diff --git a/src/models/desi/poly.ipynb b/src/models/desi/poly.ipynb
index 0214c6d512a3b082b1980d0b9bd61ebf6c5da72a..8a248cab36aa6951dec3b55849dcdb44817c1116 100644
--- a/src/models/desi/poly.ipynb
+++ b/src/models/desi/poly.ipynb
@@ -33,8 +33,7 @@
     "params = {\n",
     "        'periodicity': [1, 2, 3],\n",
     "        'power': [1, 2, 3, 4,5,7]\n",
-    "}\n",
-    "model = 'POLY'"
+    "}"
    ]
   },
   {
@@ -53,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -62,58 +61,7 @@
      "text": [
       "Start Processing files\n",
       "Start Hyperparameter Tuning\n",
-      "File: 001_NAB_id_1_Facility_tr_1007_1st_2014.csv, best hyperparameter: {'periodicity': 3, 'power': 4}\n",
-      "File: 002_NAB_id_2_WebService_tr_1500_1st_4106.csv, best hyperparameter: {'periodicity': 1, 'power': 5}\n",
-      "File: 003_NAB_id_3_WebService_tr_1362_1st_1462.csv, best hyperparameter: {'periodicity': 3, 'power': 7}\n",
-      "File: 004_NAB_id_4_Facility_tr_1007_1st_1437.csv, best hyperparameter: {'periodicity': 3, 'power': 4}\n",
-      "File: 005_NAB_id_5_Traffic_tr_594_1st_1645.csv, best hyperparameter: {'periodicity': 3, 'power': 5}\n",
-      "File: 006_NAB_id_6_Traffic_tr_2579_1st_5839.csv, best hyperparameter: {'periodicity': 1, 'power': 4}\n",
-      "File: 007_NAB_id_7_Traffic_tr_624_1st_2087.csv, best hyperparameter: {'periodicity': 1, 'power': 3}\n",
-      "File: 008_NAB_id_8_Synthetic_tr_1007_1st_2734.csv, best hyperparameter: {'periodicity': 3, 'power': 2}\n",
-      "File: 009_NAB_id_9_Traffic_tr_500_1st_438.csv, best hyperparameter: {'periodicity': 2, 'power': 1}\n",
-      "File: 010_NAB_id_10_WebService_tr_500_1st_271.csv, best hyperparameter: {'periodicity': 3, 'power': 3}\n",
-      "File: 011_NAB_id_11_Facility_tr_1007_1st_1526.csv, best hyperparameter: {'periodicity': 2, 'power': 5}\n",
-      "File: 012_NAB_id_12_Synthetic_tr_1007_1st_2787.csv, best hyperparameter: {'periodicity': 1, 'power': 5}\n",
-      "File: 013_NAB_id_13_Traffic_tr_623_1st_2084.csv, best hyperparameter: {'periodicity': 2, 'power': 4}\n",
-      "File: 014_NAB_id_14_WebService_tr_500_1st_1045.csv, best hyperparameter: {'periodicity': 3, 'power': 2}\n",
-      "File: 015_NAB_id_15_Synthetic_tr_1007_1st_2787.csv, best hyperparameter: {'periodicity': 1, 'power': 3}\n",
-      "File: 016_NAB_id_16_Environment_tr_1816_1st_3540.csv, best hyperparameter: {'periodicity': 2, 'power': 4}\n",
-      "File: 017_NAB_id_17_Synthetic_tr_1007_1st_1805.csv, best hyperparameter: {'periodicity': 2, 'power': 2}\n",
-      "File: 018_NAB_id_18_Facility_tr_500_1st_669.csv, best hyperparameter: {'periodicity': 1, 'power': 7}\n",
-      "File: 019_NAB_id_19_Facility_tr_1007_1st_1171.csv, best hyperparameter: {'periodicity': 3, 'power': 1}\n",
-      "File: 020_NAB_id_20_Synthetic_tr_1007_1st_2679.csv, best hyperparameter: {'periodicity': 1, 'power': 7}\n",
-      "File: 021_NAB_id_21_WebService_tr_500_1st_565.csv, best hyperparameter: {'periodicity': 1, 'power': 2}\n",
-      "File: 022_NAB_id_22_Facility_tr_1007_1st_2980.csv, best hyperparameter: {'periodicity': 1, 'power': 7}\n",
-      "File: 023_NAB_id_23_Facility_tr_4512_1st_16551.csv, best hyperparameter: {'periodicity': 3, 'power': 3}\n",
-      "File: 024_NAB_id_24_Synthetic_tr_1007_1st_2787.csv, best hyperparameter: {'periodicity': 1, 'power': 5}\n",
