diff --git a/Experiments.ipynb b/Experiments.ipynb
index aebe34f88030296b2dcdd58844ea5c3dafd26e33..7087397cb5761564157504bbf26f974d8be049f6 100644
--- a/Experiments.ipynb
+++ b/Experiments.ipynb
@@ -2,12 +2,12 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "id": "9d54cd9a",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-02-13T14:34:05.761955065Z",
-     "start_time": "2024-02-13T14:34:05.757963454Z"
+     "end_time": "2024-02-14T09:17:51.136290823Z",
+     "start_time": "2024-02-14T09:17:51.130011719Z"
     }
    },
    "outputs": [],
@@ -26,12 +26,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 7,
    "id": "61c53cb1",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-02-13T16:19:33.814920230Z",
-     "start_time": "2024-02-13T16:18:58.796355230Z"
+     "end_time": "2024-02-14T09:39:50.059022516Z",
+     "start_time": "2024-02-14T09:39:12.027137257Z"
     }
    },
    "outputs": [
@@ -39,196 +39,51 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/home/celine/travail.svn/recherche/phd_subject/Arthur/gitlab/cd-bpr\n",
-      "Is CUDA supported by this system? False\n",
-      "CUDA version: None\n",
-      "dataTrain: ../../data/assist0910_tkde/train_valid_0.csv\n",
-      "dataTest: ../../data/assist0910_tkde/test_0.csv\n",
-      "dataPath: ../../data/\n",
-      "embPath: ../../results/table_2/\n",
-      "epochs: 75\n",
-      "batch_size: 512\n",
-      "[Epoch 0] loss: 1.362733\n",
-      "[Epoch 5] loss: 0.946293\n",
-      "[Epoch 10] loss: 0.843926\n",
-      "[Epoch 15] loss: 0.752875\n",
-      "[Epoch 20] loss: 0.675348\n",
-      "[Epoch 25] loss: 0.609859\n",
-      "[Epoch 30] loss: 0.555984\n",
-      "[Epoch 35] loss: 0.511323\n",
-      "[Epoch 40] loss: 0.475109\n",
-      "[Epoch 45] loss: 0.445370\n",
-      "[Epoch 50] loss: 0.421545\n",
-      "[Epoch 55] loss: 0.402110\n",
-      "[Epoch 60] loss: 0.386685\n",
-      "[Epoch 65] loss: 0.374130\n",
-      "[Epoch 70] loss: 0.364230\n",
-      "Best iteration 0\n",
-      "Accuracy train 0\n",
-      "doa 0.7493086165296035\n",
-      "Evaluate\n",
-      "RMSE 0.43184939597901045\n",
-      "AUC: 0.7797187622578838\n",
-      "0\n",
-      "Doa on Train dataset: 0.7493086165296035\n",
-      "AUC and RMSE on test dataset: 0.7797187622578838 0.43184939597901045\n",
-      "doa 0.5110034511151385\n",
-      "Accuracy and Doa on test dataset: 0.734148934531055 0.5110034511151385\n",
-      "dataTrain: ../../data/assist0910_tkde/train_valid_1.csv\n",
-      "dataTest: ../../data/assist0910_tkde/test_1.csv\n",
-      "dataPath: ../../data/\n",
-      "embPath: ../../results/table_2/\n",
-      "epochs: 75\n",
-      "batch_size: 512\n",
-      "[Epoch 0] loss: 1.370187\n",
-      "[Epoch 5] loss: 0.940516\n",
-      "[Epoch 10] loss: 0.839136\n",
-      "[Epoch 15] loss: 0.751113\n",
-      "[Epoch 20] loss: 0.674149\n",
-      "[Epoch 25] loss: 0.609572\n",
-      "[Epoch 30] loss: 0.555151\n",
-      "[Epoch 35] loss: 0.510757\n",
-      "[Epoch 40] loss: 0.473884\n",
-      "[Epoch 45] loss: 0.444131\n",
-      "[Epoch 50] loss: 0.419617\n",
-      "[Epoch 55] loss: 0.400048\n",
