diff --git a/docs/evaluation/run_Sub_PCA4.csv b/docs/evaluation/run_Sub_PCA4.csv
new file mode 100644
index 0000000000000000000000000000000000000000..009cda1eb56da5526b5e8f12ac4b126f21427f02
--- /dev/null
+++ b/docs/evaluation/run_Sub_PCA4.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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diff --git a/docs/evaluation/run_Sub_PCA7.csv b/docs/evaluation/run_Sub_PCA7.csv
new file mode 100644
index 0000000000000000000000000000000000000000..b1694a00533bafa55346ce42971da5a20f975048
--- /dev/null
+++ b/docs/evaluation/run_Sub_PCA7.csv
@@ -0,0 +1,51 @@
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+"{'periodicity': 1, 'n_components': 0.75}",812_Exathlon_id_3_Facility_tr_10766_1st_12590.csv,10.86173391342163,Exathlon,False,True,0.998586415795415,0.9994681919969324,0.999748492585075,0.9999607866707123,0.9924485247840202,1.0,0.9999999999999996,0.993325542150491,0.9998077502874537,0.29354423395254736,1.0
+"{'periodicity': 3, 'n_components': 0.25}",821_Exathlon_id_12_Facility_tr_6985_1st_7085.csv,7.191134452819824,Exathlon,False,True,0.9994691179872991,0.9999371953659897,0.9992119894267968,0.9999427577664712,0.9879144631514131,1.0,0.9999999999999996,0.9893392278838602,0.9998761702968545,0.21046128500823724,1.0
+"{'periodicity': 2, 'n_components': 0.25}",570_YAHOO_id_20_Synthetic_tr_500_1st_658.csv,1.8464219570159912,YAHOO,True,False,0.002912030341485002,0.4057868736767819,0.021126027651583473,0.6241543370260579,0.007352795201859965,0.007246376811594203,0.007246376811594187,0.02702702702702703,0.6664922018892327,0.0,0.0
diff --git a/src/models/ahmad/PCA.ipynb b/src/models/ahmad/PCA.ipynb
index 16fcf03650f3bdb294f9f3c2d019b9edfb801376..0dd6dd81df6a80f371c238f099d68101b9f062c2 100644
--- a/src/models/ahmad/PCA.ipynb
+++ b/src/models/ahmad/PCA.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -29,7 +29,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -43,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -51,21 +51,138 @@
      "output_type": "stream",
      "text": [
       "Start Processing files\n",
-      "Start Hyperparameter Tuning\n"
+      "Start Hyperparameter Tuning\n",
+      "File: 265_IOPS_id_6_WebService_tr_6453_1st_6553.csv, best hyperparameter: {'periodicity': 1, 'n_components': 0.75}\n",
+      "File: 267_IOPS_id_8_WebService_tr_1534_1st_1882.csv, best hyperparameter: {'periodicity': 3, 'n_components': 0.25}\n",
+      "File: 261_IOPS_id_2_WebService_tr_2178_1st_2278.csv, best hyperparameter: {'periodicity': 3, 'n_components': 0.5}\n",
+      "File: 279_NEK_id_3_WebService_tr_500_1st_974.csv, best hyperparameter: {'periodicity': 1, 'n_components': 0.25}\n",
+      "File: 272_IOPS_id_13_WebService_tr_3442_1st_3542.csv, best hyperparameter: {'periodicity': 2, 'n_components': None}\n",
+      "File: 296_TODS_id_10_Synthetic_tr_500_1st_26.csv, best hyperparameter: {'periodicity': 1, 'n_components': None}\n",
+      "File: 281_NEK_id_5_WebService_tr_500_1st_974.csv, best hyperparameter: {'periodicity': 1, 'n_components': 0.75}\n",
+      "File: 277_NEK_id_1_WebService_tr_500_1st_17.csv, best hyperparameter: {'periodicity': 2, 'n_components': 0.25}\n",
+      "File: 264_IOPS_id_5_WebService_tr_11665_1st_11765.csv, best hyperparameter: {'periodicity': 1, 'n_components': 0.25}\n",
+      "File: 248_SVDB_id_12_Medical_tr_18462_1st_18562.csv, best hyperparameter: {'periodicity': 1, 'n_components': 0.25}\n",