-      "File: 025_NAB_id_25_WebService_tr_3958_1st_4614.csv, best hyperparameter: {'periodicity': 3, 'power': 7}\n",
-      "File: 026_NAB_id_26_Traffic_tr_624_1st_2261.csv, best hyperparameter: {'periodicity': 3, 'power': 7}\n",
-      "File: 027_NAB_id_27_Facility_tr_757_1st_2582.csv, best hyperparameter: {'periodicity': 1, 'power': 5}\n",
-      "File: 028_NAB_id_28_Facility_tr_1007_1st_3447.csv, best hyperparameter: {'periodicity': 1, 'power': 1}\n",
-      "File: 032_WSD_id_4_WebService_tr_4559_1st_11822.csv, best hyperparameter: {'periodicity': 1, 'power': 4}\n",
-      "File: 033_WSD_id_5_WebService_tr_4559_1st_12588.csv, best hyperparameter: {'periodicity': 2, 'power': 4}\n",
-      "File: 038_WSD_id_10_WebService_tr_4042_1st_4142.csv, best hyperparameter: {'periodicity': 2, 'power': 1}\n",
-      "File: 039_WSD_id_11_WebService_tr_1746_1st_1846.csv, best hyperparameter: {'periodicity': 2, 'power': 1}\n",
-      "File: 040_WSD_id_12_WebService_tr_4559_1st_9714.csv, best hyperparameter: {'periodicity': 3, 'power': 7}\n",
-      "File: 043_WSD_id_15_WebService_tr_4521_1st_6828.csv, best hyperparameter: {'periodicity': 1, 'power': 7}\n",
-      "File: 045_WSD_id_17_WebService_tr_2566_1st_2666.csv, best hyperparameter: {'periodicity': 1, 'power': 1}\n",
-      "File: 049_WSD_id_21_WebService_tr_4297_1st_7097.csv, best hyperparameter: {'periodicity': 1, 'power': 7}\n",
-      "File: 053_WSD_id_25_WebService_tr_4559_1st_9198.csv, best hyperparameter: {'periodicity': 1, 'power': 5}\n",
-      "File: 054_WSD_id_26_WebService_tr_2409_1st_2509.csv, best hyperparameter: {'periodicity': 1, 'power': 7}\n",
-      "File: 058_WSD_id_30_WebService_tr_1136_1st_1236.csv, best hyperparameter: {'periodicity': 1, 'power': 1}\n",
-      "File: 060_WSD_id_32_WebService_tr_4576_1st_4766.csv, best hyperparameter: {'periodicity': 1, 'power': 4}\n",
-      "File: 068_WSD_id_40_WebService_tr_4549_1st_13322.csv, best hyperparameter: {'periodicity': 1, 'power': 1}\n",
-      "File: 070_WSD_id_42_WebService_tr_2102_1st_2202.csv, best hyperparameter: {'periodicity': 1, 'power': 7}\n",
-      "File: 074_WSD_id_46_WebService_tr_990_1st_1090.csv, best hyperparameter: {'periodicity': 1, 'power': 1}\n",
-      "File: 089_WSD_id_61_WebService_tr_1010_1st_1110.csv, best hyperparameter: {'periodicity': 1, 'power': 7}\n",
-      "File: 094_WSD_id_66_WebService_tr_3309_1st_3914.csv, best hyperparameter: {'periodicity': 1, 'power': 7}\n",
-      "File: 099_WSD_id_71_WebService_tr_4559_1st_14411.csv, best hyperparameter: {'periodicity': 1, 'power': 5}\n",
-      "File: 106_WSD_id_78_WebService_tr_3363_1st_3463.csv, best hyperparameter: {'periodicity': 3, 'power': 1}\n",
-      "File: 113_WSD_id_85_WebService_tr_500_1st_335.csv, best hyperparameter: {'periodicity': 1, 'power': 7}\n",
-      "File: 117_WSD_id_89_WebService_tr_3251_1st_3351.csv, best hyperparameter: {'periodicity': 1, 'power': 1}\n",
-      "File: 125_WSD_id_97_WebService_tr_2217_1st_2317.csv, best hyperparameter: {'periodicity': 1, 'power': 5}\n",
-      "File: 130_WSD_id_102_WebService_tr_2362_1st_2462.csv, best hyperparameter: {'periodicity': 1, 'power': 2}\n",
-      "File: 137_WSD_id_109_WebService_tr_4559_1st_14404.csv, best hyperparameter: {'periodicity': 1, 'power': 7}\n"