-      "[Epoch 60] loss: 0.383974\n",
-      "[Epoch 65] loss: 0.371254\n",
-      "[Epoch 70] loss: 0.360791\n",
-      "Best iteration 0\n",
-      "Accuracy train 0\n",
-      "doa 0.749059681595483\n",
-      "Evaluate\n",
-      "RMSE 0.4297057532202306\n",
-      "AUC: 0.7874179495114682\n",
-      "0\n",
-      "Doa on Train dataset: 0.749059681595483\n",
-      "AUC and RMSE on test dataset: 0.7874179495114682 0.4297057532202306\n",
-      "doa 0.5106703541023732\n",
-      "Accuracy and Doa on test dataset: 0.7411206963550168 0.5106703541023732\n",
-      "dataTrain: ../../data/assist0910_tkde/train_valid_2.csv\n",
-      "dataTest: ../../data/assist0910_tkde/test_2.csv\n",
-      "dataPath: ../../data/\n",
-      "embPath: ../../results/table_2/\n",
-      "epochs: 75\n",
-      "batch_size: 512\n",
-      "[Epoch 0] loss: 1.365342\n",
-      "[Epoch 5] loss: 0.951919\n",
-      "[Epoch 10] loss: 0.867441\n",
-      "[Epoch 15] loss: 0.787939\n",
-      "[Epoch 20] loss: 0.716660\n",
-      "[Epoch 25] loss: 0.652943\n",
-      "[Epoch 30] loss: 0.598784\n",
-      "[Epoch 35] loss: 0.551974\n",
-      "[Epoch 40] loss: 0.512592\n",
-      "[Epoch 45] loss: 0.479014\n",
-      "[Epoch 50] loss: 0.451044\n",
-      "[Epoch 55] loss: 0.427375\n",
-      "[Epoch 60] loss: 0.407843\n",
-      "[Epoch 65] loss: 0.391357\n",
-      "[Epoch 70] loss: 0.377866\n",
-      "Best iteration 0\n",
-      "Accuracy train 0\n",
-      "doa 0.7756294591328354\n",
-      "Evaluate\n",
-      "RMSE 0.4299590283249851\n",
-      "AUC: 0.7876359427708508\n",
-      "0\n",
-      "Doa on Train dataset: 0.7756294591328354\n",
-      "AUC and RMSE on test dataset: 0.7876359427708508 0.4299590283249851\n",
-      "doa 0.49555815191093133\n",
-      "Accuracy and Doa on test dataset: 0.7399504966005577 0.49555815191093133\n",
-      "dataTrain: ../../data/assist0910_tkde/train_valid_3.csv\n",
-      "dataTest: ../../data/assist0910_tkde/test_3.csv\n",
-      "dataPath: ../../data/\n",
-      "embPath: ../../results/table_2/\n",
-      "epochs: 75\n",
-      "batch_size: 512\n",
-      "[Epoch 0] loss: 1.361240\n",
-      "[Epoch 5] loss: 0.917660\n",
-      "[Epoch 10] loss: 0.810890\n",
-      "[Epoch 15] loss: 0.716422\n",
-      "[Epoch 20] loss: 0.636654\n",
-      "[Epoch 25] loss: 0.570979\n",
-      "[Epoch 30] loss: 0.518479\n",
-      "[Epoch 35] loss: 0.476139\n",
-      "[Epoch 40] loss: 0.442749\n",
-      "[Epoch 45] loss: 0.416121\n",
-      "[Epoch 50] loss: 0.395355\n",
-      "[Epoch 55] loss: 0.378881\n",
-      "[Epoch 60] loss: 0.366123\n",
-      "[Epoch 65] loss: 0.356016\n",
-      "[Epoch 70] loss: 0.348208\n",
-      "Best iteration 0\n",
-      "Accuracy train 0\n",
-      "doa 0.7598394776244297\n",
-      "Evaluate\n",
-      "RMSE 0.4295921854657559\n",
-      "AUC: 0.787839063041027\n",
-      "0\n",
-      "Doa on Train dataset: 0.7598394776244297\n",
-      "AUC and RMSE on test dataset: 0.787839063041027 0.4295921854657559\n",
-      "doa 0.5066278482467461\n",
-      "Accuracy and Doa on test dataset: 0.7418728695871734 0.5066278482467461\n",
