diff --git a/docs/Dataset info.txt b/docs/data/Dataset info.txt
similarity index 100%
rename from docs/Dataset info.txt
rename to docs/data/Dataset info.txt
diff --git a/docs/Datensatz.md b/docs/data/Datensatz.md
similarity index 100%
rename from docs/Datensatz.md
rename to docs/data/Datensatz.md
diff --git a/docs/evaluation/run_Sub_PCA3.csv b/docs/evaluation/run_Sub_PCA3.csv
new file mode 100644
index 0000000000000000000000000000000000000000..f0e714f6d4bbf178292724b802729a75f6aa02ac
--- /dev/null
+++ b/docs/evaluation/run_Sub_PCA3.csv
@@ -0,0 +1,51 @@
+params,file_name,duration,group,point anomaly,seq anomaly,AUC-PR,AUC-ROC,VUS-PR,VUS-ROC,Standard-F1,PA-F1,Event-based-F1,R-based-F1,Affiliation-F,Recall,Precision
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+"{'periodicity': 1, 'n_components': None}",242_SVDB_id_6_Medical_tr_10726_1st_10826.csv,32.02355456352234,SVDB,False,True,0.7036081536355089,0.9547193654981933,0.6769273905227109,0.9847054498065262,0.6843482709045831,0.8658578856152513,0.821919045898084,0.6947880912499647,0.9418662860841549,0.6754201680672269,0.6326664480157429
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+"{'periodicity': 1, 'n_components': None}",235_SED_id_2_Medical_tr_2499_1st_3840.csv,5.831834316253662,SED,False,True,0.019606987156960354,0.012814450078003123,0.028040225131610357,0.0799516090808896,0.07396634923478186,0.07491221225126804,0.07378865234563137,0.3510919980167112,0.669779515043625,0.0,0.0
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+"{'periodicity': 3, 'n_components': None}",212_SMD_id_35_Facility_tr_5925_1st_17580.csv,2.751281499862671,SMD,False,False,0.9352630348115477,0.9988874120047638,0.9082102925573098,0.9991617747043863,0.9118491056343243,1.0,0.9999999999999996,0.915547463696919,0.9998308888439115,0.9316770186335404,0.8875739644970414
+"{'periodicity': 3, 'n_components': None}",209_SMD_id_32_Facility_tr_5925_1st_17580.csv,2.5272674560546875,SMD,False,False,0.8133682893795908,0.9616493529998718,0.7817963120467334,0.9650964454635006,0.761533745887189,1.0,0.9999999999999996,0.6168384879725086,0.9997499494787856,0.8633540372670807,0.6043478260869565
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+"{'periodicity': 1, 'n_components': None}",198_SMD_id_21_Facility_tr_5925_1st_17580.csv,2.151216745376587,SMD,False,False,0.8348379883559753,0.9981581748174665,0.8116696434190227,0.9986329552789222,0.8424387361882429,1.0,0.9999999999999996,0.8498402555910544,0.9993674155579475,0.7453416149068323,0.8695652173913043
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diff --git a/docs/evaluation/run_Sub_PCA6.csv b/docs/evaluation/run_Sub_PCA6.csv
new file mode 100644
index 0000000000000000000000000000000000000000..3727f3573aa8bc745e8f70bb6c52af227e66df7d
--- /dev/null
+++ b/docs/evaluation/run_Sub_PCA6.csv
@@ -0,0 +1,51 @@
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+"{'periodicity': 1, 'n_components': 0.25}",545_SMAP_id_15_Sensor_tr_1173_1st_2750.csv,2.3520584106445312,SMAP,False,False,0.35907013709938385,0.9914844007400461,0.7387481553967095,0.9971610914948849,0.6891846722355942,0.7555555555555555,0.6853146853146848,0.6845637583892618,0.9950712506535755,0.0,0.0
+"{'periodicity': 2, 'n_components': None}",532_SMAP_id_2_Sensor_tr_2075_1st_5550.csv,3.4671173095703125,SMAP,False,False,0.21492635811559677,0.9294130968134239,0.22548894212153012,0.9305320216408012,0.44285338575636435,0.6971201588877854,0.45641838351822467,0.4520283322601417,0.9313520919999877,0.0,0.0
diff --git a/src/models/ahmad/PCA.ipynb b/src/models/ahmad/PCA.ipynb
index cf99c7d2a349e25954285fffbd793e867d5cde68..16fcf03650f3bdb294f9f3c2d019b9edfb801376 100644
--- a/src/models/ahmad/PCA.ipynb
+++ b/src/models/ahmad/PCA.ipynb
@@ -2,38 +2,9 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 12,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CUDA available: False\n",
- "cuDNN version: 90100\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/wattar/DataMining_Projekt/data-mining/src/affiliation/_integral_interval.py:126: SyntaxWarning: invalid escape sequence '\\i'\n",