+      "File: 001_NAB_id_1_Facility_tr_1007_1st_2014.csv, best hyperparameter: {'periodicity': 3, 'power': 4}\n"
      ]
     },
     {
@@ -128,14 +76,17 @@
       "File \u001b[1;32mc:\\Users\\desiw\\Desktop\\data-mining\\src\\run_model_wrapper.py:86\u001b[0m, in \u001b[0;36mhyperparameter_optimization\u001b[1;34m(run_model, data, label, train_data, hyperparams, slidingWindow, model_type)\u001b[0m\n\u001b[0;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[0;32m     71\u001b[0m \u001b[38;5;66;03m#function from autors creates from scores the evaluation: returns\u001b[39;00m\n\u001b[0;32m     72\u001b[0m \u001b[38;5;66;03m# '''{\u001b[39;00m\n\u001b[0;32m     73\u001b[0m \u001b[38;5;66;03m#     'AUC-PR':float,\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     83\u001b[0m \u001b[38;5;66;03m#     'Recall': float\u001b[39;00m\n\u001b[0;32m     84\u001b[0m \u001b[38;5;66;03m# }'''\u001b[39;00m\n\u001b[1;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[0;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[0;32m     89\u001b[0m \u001b[38;5;66;03m#check if score is better as best score\u001b[39;00m\n",
       "File \u001b[1;32mc:\\Users\\desiw\\Desktop\\data-mining\\src\\evaluation.py:823\u001b[0m, in \u001b[0;36mget_metrics\u001b[1;34m(score, labels, slidingWindow, pred, version, thre)\u001b[0m\n\u001b[0;32m    820\u001b[0m AUC_PR \u001b[38;5;241m=\u001b[39m grader\u001b[38;5;241m.\u001b[39mmetric_PR(labels, score)\n\u001b[0;32m    822\u001b[0m \u001b[38;5;66;03m# R_AUC_ROC, R_AUC_PR, _, _, _ = grader.RangeAUC(labels=labels, score=score, window=slidingWindow, plot_ROC=True)\u001b[39;00m\n\u001b[1;32m--> 823\u001b[0m _, _, _, _, _, _,VUS_ROC, VUS_PR \u001b[38;5;241m=\u001b[39m \u001b[43mgenerate_curve\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[43mslidingWindow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mversion\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mthre\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    826\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m'''\u001b[39;00m\n\u001b[0;32m    827\u001b[0m \u001b[38;5;124;03mThreshold Dependent\u001b[39;00m\n\u001b[0;32m    828\u001b[0m \u001b[38;5;124;03mif pred is None --> use the oracle threshold\u001b[39;00m\n\u001b[0;32m    829\u001b[0m \u001b[38;5;124;03m'''\u001b[39;00m\n\u001b[0;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",
       "File \u001b[1;32mc:\\Users\\desiw\\Desktop\\data-mining\\src\\evaluation.py:20\u001b[0m, in \u001b[0;36mgenerate_curve\u001b[1;34m(label, score, slidingWindow, version, thre)\u001b[0m\n\u001b[0;32m     18\u001b[0m     tpr_3d, fpr_3d, prec_3d, window_3d, avg_auc_3d, avg_ap_3d \u001b[38;5;241m=\u001b[39m basic_metricor()\u001b[38;5;241m.\u001b[39mRangeAUC_volume_opt_mem(labels_original\u001b[38;5;241m=\u001b[39mlabel, score\u001b[38;5;241m=\u001b[39mscore, windowSize\u001b[38;5;241m=\u001b[39mslidingWindow, thre\u001b[38;5;241m=\u001b[39mthre)\n\u001b[0;32m     19\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m---> 20\u001b[0m     tpr_3d, fpr_3d, prec_3d, window_3d, avg_auc_3d, avg_ap_3d \u001b[38;5;241m=\u001b[39m \u001b[43mbasic_metricor\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mRangeAUC_volume_opt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels_original\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscore\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscore\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwindowSize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mslidingWindow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mthre\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mthre\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     23\u001b[0m X \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(tpr_3d)\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\u001b[38;5;241m.\u001b[39mravel()\n\u001b[0;32m     24\u001b[0m X_ap \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(tpr_3d)[:,:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\u001b[38;5;241m.\u001b[39mravel()\n",
-      "File \u001b[1;32mc:\\Users\\desiw\\Desktop\\data-mining\\src\\evaluation.py:642\u001b[0m, in \u001b[0;36mbasic_metricor.RangeAUC_volume_opt\u001b[1;34m(self, labels_original, score, windowSize, thre)\u001b[0m\n\u001b[0;32m    640\u001b[0m N_labels \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m    641\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m seg \u001b[38;5;129;01min\u001b[39;00m l:\n\u001b[1;32m--> 642\u001b[0m     TP \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m[\u001b[49m\u001b[43mseg\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m:\u001b[49m\u001b[43mseg\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpred\u001b[49m\u001b[43m[\u001b[49m\u001b[43mseg\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m:\u001b[49m\u001b[43mseg\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    643\u001b[0m     N_labels \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39msum(labels[seg[\u001b[38;5;241m0\u001b[39m]:seg[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m])\n\u001b[0;32m    645\u001b[0m TP \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m tp[j]\n",
+      "File \u001b[1;32mc:\\Users\\desiw\\Desktop\\data-mining\\src\\evaluation.py:629\u001b[0m, in \u001b[0;36mbasic_metricor.RangeAUC_volume_opt\u001b[1;34m(self, labels_original, score, windowSize, thre)\u001b[0m\n\u001b[0;32m    627\u001b[0m threshold \u001b[38;5;241m=\u001b[39m score_sorted[i]\n\u001b[0;32m    628\u001b[0m pred \u001b[38;5;241m=\u001b[39m score \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m threshold\n\u001b[1;32m--> 629\u001b[0m labels \u001b[38;5;241m=\u001b[39m labels_extended\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[0;32m    630\u001b[0m existence \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m    632\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m seg \u001b[38;5;129;01min\u001b[39;00m L:\n",
       "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
    ],
    "source": [
     "\n",
-    "main(run_POLY,params,'POLY',data_folders = '../../../data/', model_type='unsupervised',output_dir = '../../../docs/evaluation/')"
+    "model = 'POLY'\n",
+    "output_path = '../../../docs/evaluation/'\n",
+    "\n",
+    "main(run_POLY,params,model,data_folders = '../../../data/', model_type='unsupervised',output_dir = output_path)"
    ]
   },
   {
@@ -281,7 +232,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "output_path = '../../../docs/evaluation/'\n",
     "df.to_csv(output_path+model+'.csv')"
    ]
   }