-      "dataTrain: ../../data/assist0910_tkde/train_valid_4.csv\n",
-      "dataTest: ../../data/assist0910_tkde/test_4.csv\n",
-      "dataPath: ../../data/\n",
-      "embPath: ../../results/table_2/\n",
-      "epochs: 75\n",
-      "batch_size: 512\n",
-      "[Epoch 0] loss: 1.362248\n",
-      "[Epoch 5] loss: 0.923156\n",
-      "[Epoch 10] loss: 0.807012\n",
-      "[Epoch 15] loss: 0.707919\n",
-      "[Epoch 20] loss: 0.625367\n",
-      "[Epoch 25] loss: 0.558423\n",
-      "[Epoch 30] loss: 0.505917\n",
-      "[Epoch 35] loss: 0.464502\n",
-      "[Epoch 40] loss: 0.432523\n",
-      "[Epoch 45] loss: 0.407566\n",
-      "[Epoch 50] loss: 0.388625\n",
-      "[Epoch 55] loss: 0.373885\n",
-      "[Epoch 60] loss: 0.362804\n",
-      "[Epoch 65] loss: 0.354112\n",
-      "[Epoch 70] loss: 0.347627\n",
-      "Best iteration 0\n",
-      "Accuracy train 0\n",
-      "doa 0.7570199245665414\n",
-      "Evaluate\n",
-      "RMSE 0.42942382801447393\n",
-      "AUC: 0.7883277553203483\n",
-      "0\n",
-      "Doa on Train dataset: 0.7570199245665414\n",
-      "AUC and RMSE on test dataset: 0.7883277553203483 0.42942382801447393\n",
-      "doa 0.4913074020630229\n",
-      "Accuracy and Doa on test dataset: 0.7404650570983237 0.4913074020630229\n",
-      "[0.734148934531055, 0.7411206963550168, 0.7399504966005577, 0.7418728695871734, 0.7404650570983237]\n",
-      "[0.7797187622578838, 0.7874179495114682, 0.7876359427708508, 0.787839063041027, 0.7883277553203483]\n",
-      "[0.43184939597901045, 0.4297057532202306, 0.4299590283249851, 0.4295921854657559, 0.42942382801447393]\n",
-      "[0.7493086165296035, 0.749059681595483, 0.7756294591328354, 0.7598394776244297, 0.7570199245665414]\n",
-      "[0.5110034511151385, 0.5106703541023732, 0.49555815191093133, 0.5066278482467461, 0.4913074020630229]\n",
-      "acc : 0.7395116108344253 +- 0.002757704724534191\n",
-      "auc : 0.7861878945803157 +- 0.0032485354538092292\n",
-      "rmse : 0.4301060382008912 +- 0.0008888860088608456\n",
-      "doa_train : 0.7581714318897786 +- 0.00970015000339504\n",
-      "doa_test : 0.5030334414876424 +- 0.008101150566934542\n",
-      "reo : 0.33651754691705094\n",
-      "  File \"/home/celine/travail.svn/recherche/phd_subject/Arthur/gitlab/cd-bpr/code/binary_bpr/main.py\", line 393\n",
-      "    \"doa = compute_doa(testFileName)\n",
-      "    ^\n",
-      "SyntaxError: unterminated string literal (detected at line 393)\n",
-      "  File \"/home/celine/travail.svn/recherche/phd_subject/Arthur/gitlab/cd-bpr/code/binary_bpr/main.py\", line 393\n",
-      "    \"doa = compute_doa(testFileName)\n",
-      "    ^\n",
-      "SyntaxError: unterminated string literal (detected at line 393)\n",
-      "  File \"/home/celine/travail.svn/recherche/phd_subject/Arthur/gitlab/cd-bpr/code/binary_bpr/main.py\", line 393\n",
-      "    \"doa = compute_doa(testFileName)\n",
-      "    ^\n",
-      "SyntaxError: unterminated string literal (detected at line 393)\n",
-      "  File \"/home/celine/travail.svn/recherche/phd_subject/Arthur/gitlab/cd-bpr/code/binary_bpr/main.py\", line 393\n",