- " \"\"\"\n",
- "/home/wattar/DataMining_Projekt/data-mining/src/affiliation/_integral_interval.py:145: SyntaxWarning: invalid escape sequence '\\i'\n",
- " \"\"\"\n",
- "/home/wattar/DataMining_Projekt/data-mining/src/affiliation/_integral_interval.py:178: SyntaxWarning: invalid escape sequence '\\i'\n",
- " \"\"\"\n",
- "/home/wattar/DataMining_Projekt/data-mining/src/affiliation/_integral_interval.py:214: SyntaxWarning: invalid escape sequence '\\i'\n",
- " \"\"\"\n",
- "/home/wattar/DataMining_Projekt/data-mining/src/affiliation/_integral_interval.py:245: SyntaxWarning: invalid escape sequence '\\i'\n",
- " \"\"\"\n",
- "/home/wattar/DataMining_Projekt/data-mining/src/affiliation/_integral_interval.py:307: SyntaxWarning: invalid escape sequence '\\i'\n",
- " \"\"\"\n",
- "/home/wattar/DataMining_Projekt/data-mining/src/affiliation/_integral_interval.py:423: SyntaxWarning: invalid escape sequence '\\i'\n",
- " \"\"\"\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from PCA import PCA\n",
"import sys\n",
@@ -45,7 +16,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
@@ -58,24 +29,11 @@
},
{
"cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "def run_PCA(data, slidingWindow=100, n_components=None, n_jobs=1):\n",
- " clf = PCA(slidingWindow = slidingWindow, n_components=n_components)\n",
- " clf.fit(data)\n",
- " score = clf.decision_scores_\n",
- " return score.ravel()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
+ "execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
- "def run_Sub_PCA(data, periodicity=1, n_components=None, n_jobs=1):\n",
+ "def run_Sub_PCA(data, periodicity=1, n_components=None, n_jobs=-1):\n",
" slidingWindow = find_length_rank(data, rank=periodicity)\n",
" clf = PCA(slidingWindow = slidingWindow, n_components=n_components)\n",
" clf.fit(data)\n",
@@ -85,374 +43,106 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Start Processing files\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "Start Processing files\n",
"Start Hyperparameter Tuning\n"
]
},
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "File: 542_SMAP_id_12_Sensor_tr_1908_1st_4690.csv, best hyperparameter: {'periodicity': 2, 'n_components': 0.5}\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "File: 807_YAHOO_id_257_WebService_tr_500_1st_1210.csv, best hyperparameter: {'periodicity': 1, 'n_components': 0.75}\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "File: 344_UCR_id_42_Sensor_tr_2851_1st_5365.csv, best hyperparameter: {'periodicity': 2, 'n_components': 0.5}\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "File: 334_UCR_id_32_HumanActivity_tr_1671_1st_2764.csv, best hyperparameter: {'periodicity': 1, 'n_components': 0.5}\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "File: 160_Stock_id_12_Finance_tr_500_1st_14.csv, best hyperparameter: {'periodicity': 1, 'n_components': 0.25}\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "File: 371_UCR_id_69_Medical_tr_1853_1st_6200.csv, best hyperparameter: {'periodicity': 2, 'n_components': 0.25}\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "File: 445_UCR_id_143_Environment_tr_6166_1st_19280.csv, best hyperparameter: {'periodicity': 1, 'n_components': None}\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "File: 405_UCR_id_103_Sensor_tr_2827_1st_5988.csv, best hyperparameter: {'periodicity': 1, 'n_components': None}\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/home/wattar/miniconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
- ]
- },
- {
- "ename": "KeyboardInterrupt",
- "evalue": "",
+ "ename": "AttributeError",
+ "evalue": "module 'numpy' has no attribute 'RankWarning'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrun_Sub_PCA\u001b[49m\u001b[43m,\u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mSub_PCA\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mdata_folders\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m../../../data/\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43munsupervised\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43moutput_dir\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m../../../docs/evaluation/\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
