diff --git a/docs/evaluation/POLY1.csv b/docs/evaluation/POLY1.csv
new file mode 100644
index 0000000000000000000000000000000000000000..0f6031c31bfedc6a2b011d4b4703803592d2508b
--- /dev/null
+++ b/docs/evaluation/POLY1.csv
@@ -0,0 +1,52 @@
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diff --git a/src/models/desi/poly.ipynb b/src/models/desi/poly.ipynb
index 8a248cab36aa6951dec3b55849dcdb44817c1116..10eac4611720544c30f338beedd27d295359a500 100644
--- a/src/models/desi/poly.ipynb
+++ b/src/models/desi/poly.ipynb
@@ -52,7 +52,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
@@ -61,23 +61,108 @@
"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: 137_WSD_id_109_WebService_tr_4559_1st_14404.csv, best hyperparameter: {'periodicity': 1, 'power': 7}\n",
+ "File: 139_WSD_id_111_WebService_tr_1701_1st_1801.csv, best hyperparameter: {'periodicity': 2, 'power': 1}\n",
+ "File: 140_MSL_id_1_Sensor_tr_530_1st_630.csv, best hyperparameter: {'periodicity': 1, 'power': 1}\n",
+ "File: 141_MSL_id_2_Sensor_tr_500_1st_550.csv, best hyperparameter: {'periodicity': 1, 'power': 2}\n",
+ "File: 142_MSL_id_3_Sensor_tr_1525_1st_4575.csv, best hyperparameter: {'periodicity': 1, 'power': 2}\n",
+ "File: 143_MSL_id_4_Sensor_tr_980_1st_3550.csv, best hyperparameter: {'periodicity': 3, 'power': 1}\n",
+ "File: 144_MSL_id_5_Sensor_tr_1525_1st_4590.csv, best hyperparameter: {'periodicity': 1, 'power': 1}\n",
+ "File: 145_MSL_id_6_Sensor_tr_530_1st_630.csv, best hyperparameter: {'periodicity': 1, 'power': 2}\n",
+ "File: 146_MSL_id_7_Sensor_tr_554_1st_1172.csv, best hyperparameter: {'periodicity': 1, 'power': 1}\n",
+ "File: 147_MSL_id_8_Sensor_tr_590_1st_690.csv, best hyperparameter: {'periodicity': 1, 'power': 3}\n",
+ "File: 148_MSL_id_9_Sensor_tr_883_1st_1238.csv, best hyperparameter: {'periodicity': 2, 'power': 1}\n",
+ "File: 149_Stock_id_1_Finance_tr_500_1st_7.csv, best hyperparameter: {'periodicity': 2, 'power': 1}\n",
+ "File: 151_Stock_id_3_Finance_tr_500_1st_62.csv, best hyperparameter: {'periodicity': 2, 'power': 1}\n",
+ "File: 152_Stock_id_4_Finance_tr_500_1st_2.csv, best hyperparameter: {'periodicity': 1, 'power': 1}\n",
+ "File: 155_Stock_id_7_Finance_tr_500_1st_18.csv, best hyperparameter: {'periodicity': 2, 'power': 1}\n",
+ "File: 156_Stock_id_8_Finance_tr_500_1st_24.csv, best hyperparameter: {'periodicity': 1, 'power': 1}\n",
+ "File: 160_Stock_id_12_Finance_tr_500_1st_14.csv, best hyperparameter: {'periodicity': 1, 'power': 1}\n",
+ "File: 161_Stock_id_13_Finance_tr_500_1st_3.csv, best hyperparameter: {'periodicity': 1, 'power': 1}\n",
+ "File: 162_Stock_id_14_Finance_tr_500_1st_4.csv, best hyperparameter: {'periodicity': 2, 'power': 1}\n",
+ "File: 165_Stock_id_17_Finance_tr_500_1st_7.csv, best hyperparameter: {'periodicity': 2, 'power': 1}\n",
+ "File: 166_Stock_id_18_Finance_tr_500_1st_18.csv, best hyperparameter: {'periodicity': 1, 'power': 1}\n",
+ "File: 169_Daphnet_id_1_HumanActivity_tr_9693_1st_20732.csv, best hyperparameter: {'periodicity': 3, 'power': 4}\n",
+ "File: 170_MITDB_id_1_Medical_tr_17675_1st_17775.csv, best hyperparameter: {'periodicity': 1, 'power': 3}\n",
+ "File: 171_MITDB_id_2_Medical_tr_50000_1st_88864.csv, best hyperparameter: {'periodicity': 1, 'power': 7}\n",
+ "File: 172_MITDB_id_3_Medical_tr_27274_1st_27374.csv, best hyperparameter: {'periodicity': 3, 'power': 3}\n",
+ "File: 173_MITDB_id_4_Medical_tr_13620_1st_13720.csv, best hyperparameter: {'periodicity': 1, 'power': 5}\n",