-      "    \"doa = compute_doa(testFileName)\n",
-      "    ^\n",
-      "SyntaxError: unterminated string literal (detected at line 393)\n"
+      "/home/arthurb/Programmation/cd-bpr\n",
+      "assist0910_tkde\r\n",
+      "Is CUDA supported by this system? False\r\n",
+      "CUDA version: None\r\n",
+      "==========> fold 0\r\n",
+      "dataTrain: ../../data/assist0910_tkde/train_valid_0.csv\r\n",
+      "dataTest: ../../data/assist0910_tkde/test_0.csv\r\n",
+      "embPath: ../../results/table_2/\r\n",
+      "epochs: 1\r\n",
+      "batch_size: 512\r\n",
+      "[Epoch 0] loss: 1.370641\r\n",
+      "Doa: 0.7126622039356039\r\n",
+      "AUC and RMSE: 0.7249253060269787 0.4473763215794524\r\n",
+      "==========> fold 1\r\n",
+      "dataTrain: ../../data/assist0910_tkde/train_valid_1.csv\r\n",
+      "dataTest: ../../data/assist0910_tkde/test_1.csv\r\n",
+      "embPath: ../../results/table_2/\r\n",
+      "epochs: 1\r\n",
+      "batch_size: 512\r\n",
+      "^C\r\n",
+      "Traceback (most recent call last):\r\n",
+      "  File \"/home/arthurb/Programmation/cd-bpr/code/binary_bpr/main.py\", line 312, in <module>\r\n",
+      "    dico_items, test, y_test = parse_dataframe(dataTest, dico_kc, dico_users, dico_items, False)\r\n",
+      "                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n",
+      "  File \"/home/arthurb/Programmation/cd-bpr/code/binary_bpr/main.py\", line 105, in parse_dataframe\r\n",
+      "    for row_index, row in df_group.iterrows():\r\n",
+      "  File \"/home/arthurb/anaconda3/envs/cdbpr-env/lib/python3.11/site-packages/pandas/core/frame.py\", line 1449, in iterrows\r\n",
+      "    for k, v in zip(self.index, self.values):\r\n",
+      "                                ^^^^^^^^^^^\r\n",
+      "  File \"/home/arthurb/anaconda3/envs/cdbpr-env/lib/python3.11/site-packages/pandas/core/frame.py\", line 12281, in values\r\n",
+      "    return self._mgr.as_array()\r\n",
+      "           ^^^^^^^^^^^^^^^^^^^^\r\n",
+      "  File \"/home/arthurb/anaconda3/envs/cdbpr-env/lib/python3.11/site-packages/pandas/core/internals/managers.py\", line 1656, in as_array\r\n",
+      "    arr = self._interleave(dtype=dtype, na_value=na_value)\r\n",
+      "          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n",
+      "  File \"/home/arthurb/anaconda3/envs/cdbpr-env/lib/python3.11/site-packages/pandas/core/internals/managers.py\", line 1682, in _interleave\r\n",
+      "    dtype = interleaved_dtype(  # type: ignore[assignment]\r\n",
+      "            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n",
+      "  File \"/home/arthurb/anaconda3/envs/cdbpr-env/lib/python3.11/site-packages/pandas/core/internals/base.py\", line 363, in interleaved_dtype\r\n",
+      "    return find_common_type(dtypes)\r\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^\r\n",
+      "  File \"/home/arthurb/anaconda3/envs/cdbpr-env/lib/python3.11/site-packages/pandas/core/dtypes/cast.py\", line 1428, in find_common_type\r\n",
+      "    types = list(dict.fromkeys(types).keys())\r\n",
+      "                 ^^^^^^^^^^^^^^^^^^^^\r\n",