+      "File: 269_IOPS_id_10_WebService_tr_1216_1st_1728.csv, best hyperparameter: {'periodicity': 3, 'n_components': 0.75}\n",
+      "File: 284_NEK_id_8_WebService_tr_500_1st_976.csv, best hyperparameter: {'periodicity': 2, 'n_components': 0.25}\n",
+      "File: 276_IOPS_id_17_WebService_tr_19197_1st_19297.csv, best hyperparameter: {'periodicity': 1, 'n_components': 0.25}\n",
+      "File: 280_NEK_id_4_WebService_tr_500_1st_231.csv, best hyperparameter: {'periodicity': 1, 'n_components': 0.25}\n",
+      "File: 270_IOPS_id_11_WebService_tr_5638_1st_5738.csv, best hyperparameter: {'periodicity': 1, 'n_components': None}\n",
+      "File: 274_IOPS_id_15_WebService_tr_4591_1st_4691.csv, best hyperparameter: {'periodicity': 1, 'n_components': None}\n",
+      "File: 252_SVDB_id_16_Medical_tr_12167_1st_12267.csv, best hyperparameter: {'periodicity': 1, 'n_components': 0.25}\n",
+      "File: 257_TAO_id_1_Environment_tr_500_1st_3.csv, best hyperparameter: {'periodicity': 1, 'n_components': 0.75}\n",
+      "File: 260_IOPS_id_1_WebService_tr_5000_1st_11804.csv, best hyperparameter: {'periodicity': 2, 'n_components': None}\n",
+      "File: 259_TAO_id_3_Environment_tr_500_1st_7.csv, best hyperparameter: {'periodicity': 1, 'n_components': 0.75}\n",
+      "File: 282_NEK_id_6_WebService_tr_500_1st_64.csv, best hyperparameter: {'periodicity': 1, 'n_components': 0.25}\n",
+      "File: 291_TODS_id_5_Synthetic_tr_500_1st_11.csv, best hyperparameter: {'periodicity': 2, 'n_components': 0.25}\n",
+      "File: 286_CATSv2_id_1_Sensor_tr_17792_1st_17892.csv, best hyperparameter: {'periodicity': 2, 'n_components': 0.25}\n",
+      "File: 268_IOPS_id_9_WebService_tr_6216_1st_6316.csv, best hyperparameter: {'periodicity': 3, 'n_components': None}\n",
+      "File: 250_SVDB_id_14_Medical_tr_50000_1st_107354.csv, best hyperparameter: {'periodicity': 2, 'n_components': 0.25}\n",
+      "File: 263_IOPS_id_4_WebService_tr_3482_1st_3582.csv, best hyperparameter: {'periodicity': 2, 'n_components': None}\n",
+      "File: 254_SVDB_id_18_Medical_tr_30824_1st_30924.csv, best hyperparameter: {'periodicity': 3, 'n_components': None}\n",
+      "File: 290_TODS_id_4_Synthetic_tr_500_1st_26.csv, best hyperparameter: {'periodicity': 1, 'n_components': None}\n",
+      "File: 255_SVDB_id_19_Medical_tr_50000_1st_54982.csv, best hyperparameter: {'periodicity': 1, 'n_components': None}\n",
+      "File: 285_NEK_id_9_WebService_tr_500_1st_780.csv, best hyperparameter: {'periodicity': 1, 'n_components': 0.25}\n",
+      "File: 288_TODS_id_2_Synthetic_tr_500_1st_65.csv, best hyperparameter: {'periodicity': 2, 'n_components': None}\n",
+      "File: 266_IOPS_id_7_WebService_tr_4010_1st_4110.csv, best hyperparameter: {'periodicity': 1, 'n_components': None}\n",
+      "File: 256_SVDB_id_20_Medical_tr_6264_1st_6364.csv, best hyperparameter: {'periodicity': 1, 'n_components': None}\n",
+      "File: 287_TODS_id_1_Synthetic_tr_500_1st_11.csv, best hyperparameter: {'periodicity': 1, 'n_components': None}\n",
+      "File: 249_SVDB_id_13_Medical_tr_50000_1st_54982.csv, best hyperparameter: {'periodicity': 1, 'n_components': 0.75}\n",
+      "File: 251_SVDB_id_15_Medical_tr_5421_1st_5521.csv, best hyperparameter: {'periodicity': 1, 'n_components': 0.25}\n",