- "File \u001b[0;32m~/DataMining_Projekt/data-mining/src/run_model_wrapper.py:265\u001b[0m, in \u001b[0;36mmain\u001b[0;34m(run_model, hyperparams, model_name, data_folders, model_type, output_dir)\u001b[0m\n\u001b[1;32m 262\u001b[0m sliding_window \u001b[38;5;241m=\u001b[39m time_series[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msliding_window\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 264\u001b[0m \u001b[38;5;66;03m# Hyperparameter-Optimierung\u001b[39;00m\n\u001b[0;32m--> 265\u001b[0m best_params \u001b[38;5;241m=\u001b[39m \u001b[43mhyperparameter_optimization\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrun_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m,\u001b[49m\u001b[43mtrain_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhyperparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43msliding_window\u001b[49m\u001b[43m,\u001b[49m\u001b[43mmodel_type\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 266\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFile: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfilename\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, best hyperparameter: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbest_params\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 267\u001b[0m \u001b[38;5;66;03m#grid_list looks like this: [{ params: {key:value}, file_name:str,train_data:list,data:list, label:list, sliding_window:int}]\u001b[39;00m\n",
- "File \u001b[0;32m~/DataMining_Projekt/data-mining/src/run_model_wrapper.py:78\u001b[0m, in \u001b[0;36mhyperparameter_optimization\u001b[0;34m(run_model, data, label, train_data, hyperparams, slidingWindow, model_type)\u001b[0m\n\u001b[1;32m 63\u001b[0m scores \u001b[38;5;241m=\u001b[39m run_model(train_data, data, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mparams)\n\u001b[1;32m 65\u001b[0m \u001b[38;5;66;03m#function from autors creates from scores the evaluation: returns\u001b[39;00m\n\u001b[1;32m 66\u001b[0m \u001b[38;5;66;03m# '''{\u001b[39;00m\n\u001b[1;32m 67\u001b[0m \u001b[38;5;66;03m# 'AUC-PR':float,\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 75\u001b[0m \u001b[38;5;66;03m# 'Affiliation-F':float\u001b[39;00m\n\u001b[1;32m 76\u001b[0m \u001b[38;5;66;03m# }'''\u001b[39;00m\n\u001b[0;32m---> 78\u001b[0m metrics \u001b[38;5;241m=\u001b[39m \u001b[43mget_metrics\u001b[49m\u001b[43m(\u001b[49m\u001b[43mscores\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mslidingWindow\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mslidingWindow\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 79\u001b[0m auc_pr \u001b[38;5;241m=\u001b[39m metrics\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAUC-PR\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m0\u001b[39m)\u001b[38;5;66;03m# get value from auc-pr\u001b[39;00m\n\u001b[1;32m 81\u001b[0m \u001b[38;5;66;03m#check if score is better as best score\u001b[39;00m\n",
- "File \u001b[0;32m~/DataMining_Projekt/data-mining/src/evaluation.py:833\u001b[0m, in \u001b[0;36mget_metrics\u001b[0;34m(score, labels, slidingWindow, pred, version, thre)\u001b[0m\n\u001b[1;32m 831\u001b[0m PointF1PA \u001b[38;5;241m=\u001b[39m grader\u001b[38;5;241m.\u001b[39mmetric_PointF1PA(labels, score, preds\u001b[38;5;241m=\u001b[39mpred)\n\u001b[1;32m 832\u001b[0m EventF1PA \u001b[38;5;241m=\u001b[39m grader\u001b[38;5;241m.\u001b[39mmetric_EventF1PA(labels, score, preds\u001b[38;5;241m=\u001b[39mpred)\n\u001b[0;32m--> 833\u001b[0m RF1 \u001b[38;5;241m=\u001b[39m \u001b[43mgrader\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmetric_RF1\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscore\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpreds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpred\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 834\u001b[0m Affiliation_F \u001b[38;5;241m=\u001b[39m grader\u001b[38;5;241m.\u001b[39mmetric_Affiliation(labels, score, preds\u001b[38;5;241m=\u001b[39mpred)\n\u001b[1;32m 836\u001b[0m metrics[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAUC-PR\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m AUC_PR\n",