+      "KeyboardInterrupt\r\n"
      ]
     }
    ],
@@ -252,12 +107,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "id": "790a43dd",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-02-13T16:09:34.105849299Z",
-     "start_time": "2024-02-13T16:09:33.923825334Z"
+     "end_time": "2024-02-14T09:17:54.627435200Z",
+     "start_time": "2024-02-14T09:17:54.189951143Z"
     }
    },
    "outputs": [
@@ -265,36 +120,43 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/home/celine/travail.svn/recherche/phd_subject/Arthur/gitlab/cd-bpr\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/home/celine/travail.svn/recherche/phd_subject/Arthur/gitlab/cd-bpr/./code/binary_bpr_ablation/compute_doa.py\", line 11, in <module>\n",
-      "    doa = compute_doa(data)\n",
-      "  File \"/home/celine/travail.svn/recherche/phd_subject/Arthur/gitlab/cd-bpr/code/binary_bpr_ablation/utils.py\", line 161, in compute_doa\n",
-      "    F = fromDFtoArray(filename+\"_embed.csv\",False,'f')\n",
-      "  File \"/home/celine/travail.svn/recherche/phd_subject/Arthur/gitlab/cd-bpr/code/binary_bpr_ablation/utils.py\", line 7, in fromDFtoArray\n",
-      "    df = pd.read_csv(name,index_col=None, header=None)\n",
-      "  File \"/home/celine/miniconda3/lib/python3.10/site-packages/pandas/util/_decorators.py\", line 211, in wrapper\n",
-      "    return func(*args, **kwargs)\n",
-      "  File \"/home/celine/miniconda3/lib/python3.10/site-packages/pandas/util/_decorators.py\", line 331, in wrapper\n",
-      "    return func(*args, **kwargs)\n",
-      "  File \"/home/celine/miniconda3/lib/python3.10/site-packages/pandas/io/parsers/readers.py\", line 950, in read_csv\n",
-      "    return _read(filepath_or_buffer, kwds)\n",
-      "  File \"/home/celine/miniconda3/lib/python3.10/site-packages/pandas/io/parsers/readers.py\", line 605, in _read\n",
-      "    parser = TextFileReader(filepath_or_buffer, **kwds)\n",
-      "  File \"/home/celine/miniconda3/lib/python3.10/site-packages/pandas/io/parsers/readers.py\", line 1442, in __init__\n",
-      "    self._engine = self._make_engine(f, self.engine)\n",
-      "  File \"/home/celine/miniconda3/lib/python3.10/site-packages/pandas/io/parsers/readers.py\", line 1735, in _make_engine\n",
-      "    self.handles = get_handle(\n",
-      "  File \"/home/celine/miniconda3/lib/python3.10/site-packages/pandas/io/common.py\", line 856, in get_handle\n",
-      "    handle = open(\n",
-      "FileNotFoundError: [Errno 2] No such file or directory: '../../results/table_2/users/math1/train_embed.csv'\n"
+      "/home/arthurb/Programmation/cd-bpr\n",
+      "Traceback (most recent call last):\r\n",
+      "  File \"/home/arthurb/Programmation/cd-bpr/./code/binary_bpr_ablation/compute_doa.py\", line 11, in <module>\r\n",
+      "    doa = compute_doa(data)\r\n",
+      "          ^^^^^^^^^^^^^^^^^\r\n",
+      "  File \"/home/arthurb/Programmation/cd-bpr/code/binary_bpr_ablation/utils.py\", line 161, in compute_doa\r\n",
+      "    F = fromDFtoArray(filename+\"_embed.csv\",False,'f')\r\n",