+      "File: 247_SVDB_id_11_Medical_tr_20100_1st_23611.csv, best hyperparameter: {'periodicity': 3, 'n_components': None}\n",
+      "File: 273_IOPS_id_14_WebService_tr_5569_1st_5669.csv, best hyperparameter: {'periodicity': 1, 'n_components': None}\n",
+      "File: 258_TAO_id_2_Environment_tr_500_1st_4.csv, best hyperparameter: {'periodicity': 1, 'n_components': 0.75}\n",
+      "File: 293_TODS_id_7_Synthetic_tr_500_1st_7.csv, best hyperparameter: {'periodicity': 1, 'n_components': None}\n",
+      "File: 283_NEK_id_7_WebService_tr_500_1st_777.csv, best hyperparameter: {'periodicity': 1, 'n_components': 0.25}\n",
+      "File: 278_NEK_id_2_WebService_tr_500_1st_64.csv, best hyperparameter: {'periodicity': 1, 'n_components': 0.25}\n",
+      "File: 275_IOPS_id_16_WebService_tr_1482_1st_1582.csv, best hyperparameter: {'periodicity': 3, 'n_components': 0.25}\n",
+      "File: 253_SVDB_id_17_Medical_tr_17803_1st_17903.csv, best hyperparameter: {'periodicity': 1, 'n_components': 0.75}\n",
+      "File: 292_TODS_id_6_Synthetic_tr_500_1st_11.csv, best hyperparameter: {'periodicity': 1, 'n_components': None}\n",
+      "File: 271_IOPS_id_12_WebService_tr_500_1st_341.csv, best hyperparameter: {'periodicity': 3, 'n_components': 0.25}\n",
+      "File: 294_TODS_id_8_Synthetic_tr_500_1st_200.csv, best hyperparameter: {'periodicity': 1, 'n_components': None}\n",
+      "File: 289_TODS_id_3_Synthetic_tr_500_1st_26.csv, best hyperparameter: {'periodicity': 1, 'n_components': 0.5}\n",
+      "File: 262_IOPS_id_3_WebService_tr_7028_1st_7128.csv, best hyperparameter: {'periodicity': 2, 'n_components': None}\n",
+      "File: 295_TODS_id_9_Synthetic_tr_1250_1st_2046.csv, best hyperparameter: {'periodicity': 3, 'n_components': None}\n",
+      "Start Training and evaluation\n",
+      "Job done. Duration: 2325.398 seconds\n",
+      "Ergebnisse gespeichert in: ../../../docs/evaluation/run_Sub_PCA4.csv\n",
+      "Start Processing files\n",
+      "Start Hyperparameter Tuning\n",
+      "File: 807_YAHOO_id_257_WebService_tr_500_1st_1210.csv, best hyperparameter: {'periodicity': 1, 'n_components': 0.75}\n",
+      "File: 568_YAHOO_id_18_WebService_tr_500_1st_333.csv, best hyperparameter: {'periodicity': 1, 'n_components': 0.75}\n",
+      "File: 797_YAHOO_id_247_Synthetic_tr_500_1st_893.csv, best hyperparameter: {'periodicity': 1, 'n_components': 0.25}\n",
+      "File: 558_YAHOO_id_8_WebService_tr_500_1st_1125.csv, best hyperparameter: {'periodicity': 1, 'n_components': 0.25}\n",
+      "File: 723_YAHOO_id_173_WebService_tr_500_1st_1214.csv, best hyperparameter: {'periodicity': 2, 'n_components': 0.25}\n",
+      "File: 643_YAHOO_id_93_WebService_tr_500_1st_1038.csv, best hyperparameter: {'periodicity': 1, 'n_components': 0.75}\n",
+      "File: 573_YAHOO_id_23_Synthetic_tr_500_1st_623.csv, best hyperparameter: {'periodicity': 1, 'n_components': None}\n",
+      "File: 657_YAHOO_id_107_WebService_tr_500_1st_1260.csv, best hyperparameter: {'periodicity': 3, 'n_components': 0.25}\n",
+      "File: 579_YAHOO_id_29_WebService_tr_500_1st_1342.csv, best hyperparameter: {'periodicity': 1, 'n_components': 0.75}\n",
+      "File: 603_YAHOO_id_53_Synthetic_tr_500_1st_893.csv, best hyperparameter: {'periodicity': 2, 'n_components': 0.25}\n",
+      "File: 815_Exathlon_id_6_Facility_tr_10766_1st_12590.csv, best hyperparameter: {'periodicity': 1, 'n_components': 0.75}\n",