- "File \u001b[0;32m~/DataMining_Projekt/data-mining/src/evaluation.py:273\u001b[0m, in \u001b[0;36mbasic_metricor.metric_RF1\u001b[0;34m(self, label, score, preds)\u001b[0m\n\u001b[1;32m 270\u001b[0m preds \u001b[38;5;241m=\u001b[39m (score \u001b[38;5;241m>\u001b[39m threshold)\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mint\u001b[39m)\n\u001b[1;32m 272\u001b[0m Rrecall, ExistenceReward, OverlapReward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrange_recall_new(label, preds, alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.2\u001b[39m)\n\u001b[0;32m--> 273\u001b[0m Rprecision \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrange_recall_new\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpreds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 274\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m Rprecision \u001b[38;5;241m+\u001b[39m Rrecall\u001b[38;5;241m==\u001b[39m\u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 275\u001b[0m Rf\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m\n",
- "File \u001b[0;32m~/DataMining_Projekt/data-mining/src/evaluation.py:380\u001b[0m, in \u001b[0;36mbasic_metricor.range_recall_new\u001b[0;34m(self, labels, preds, alpha)\u001b[0m\n\u001b[1;32m 378\u001b[0m OverlapReward \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m 379\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m range_label:\n\u001b[0;32m--> 380\u001b[0m OverlapReward \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mp\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mCardinality_factor(i, range_pred)\n\u001b[1;32m 383\u001b[0m score \u001b[38;5;241m=\u001b[39m alpha \u001b[38;5;241m*\u001b[39m ExistenceReward \u001b[38;5;241m+\u001b[39m (\u001b[38;5;241m1\u001b[39m\u001b[38;5;241m-\u001b[39malpha) \u001b[38;5;241m*\u001b[39m OverlapReward\n\u001b[1;32m 384\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m Nr \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
- "File \u001b[0;32m~/DataMining_Projekt/data-mining/src/evaluation.py:62\u001b[0m, in \u001b[0;36mbasic_metricor.w\u001b[0;34m(self, AnomalyRange, p)\u001b[0m\n\u001b[1;32m 60\u001b[0m MaxValue \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m bi\n\u001b[1;32m 61\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m p:\n\u001b[0;32m---> 62\u001b[0m MyValue \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m bi\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m MyValue\u001b[38;5;241m/\u001b[39mMaxValue\n",
- "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+ "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[16], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrun_Sub_PCA\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 2\u001b[0m output_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m../../../docs/evaluation/\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m----> 3\u001b[0m \u001b[43mmain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrun_Sub_PCA\u001b[49m\u001b[43m,\u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata_folders\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m../../../data/\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43munsupervised\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43moutput_dir\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43moutput_path\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m~/DataMining_Projekt/data-mining/src/run_model_wrapper.py:283\u001b[0m, in \u001b[0;36mmain\u001b[0;34m(run_model, hyperparams, model_name, data_folders, model_type, output_dir)\u001b[0m\n\u001b[1;32m 280\u001b[0m sliding_window \u001b[38;5;241m=\u001b[39m