+      "        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n",
+      "  File \"/home/arthurb/Programmation/cd-bpr/code/binary_bpr_ablation/utils.py\", line 7, in fromDFtoArray\r\n",
+      "    df = pd.read_csv(name,index_col=None, header=None)\r\n",
+      "         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n",
+      "  File \"/home/arthurb/anaconda3/envs/cdbpr-env/lib/python3.11/site-packages/pandas/io/parsers/readers.py\", line 948, in read_csv\r\n",
+      "    return _read(filepath_or_buffer, kwds)\r\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n",
+      "  File \"/home/arthurb/anaconda3/envs/cdbpr-env/lib/python3.11/site-packages/pandas/io/parsers/readers.py\", line 611, in _read\r\n",
+      "    parser = TextFileReader(filepath_or_buffer, **kwds)\r\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n",
+      "  File \"/home/arthurb/anaconda3/envs/cdbpr-env/lib/python3.11/site-packages/pandas/io/parsers/readers.py\", line 1448, in __init__\r\n",
+      "    self._engine = self._make_engine(f, self.engine)\r\n",
+      "                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n",
+      "  File \"/home/arthurb/anaconda3/envs/cdbpr-env/lib/python3.11/site-packages/pandas/io/parsers/readers.py\", line 1705, in _make_engine\r\n",
+      "    self.handles = get_handle(\r\n",
+      "                   ^^^^^^^^^^^\r\n",
+      "  File \"/home/arthurb/anaconda3/envs/cdbpr-env/lib/python3.11/site-packages/pandas/io/common.py\", line 863, in get_handle\r\n",
+      "    handle = open(\r\n",
+      "             ^^^^^\r\n",
+      "FileNotFoundError: [Errno 2] No such file or directory: '../../results/table_2/users/math_1/train_embed.csv'\r\n"
      ]
     }
    ],
    "source": [
-    "i = 3\n",
-    "\n",
+    "import os\n",
     "embDirPath = \"../../results/table_2/users/\"\n",
+    "\n",
+    "i = 3 # dataset index\n",
+    "\n",
+    "\n",
     "print(os.getcwd())\n",
     "cmd = 'python ./code/binary_bpr_ablation/compute_doa.py --data '+embDirPath+datasets[i]+'/train' \n",
     "!{cmd}\n",
@@ -373,7 +235,7 @@
     "# 0 no ablation, 1 ablation L2, 2 ablation init, 3 both\n",
     "for abla in range(4):\n",
     "    for i in range(5):\n",
-    "        cmd = 'python ./binary_model/main.py --dataTrain '+ path+'data/'+datasets[i]+'/train.csv --dataTest '+path+'data/'+datasets[i]+'/test.csv --ablation '+str(abla)\n",
+    "        cmd = 'python ./binary_model/main.py --dataTrain '+ path+'data/cdbpr_format/'+datasets[i]+'/train.csv --dataTest '+path+'data/'+datasets[i]+'/test.csv --ablation '+str(abla)\n",
     "    os.system(cmd)"
    ]
   },
diff --git a/makefile b/makefile
index f76ca8a35aab603da3c47ea3aefa4a57b479ecdd..39341c59cdf974917a03bc9ec19d01610e80e0c3 100644
--- a/makefile
+++ b/makefile
@@ -2,7 +2,7 @@
 
 build:
 	unzip data.zip
-	mkdir -p results/ results/table_2 results/table_2/users results/table_2/items
+	unzip results.zip
 	
 clean:
 	rm -rf data/
diff --git a/results.zip b/results.zip
new file mode 100644
index 0000000000000000000000000000000000000000..a8129701eff340ff1dcf8b8e47703606a2126212
Binary files /dev/null and b/results.zip differ