+      "File: 658_YAHOO_id_108_Synthetic_tr_500_1st_893.csv, best hyperparameter: {'periodicity': 1, 'n_components': None}\n",
+      "File: 813_Exathlon_id_4_Facility_tr_10766_1st_12590.csv, best hyperparameter: {'periodicity': 3, 'n_components': 0.25}\n",
+      "File: 820_Exathlon_id_11_Facility_tr_11665_1st_13484.csv, best hyperparameter: {'periodicity': 3, 'n_components': None}\n",
+      "File: 583_YAHOO_id_33_WebService_tr_500_1st_1616.csv, best hyperparameter: {'periodicity': 2, 'n_components': None}\n",
+      "File: 806_YAHOO_id_256_Synthetic_tr_500_1st_893.csv, best hyperparameter: {'periodicity': 2, 'n_components': 0.5}\n",
+      "File: 810_Exathlon_id_1_Facility_tr_10766_1st_12590.csv, best hyperparameter: {'periodicity': 3, 'n_components': None}\n",
+      "File: 550_SWaT_id_1_Sensor_tr_43700_1st_43800.csv, best hyperparameter: {'periodicity': 3, 'n_components': 0.25}\n",
+      "File: 822_Exathlon_id_13_Facility_tr_10766_1st_12590.csv, best hyperparameter: {'periodicity': 3, 'n_components': None}\n",
+      "File: 562_YAHOO_id_12_Synthetic_tr_500_1st_893.csv, best hyperparameter: {'periodicity': 1, 'n_components': None}\n",
+      "File: 724_YAHOO_id_174_WebService_tr_500_1st_1030.csv, best hyperparameter: {'periodicity': 1, 'n_components': 0.75}\n",
+      "File: 552_YAHOO_id_2_Synthetic_tr_500_1st_893.csv, best hyperparameter: {'periodicity': 2, 'n_components': None}\n",
+      "File: 560_YAHOO_id_10_Synthetic_tr_500_1st_893.csv, best hyperparameter: {'periodicity': 1, 'n_components': 0.25}\n",
+      "File: 680_YAHOO_id_130_Synthetic_tr_500_1st_893.csv, best hyperparameter: {'periodicity': 1, 'n_components': 0.25}\n",
+      "File: 817_Exathlon_id_8_Facility_tr_10766_1st_12590.csv, best hyperparameter: {'periodicity': 3, 'n_components': None}\n",
+      "File: 819_Exathlon_id_10_Facility_tr_10766_1st_12590.csv, best hyperparameter: {'periodicity': 1, 'n_components': 0.25}\n",
+      "File: 551_YAHOO_id_1_Synthetic_tr_500_1st_893.csv, best hyperparameter: {'periodicity': 1, 'n_components': 0.25}\n",
+      "File: 741_YAHOO_id_191_WebService_tr_500_1st_1114.csv, best hyperparameter: {'periodicity': 1, 'n_components': None}\n",
+      "File: 734_YAHOO_id_184_WebService_tr_500_1st_768.csv, best hyperparameter: {'periodicity': 3, 'n_components': 0.75}\n",
+      "File: 555_YAHOO_id_5_WebService_tr_500_1st_730.csv, best hyperparameter: {'periodicity': 1, 'n_components': None}\n",
+      "File: 816_Exathlon_id_7_Facility_tr_10766_1st_12590.csv, best hyperparameter: {'periodicity': 2, 'n_components': None}\n",
+      "File: 780_YAHOO_id_230_Synthetic_tr_500_1st_893.csv, best hyperparameter: {'periodicity': 2, 'n_components': 0.5}\n",
+      "File: 788_YAHOO_id_238_WebService_tr_500_1st_973.csv, best hyperparameter: {'periodicity': 1, 'n_components': 0.25}\n",
+      "File: 701_YAHOO_id_151_Synthetic_tr_500_1st_893.csv, best hyperparameter: {'periodicity': 2, 'n_components': 0.25}\n",
+      "File: 645_YAHOO_id_95_WebService_tr_500_1st_1047.csv, best hyperparameter: {'periodicity': 1, 'n_components': None}\n",
+      "File: 693_YAHOO_id_143_WebService_tr_500_1st_1003.csv, best hyperparameter: {'periodicity': 2, 'n_components': None}\n",
+      "File: 818_Exathlon_id_9_Facility_tr_11665_1st_13484.csv, best hyperparameter: {'periodicity': 1, 'n_components': None}\n",