+ "File: 174_MITDB_id_5_Medical_tr_50000_1st_539948.csv, best hyperparameter: {'periodicity': 2, 'power': 1}\n",
+ "Unexpected exception formatting exception. Falling back to standard exception\n"
]
},
{
- "ename": "KeyboardInterrupt",
- "evalue": "",
- "output_type": "error",
- "traceback": [
- "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[1;32mIn[4], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mmain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrun_POLY\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;43mPOLY\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[1;32mc:\\Users\\desiw\\Desktop\\data-mining\\src\\run_model_wrapper.py:283\u001b[0m, in \u001b[0;36mmain\u001b[1;34m(run_model, hyperparams, model_name, data_folders, model_type, output_dir)\u001b[0m\n\u001b[0;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[0;32m 282\u001b[0m \u001b[38;5;66;03m# Hyperparameter-Optimierung\u001b[39;00m\n\u001b[1;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[0;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[0;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[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: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: "
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Traceback (most recent call last):\n",
+ " File \"C:\\Users\\desiw\\AppData\\Roaming\\Python\\Python311\\site-packages\\IPython\\core\\interactiveshell.py\", line 3508, in run_code\n",
+ " exec(code_obj, self.user_global_ns, self.user_ns)\n",
+ " File \"C:\\Users\\desiw\\AppData\\Local\\Temp\\ipykernel_33080\\4202162650.py\", line 4, in <module>\n",
+ " main(run_POLY,params,model,data_folders = '../../../data/', model_type='unsupervised',output_dir = output_path)\n",
+ " File \"c:\\Users\\desiw\\Desktop\\data-mining\\src\\run_model_wrapper.py\", line 283, in main\n",
+ " best_params = hyperparameter_optimization(run_model, data,label,train_data, hyperparams,sliding_window,model_type)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"c:\\Users\\desiw\\Desktop\\data-mining\\src\\run_model_wrapper.py\", line 86, in hyperparameter_optimization\n",
+ " metrics = get_metrics(scores, labels=label, slidingWindow=slidingWindow)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"c:\\Users\\desiw\\Desktop\\data-mining\\src\\evaluation.py\", line 834, in get_metrics\n",
+ " RF1 = grader.metric_RF1(labels, score, preds=pred)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"c:\\Users\\desiw\\Desktop\\data-mining\\src\\evaluation.py\", line 273, in metric_RF1\n",
+ " Rrecall, ExistenceReward, OverlapReward = self.range_recall_new(label, preds, alpha=0.2)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"c:\\Users\\desiw\\Desktop\\data-mining\\src\\evaluation.py\", line 381, in range_recall_new\n",
+ " OverlapReward += self.w(i, p) * self.Cardinality_factor(i, range_pred)\n",
+ " ^^^^^^^^^^^^\n",
+ " File \"c:\\Users\\desiw\\Desktop\\data-mining\\src\\evaluation.py\", line -1, in w\n",
+ "KeyboardInterrupt\n",
+ "\n",
+ "During handling of the above exception, another exception occurred:\n",
+ "\n",
+ "Traceback (most recent call last):\n",
+ " File \"C:\\Users\\desiw\\AppData\\Roaming\\Python\\Python311\\site-packages\\IPython\\core\\interactiveshell.py\", line 2105, in showtraceback\n",
+ " stb = self.InteractiveTB.structured_traceback(\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"C:\\Users\\desiw\\AppData\\Roaming\\Python\\Python311\\site-packages\\IPython\\core\\ultratb.py\", line 1396, in structured_traceback\n",