time_series[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msliding_window\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 282\u001b[0m \u001b[38;5;66;03m# Hyperparameter-Optimierung\u001b[39;00m\n\u001b[0;32m--> 283\u001b[0m best_params \u001b[38;5;241m=\u001b[39m \u001b[43mhyperparameter_optimization\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrun_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m,\u001b[49m\u001b[43mtrain_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhyperparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43msliding_window\u001b[49m\u001b[43m,\u001b[49m\u001b[43mmodel_type\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 284\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFile: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfilename\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, best hyperparameter: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbest_params\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 285\u001b[0m \u001b[38;5;66;03m#grid_list looks like this: [{ params: {key:value}, file_name:str,train_data:list,data:list, label:list, sliding_window:int}]\u001b[39;00m\n",
+ "File \u001b[0;32m~/DataMining_Projekt/data-mining/src/run_model_wrapper.py:53\u001b[0m, in \u001b[0;36mhyperparameter_optimization\u001b[0;34m(run_model, data, label, train_data, hyperparams, slidingWindow, model_type)\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mhyperparameter_optimization\u001b[39m(run_model, data,label,train_data, hyperparams,slidingWindow, model_type \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124munsupervised\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m 51\u001b[0m \u001b[38;5;66;03m#ignore warnings for hyperparameter tuning\u001b[39;00m\n\u001b[1;32m 52\u001b[0m warnings\u001b[38;5;241m.\u001b[39msimplefilter(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m, category\u001b[38;5;241m=\u001b[39mUndefinedMetricWarning)\n\u001b[0;32m---> 53\u001b[0m warnings\u001b[38;5;241m.\u001b[39msimplefilter(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m, category\u001b[38;5;241m=\u001b[39m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mRankWarning\u001b[49m)\n\u001b[1;32m 54\u001b[0m best_params \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 55\u001b[0m best_score \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mfloat\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m-inf\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+ "File \u001b[0;32m~/miniconda3/lib/python3.12/site-packages/numpy/__init__.py:410\u001b[0m, in \u001b[0;36m__getattr__\u001b[0;34m(attr)\u001b[0m\n\u001b[1;32m 407\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mchar\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mchar\u001b[39;00m\n\u001b[1;32m 408\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m char\u001b[38;5;241m.\u001b[39mchararray\n\u001b[0;32m--> 410\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodule \u001b[39m\u001b[38;5;132;01m{!r}\u001b[39;00m\u001b[38;5;124m has no attribute \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 411\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;18m__name__\u001b[39m, attr))\n",
+ "\u001b[0;31mAttributeError\u001b[0m: module 'numpy' has no attribute 'RankWarning'"
]
}
],
"source": [
- "main(run_Sub_PCA,params,'Sub_PCA',data_folders = '../../../data/', model_type='unsupervised',output_dir = '../../../docs/evaluation/')"
+ "model = 'run_Sub_PCA'\n",
+ "output_path = '../../../docs/evaluation/'\n",
+ "main(run_Sub_PCA,params, model, data_folders = '../../../data/', model_type='unsupervised',output_dir = output_path)\n"
]
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 81,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2"
+ ]
+ },
+ "execution_count": 81,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
+ "import os\n",
+ "data_empty = {\n",
+ " 'params':[],\n",
+ " 'file_name': [],\n",
+ " 'duration': [],\n",
+ " 'group': [],\n",
+ " 'point anomaly': [],\n",
+ " 'seq anomaly': [],\n",
+ " 'AUC-PR': [],\n",