+      "File: 571_YAHOO_id_21_Synthetic_tr_500_1st_623.csv, best hyperparameter: {'periodicity': 1, 'n_components': None}\n",
+      "File: 549_SMAP_id_19_Sensor_tr_1998_1st_2098.csv, best hyperparameter: {'periodicity': 1, 'n_components': 0.5}\n",
+      "File: 811_Exathlon_id_2_Facility_tr_10766_1st_12590.csv, best hyperparameter: {'periodicity': 3, 'n_components': 0.75}\n",
+      "File: 762_YAHOO_id_212_WebService_tr_500_1st_1055.csv, best hyperparameter: {'periodicity': 3, 'n_components': 0.75}\n",
+      "File: 755_YAHOO_id_205_Synthetic_tr_500_1st_893.csv, best hyperparameter: {'periodicity': 1, 'n_components': None}\n",
+      "File: 649_YAHOO_id_99_WebService_tr_500_1st_1386.csv, best hyperparameter: {'periodicity': 2, 'n_components': None}\n",
+      "File: 689_YAHOO_id_139_WebService_tr_500_1st_1073.csv, best hyperparameter: {'periodicity': 1, 'n_components': 0.25}\n",
+      "File: 672_YAHOO_id_122_WebService_tr_500_1st_857.csv, best hyperparameter: {'periodicity': 1, 'n_components': 0.75}\n",
+      "File: 814_Exathlon_id_5_Facility_tr_10766_1st_12590.csv, best hyperparameter: {'periodicity': 3, 'n_components': 0.75}\n",
+      "File: 644_YAHOO_id_94_WebService_tr_500_1st_1078.csv, best hyperparameter: {'periodicity': 1, 'n_components': 0.5}\n",
+      "File: 812_Exathlon_id_3_Facility_tr_10766_1st_12590.csv, best hyperparameter: {'periodicity': 1, 'n_components': 0.75}\n",
+      "File: 821_Exathlon_id_12_Facility_tr_6985_1st_7085.csv, best hyperparameter: {'periodicity': 3, 'n_components': 0.25}\n",
+      "File: 570_YAHOO_id_20_Synthetic_tr_500_1st_658.csv, best hyperparameter: {'periodicity': 2, 'n_components': 0.25}\n",
+      "Start Training and evaluation\n",
+      "Job done. Duration: 716.159 seconds\n",
+      "Ergebnisse gespeichert in: ../../../docs/evaluation/run_Sub_PCA7.csv\n",
+      "Start Processing files\n",
+      "Start Hyperparameter Tuning\n",
+      "File: 829_Exathlon_id_20_Facility_tr_10766_1st_12590.csv, best hyperparameter: {'periodicity': 2, 'n_components': None}\n"
      ]
     },
     {
-     "ename": "AttributeError",
-     "evalue": "module 'numpy' has no attribute 'RankWarning'",
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\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",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[9], 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'"
+      "File \u001b[0;32m~/DataMining_Projekt/data-mining/src/run_model_wrapper.py:86\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     69\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     71\u001b[0m \u001b[38;5;66;03m#function from autors creates from scores the evaluation: returns\u001b[39;00m\n\u001b[1;32m     72\u001b[0m \u001b[38;5;66;03m# '''{\u001b[39;00m\n\u001b[1;32m     73\u001b[0m \u001b[38;5;66;03m#     'AUC-PR':float,\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[38;5;66;03m#     'Recall': float\u001b[39;00m\n\u001b[1;32m     84\u001b[0m \u001b[38;5;66;03m# }'''\u001b[39;00m\n\u001b[0;32m---> 86\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     87\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     89\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:832\u001b[0m, in \u001b[0;36mget_metrics\u001b[0;34m(score, labels, slidingWindow, pred, version, thre)\u001b[0m\n\u001b[1;32m    826\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m'''\u001b[39;00m\n\u001b[1;32m    827\u001b[0m \u001b[38;5;124;03mThreshold Dependent\u001b[39;00m\n\u001b[1;32m    828\u001b[0m \u001b[38;5;124;03mif pred is None --> use the oracle threshold\u001b[39;00m\n\u001b[1;32m    829\u001b[0m \u001b[38;5;124;03m'''\u001b[39;00m\n\u001b[1;32m    831\u001b[0m PointF1 \u001b[38;5;241m=\u001b[39m grader\u001b[38;5;241m.