+ " return FormattedTB.structured_traceback(\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"C:\\Users\\desiw\\AppData\\Roaming\\Python\\Python311\\site-packages\\IPython\\core\\ultratb.py\", line 1287, in structured_traceback\n",
+ " return VerboseTB.structured_traceback(\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"C:\\Users\\desiw\\AppData\\Roaming\\Python\\Python311\\site-packages\\IPython\\core\\ultratb.py\", line 1140, in structured_traceback\n",
+ " formatted_exception = self.format_exception_as_a_whole(etype, evalue, etb, number_of_lines_of_context,\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"C:\\Users\\desiw\\AppData\\Roaming\\Python\\Python311\\site-packages\\IPython\\core\\ultratb.py\", line 1055, in format_exception_as_a_whole\n",
+ " frames.append(self.format_record(record))\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"C:\\Users\\desiw\\AppData\\Roaming\\Python\\Python311\\site-packages\\IPython\\core\\ultratb.py\", line 955, in format_record\n",
+ " frame_info.lines, Colors, self.has_colors, lvals\n",
+ " ^^^^^^^^^^^^^^^^\n",
+ " File \"C:\\Users\\desiw\\AppData\\Roaming\\Python\\Python311\\site-packages\\IPython\\core\\ultratb.py\", line 778, in lines\n",
+ " return self._sd.lines\n",
+ " ^^^^^^^^^^^^^^\n",
+ " File \"C:\\Users\\desiw\\AppData\\Roaming\\Python\\Python311\\site-packages\\stack_data\\utils.py\", line 144, in cached_property_wrapper\n",
+ " value = obj.__dict__[self.func.__name__] = self.func(obj)\n",
+ " ^^^^^^^^^^^^^^\n",
+ " File \"C:\\Users\\desiw\\AppData\\Roaming\\Python\\Python311\\site-packages\\stack_data\\core.py\", line 734, in lines\n",
+ " pieces = self.included_pieces\n",
+ " ^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"C:\\Users\\desiw\\AppData\\Roaming\\Python\\Python311\\site-packages\\stack_data\\utils.py\", line 144, in cached_property_wrapper\n",
+ " value = obj.__dict__[self.func.__name__] = self.func(obj)\n",
+ " ^^^^^^^^^^^^^^\n",
+ " File \"C:\\Users\\desiw\\AppData\\Roaming\\Python\\Python311\\site-packages\\stack_data\\core.py\", line 681, in included_pieces\n",
+ " pos = scope_pieces.index(self.executing_piece)\n",
+ " ^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"C:\\Users\\desiw\\AppData\\Roaming\\Python\\Python311\\site-packages\\stack_data\\utils.py\", line 144, in cached_property_wrapper\n",
+ " value = obj.__dict__[self.func.__name__] = self.func(obj)\n",
+ " ^^^^^^^^^^^^^^\n",
+ " File \"C:\\Users\\desiw\\AppData\\Roaming\\Python\\Python311\\site-packages\\stack_data\\core.py\", line 660, in executing_piece\n",
+ " return only(\n",
+ " ^^^^^\n",
+ " File \"C:\\Users\\desiw\\AppData\\Roaming\\Python\\Python311\\site-packages\\executing\\executing.py\", line 190, in only\n",
+ " raise NotOneValueFound('Expected one value, found 0')\n",
+ "executing.executing.NotOneValueFound: Expected one value, found 0\n"
]
}
],
@@ -120,7 +205,6 @@
"df = pd.DataFrame(data_empty)\n",
"\n",
"path = '../../../docs/evaluation/'\n",
- "model = 'POLY'\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",
diff --git a/src/run_model_wrapper.py b/src/run_model_wrapper.py
index 5211a9171d937be57892b5614ff81c0b0786c884..1b6d7a755bcd28e6b2c31214b32b61644a28aef8 100644
--- a/src/run_model_wrapper.py
+++ b/src/run_model_wrapper.py
@@ -299,4 +299,4 @@ def main(run_model, hyperparams, model_name, data_folders = '../../../data/', mo
output_csv = os.path.join(output_dir,model_name + folder + '.csv')
results_df.to_csv(output_csv, index=False)
print(f"Ergebnisse gespeichert in: {output_csv}")
- time.sleep(10)
+ time.sleep(60)