+ " 'AUC-ROC': [],\n",
+ " 'VUS-PR': [],\n",
+ " 'VUS-ROC': [],\n",
+ " 'Standard-F1': [],\n",
+ " 'PA-F1': [],\n",
+ " 'Event-based-F1': [],\n",
+ " 'R-based-F1': [],\n",
+ " 'Affiliation-F': [],\n",
+ " 'Recall': [],\n",
+ " 'Precision': []\n",
+ "}\n",
+ "\n",
+ "df = pd.DataFrame(data_empty)\n",
"\n",
- "path = '../../../docs/evaluation/Sub_PCA.csv'\n",
- "df = pd.read_csv(path)"
+ "path = '../../../docs/evaluation/'\n",
+ "model = 'run_Sub_PCA'\n",
+ "#concant all batch-files to big one\n",
+ "for file in os.listdir(path):\n",
+ " file_path = os.path.join(path,file_path)\n",
+ " #check if current file belongs to selected model and avoid overwriting existing model.csv data\n",
+ " if file.startswith(model) and file.split('.')[0] != model:\n",
+ " df_batch = pd.read_csv(file_path)\n",
+ " #join with dataframe with all data\n",
+ " df = pd.concat(df,df_batch)\n",
+ "\n",
+ "df.shape"
]
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"metadata": {},
"outputs": [
{
@@ -491,90 +181,78 @@
" <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>001_NAB_id_1_Facility_tr_1007_1st_2014.csv</td>\n",
- " <td>1.578039</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>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>{'periodicity': 1, 'n_components': 0.25}</td>\n",
" <td>002_NAB_id_2_WebService_tr_1500_1st_4106.csv</td>\n",
- " <td>3.787581</td>\n",
+ " <td>3.342668</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>1.000000</td>\n",
- " <td>1.000000</td>\n",
- " <td>0.572999</td>\n",
- " <td>0.994413</td>\n",
+ " <td>0.531921</td>\n",
+ " <td>0.725510</td>\n",
+ " <td>0.531449</td>\n",
+ " <td>0.736615</td>\n",
+ " <td>0.604596</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.543667</td>\n",
+ " <td>0.995006</td>\n",
+ " <td>0.205651</td>\n",
+ " <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>2</th>\n",
- " <td>{'periodicity': 3, 'power': 1}</td>\n",
- " <td>005_NAB_id_5_Traffic_tr_594_1st_1645.csv</td>\n",
- " <td>1.826716</td>\n",
+ " <th>1</th>\n",
+ " <td>{'periodicity': 3, 'n_components': 0.25}</td>\n",
+ " <td>001_NAB_id_1_Facility_tr_1007_1st_2014.csv</td>\n",
+ " <td>1.325033</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>True</td>\n",
+ " <td>0.278238</td>\n",
+ " <td>0.568376</td>\n",
+ " <td>0.274697</td>\n",
+ " <td>0.572207</td>\n",
+ " <td>0.280371</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.664639</td>\n",
+ " <td>0.966270</td>\n",
+ " <td>0.102041</td>\n",
+ " <td>1.0</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 {'periodicity': 1, 'n_components': 0.25} \n",
+ "1 {'periodicity': 3, 'n_components': 0.25} \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",
+ "0 002_NAB_id_2_WebService_tr_1500_1st_4106.csv 3.342668 NAB \n",
+ "1 001_NAB_id_1_Facility_tr_1007_1st_2014.csv 1.325033 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 False 0.531921 0.725510 0.531449 0.736615 \n",
+ "1 False True 0.278238 0.568376 0.274697 0.572207 \n",
+ "\n",
+ " Standard-F1 PA-F1 Event-based-F1 R-based-F1 Affiliation-F Recall \\\n",
+ "0 0.604596 1.0 1.0 0.543667 0.995006 0.205651 \n",
+ "1 0.280371 1.0 1.0 0.664639 0.966270 0.102041 \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 "
+ " Precision \n",
+ "0 1.0 \n",
+ "1 1.0 "
]
},
- "execution_count": 8,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
diff --git a/src/run_model_wrapper.py b/src/run_model_wrapper.py
index 5211a9171d937be57892b5614ff81c0b0786c884..2275d78c2ae6e223a369cabe94908f6fb64241b6 100644
--- a/src/run_model_wrapper.py
+++ b/src/run_model_wrapper.py
@@ -50,7 +50,7 @@ Semisupervise_AD_Pool = ['SAND','OCSVM','AutoEncoder', 'CNN', 'LSTMAD', 'USAD',
def hyperparameter_optimization(run_model, data,label,train_data, hyperparams,slidingWindow, model_type = 'unsupervised'):
#ignore warnings for hyperparameter tuning
warnings.simplefilter("ignore", category=UndefinedMetricWarning)
- warnings.simplefilter("ignore", category=np.RankWarning)
+ #warnings.simplefilter("ignore", category=np.RankWarning)
best_params = None
best_score = float("-inf")