\u001b[39mmetric_PointF1(labels, score, preds\u001b[38;5;241m=\u001b[39mpred)\n\u001b[0;32m--> 832\u001b[0m PointF1PA \u001b[38;5;241m=\u001b[39m \u001b[43mgrader\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmetric_PointF1PA\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    833\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[1;32m    834\u001b[0m RF1 \u001b[38;5;241m=\u001b[39m grader\u001b[38;5;241m.\u001b[39mmetric_RF1(labels, score, preds\u001b[38;5;241m=\u001b[39mpred)\n",
+      "File \u001b[0;32m~/DataMining_Projekt/data-mining/src/evaluation.py:303\u001b[0m, in \u001b[0;36mbasic_metricor.metric_PointF1PA\u001b[0;34m(self, label, score, preds)\u001b[0m\n\u001b[1;32m    300\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    302\u001b[0m     adjust_preds \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_adjust_predicts(score, label, pred\u001b[38;5;241m=\u001b[39mpreds)\n\u001b[0;32m--> 303\u001b[0m     PointF1PA \u001b[38;5;241m=\u001b[39m \u001b[43mmetrics\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mf1_score\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43madjust_preds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    305\u001b[0m     PointF1PA_scores\u001b[38;5;241m.\u001b[39mappend(PointF1PA)\n\u001b[1;32m    307\u001b[0m PointF1PA_Threshold \u001b[38;5;241m=\u001b[39m thresholds[np\u001b[38;5;241m.\u001b[39margmax(PointF1PA_scores)]\n",
+      "File \u001b[0;32m~/miniconda3/lib/python3.12/site-packages/sklearn/utils/_param_validation.py:206\u001b[0m, in \u001b[0;36mvalidate_params.<locals>.decorator.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    203\u001b[0m to_ignore \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mself\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcls\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m    204\u001b[0m params \u001b[38;5;241m=\u001b[39m {k: v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m params\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m to_ignore}\n\u001b[0;32m--> 206\u001b[0m \u001b[43mvalidate_parameter_constraints\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    207\u001b[0m \u001b[43m    \u001b[49m\u001b[43mparameter_constraints\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaller_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;18;43m__qualname__\u001b[39;49m\n\u001b[1;32m    208\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    210\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    211\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[1;32m    212\u001b[0m         skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m    213\u001b[0m             prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[1;32m    214\u001b[0m         )\n\u001b[1;32m    215\u001b[0m     ):\n",
