-PR=%{y}<extra></extra>",
- "legendgroup": "NAB",
+ "customdata": [
+ [
+ "OPPORTUNITY"
+ ],
+ [
+ "OPPORTUNITY"
+ ],
+ [
+ "OPPORTUNITY"
+ ],
+ [
+ "OPPORTUNITY"
+ ],
+ [
+ "OPPORTUNITY"
+ ],
+ [
+ "OPPORTUNITY"
+ ],
+ [
+ "OPPORTUNITY"
+ ],
+ [
+ "OPPORTUNITY"
+ ],
+ [
+ "OPPORTUNITY"
+ ],
+ [
+ "OPPORTUNITY"
+ ],
+ [
+ "OPPORTUNITY"
+ ],
+ [
+ "OPPORTUNITY"
+ ],
+ [
+ "OPPORTUNITY"
+ ]
+ ],
+ "hovertemplate": "<b>%{hovertext}</b><br><br>group=%{customdata[0]}<br>anomaly=%{x}<br>AUC-PR=%{y}<extra></extra>",
+ "hovertext": [
+ "OPPORTUNITY",
+ "OPPORTUNITY",
+ "OPPORTUNITY",
+ "OPPORTUNITY",
+ "OPPORTUNITY",
+ "OPPORTUNITY",
+ "OPPORTUNITY",
+ "OPPORTUNITY",
+ "OPPORTUNITY",
+ "OPPORTUNITY",
+ "OPPORTUNITY",
+ "OPPORTUNITY",
+ "OPPORTUNITY"
+ ],
+ "legendgroup": "OPPORTUNITY",
"marker": {
- "color": "#636efa"
+ "color": "darkgray"
},
- "name": "NAB",
+ "name": "OPPORTUNITY",
"notched": false,
- "offsetgroup": "NAB",
+ "offsetgroup": "OPPORTUNITY",
"orientation": "v",
"showlegend": true,
"type": "box",
"x": [
+ "Sequenzeanomalie",
+ "Sequenzeanomalie",
+ "Sequenzeanomalie",
+ "Sequenzeanomalie",
+ "Sequenzeanomalie",
+ "Sequenzeanomalie",
+ "Sequenzeanomalie",
+ "Sequenzeanomalie",
+ "Sequenzeanomalie",
+ "Sequenzeanomalie",
+ "Sequenzeanomalie",
+ "Sequenzeanomalie",
"Sequenzeanomalie"
],
"x0": " ",
"xaxis": "x",
"y": [
- 0.4233042158969708
+ 0.9718491015673606,
+ 0.4945030819575546,
+ 0.0111357415519349,
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],
"y0": " ",
"yaxis": "y"
@@ -1552,7 +5412,7 @@
}
},
"title": {
- "text": "<b> Vergleich der Ergebnisse der Anomalien zwischen den Gruppen für POLY </b>",
+ "text": "<b> Vergleich der Ergebnisse der Anomalien zwischen den Gruppen für run_LSTMAD </b>",
"xanchor": "center",
"y": 0.95,
"yanchor": "top"
@@ -1588,7 +5448,7 @@
"source": [
"#Boxplot for anomalies\n",
"metric = 'AUC-PR'\n",
- "fig = px.box(df_anomaly, x = 'anomaly',y=metric, color='group')\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",
" title={\n",
diff --git a/src/models/sofia_modelle/call_cnn.py b/src/models/sofia_modelle/call_cnn.py
index 9953690..26e5eee 100644
--- a/src/models/sofia_modelle/call_cnn.py
+++ b/src/models/sofia_modelle/call_cnn.py
@@ -2,17 +2,16 @@ from CNN import CNN
import sys
import pathlib
sys.path.append(str(pathlib.Path.absolute)+ '../../')
-from src.utils.slidingWindows import find_length_rank
from src.run_model_wrapper import main
#optimal hyperparameters from autors: 'POLY': {'periodicity': 1, 'power': 4}
params = {
'window_size': [50, 100, 150],
- 'num_channel': [[32, 32, 40], [16, 32, 64]]
+ 'lr': [5e-5, 1e-4, 5e-4, 8e-4, 1e-3]
}
-def run_CNN(data_train, data_test, window_size=100, num_channel=[32, 32, 40], lr=0.0008, n_jobs=1):
- clf = CNN(window_size=window_size, num_channel=num_channel, feats=data_test.shape[1], lr=lr, batch_size=128)
+def run_CNN(data_train, data_test, window_size=100, num_channel=[32, 32, 40], lr=0.0008,batch_size=128, n_jobs=1):
+ clf = CNN(window_size=window_size, num_channel=num_channel, feats=data_test.shape[1], lr=lr, batch_size=batch_size)
clf.fit(data_train)
score = clf.decision_function(data_test)
return score.ravel()
--
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