+      "File \u001b[0;32m~/miniconda3/lib/python3.12/site-packages/sklearn/utils/_param_validation.py:78\u001b[0m, in \u001b[0;36mvalidate_parameter_constraints\u001b[0;34m(parameter_constraints, params, caller_name)\u001b[0m\n\u001b[1;32m     75\u001b[0m constraints \u001b[38;5;241m=\u001b[39m [make_constraint(constraint) \u001b[38;5;28;01mfor\u001b[39;00m constraint \u001b[38;5;129;01min\u001b[39;00m constraints]\n\u001b[1;32m     77\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m constraint \u001b[38;5;129;01min\u001b[39;00m constraints:\n\u001b[0;32m---> 78\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mconstraint\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mis_satisfied_by\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparam_val\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m     79\u001b[0m         \u001b[38;5;66;03m# this constraint is satisfied, no need to check further.\u001b[39;00m\n\u001b[1;32m     80\u001b[0m         \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m     81\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     82\u001b[0m     \u001b[38;5;66;03m# No constraint is satisfied, raise with an informative message.\u001b[39;00m\n\u001b[1;32m     83\u001b[0m \n\u001b[1;32m     84\u001b[0m     \u001b[38;5;66;03m# Ignore constraints that we don't want to expose in the error message,\u001b[39;00m\n\u001b[1;32m     85\u001b[0m     \u001b[38;5;66;03m# i.e. options that are for internal purpose or not officially supported.\u001b[39;00m\n",
+      "File \u001b[0;32m~/miniconda3/lib/python3.12/site-packages/sklearn/utils/_param_validation.py:529\u001b[0m, in \u001b[0;36m_ArrayLikes.is_satisfied_by\u001b[0;34m(self, val)\u001b[0m\n\u001b[1;32m    528\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mis_satisfied_by\u001b[39m(\u001b[38;5;28mself\u001b[39m, val):\n\u001b[0;32m--> 529\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_is_arraylike_not_scalar\u001b[49m\u001b[43m(\u001b[49m\u001b[43mval\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniconda3/lib/python3.12/site-packages/sklearn/utils/validation.py:315\u001b[0m, in \u001b[0;36m_is_arraylike_not_scalar\u001b[0;34m(array)\u001b[0m\n\u001b[1;32m    313\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_is_arraylike_not_scalar\u001b[39m(array):\n\u001b[1;32m    314\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Return True if array is array-like and not a scalar\"\"\"\u001b[39;00m\n\u001b[0;32m--> 315\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_is_arraylike\u001b[49m\u001b[43m(\u001b[49m\u001b[43marray\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39misscalar(array)\n",
+      "File \u001b[0;32m~/miniconda3/lib/python3.12/site-packages/sklearn/utils/validation.py:310\u001b[0m, in \u001b[0;36m_is_arraylike\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m    307\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m sp\u001b[38;5;241m.\u001b[39missparse(x):\n\u001b[1;32m    308\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m--> 310\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(x, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__len__\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28;43mhasattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mshape\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(x, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__array__\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
    ],
@@ -126,8 +243,6 @@
     "\n",
     "df = pd.DataFrame(data_empty)\n",
     "\n",
-    "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",