diff --git a/experiment_log_3.txt b/experiment_log_3.txt
new file mode 100644
index 00000000..0eebadbe
--- /dev/null
+++ b/experiment_log_3.txt
@@ -0,0 +1,92 @@
+
+========================================
+Experiment conducted on: 2024-12-15 14:35:04.892244
+
+Hyperparameters:
+name: GlobalLocalKernel
+trainable: True
+verbose: False
+is_fitted: False
+ignored_attrs: ['train_set', 'val_set', 'test_set']
+num_users: None
+num_items: 1656
+uid_map: None
+iid_map: None
+max_rating: 4.0
+min_rating: 1.0
+global_mean: None
+_Recommender__user_ids: None
+_Recommender__item_ids: None
+n_hid: 10
+n_dim: 2
+n_layers: 2
+lambda_s: 0.006
+lambda_2: 0.001
+gk_size: 3
+dot_scale: 1
+max_epoch_p: 500
+max_epoch_f: 1000
+tol_p: 0.0001
+tol_f: 1e-05
+patience_p: 10
+patience_f: 10
+lr_p: 0.1
+lr_f: 0.01
+device: cuda
+model: CompleteNet(
+  (local_kernel_net): KernelNet(
+    (layers): ModuleList(
+      (0-1): 2 x KernelLayer(
+        (activation): Sigmoid()
+      )
+      (2): KernelLayer(
+        (activation): Identity()
+      )
+    )
+    (dropout): Dropout(p=0.33, inplace=False)
+  )
+)
+train_r_local: [[4.13143   3.8251498 4.2022805 ... 3.46096   4.9777036 3.8819532]
+ [4.1326137 3.825405  4.202767  ... 3.4611    4.9777055 3.8823788]
+ [3.8330228 3.6725302 4.1033254 ... 3.3236573 4.9774    3.8047073]
+ ...
+ [3.1168575 3.5055826 3.8102179 ... 3.2242472 4.9762387 3.5493011]
+ [3.1168575 3.5055826 3.8102179 ... 3.2242472 4.9762387 3.5493011]
+ [3.1168575 3.5055826 3.8102179 ... 3.2242472 4.9762387 3.5493011]]
+_train_r: [[4. 0. 0. ... 0. 0. 0.]
+ [0. 5. 0. ... 0. 0. 0.]
+ [0. 0. 5. ... 0. 0. 0.]
+ ...
+ [0. 0. 0. ... 0. 0. 0.]
+ [0. 0. 0. ... 0. 0. 0.]
+ [0. 0. 0. ... 0. 0. 0.]]
+_train_mask: [[1. 0. 0. ... 0. 0. 0.]
+ [0. 1. 0. ... 0. 0. 0.]
+ [0. 0. 1. ... 0. 0. 0.]
+ ...
+ [0. 0. 0. ... 0. 0. 0.]
+ [0. 0. 0. ... 0. 0. 0.]
+ [0. 0. 0. ... 0. 0. 0.]]
+
+Test Results:
+Early stopping fine-tuning at epoch: 237
+Early stopping fine-tuning at epoch: 241
+Early stopping fine-tuning at epoch: 251
+Early stopping fine-tuning at epoch: 261
+Early stopping fine-tuning at epoch: 271
+Early stopping fine-tuning at epoch: 281
+Early stopping fine-tuning at epoch: 420
+Early stopping fine-tuning at epoch: 421
+Early stopping fine-tuning at epoch: 900
+
+TEST:
+...
+                  |    MAE |   RMSE |    AUC |    MAP | NDCG@10 | Precision@10 | Recall@10 | Train (s) | Test (s)
+----------------- + ------ + ------ + ------ + ------ + ------- + ------------ + --------- + --------- + --------
+GlobalLocalKernel | 0.8029 | 0.9731 | 0.4371 | 0.0158 |  0.0168 |       0.0137 |    0.0110 |   55.5029 | 232.3715
+MF                | 0.7430 | 0.8998 | 0.7445 | 0.0548 |  0.0761 |       0.0675 |    0.0463 |    0.0302 |   0.8123
+PMF               | 0.7534 | 0.9138 | 0.7744 | 0.0671 |  0.0969 |       0.0813 |    0.0639 |    1.4669 |   1.2117
+BPR               | 2.0143 | 2.2267 | 0.8695 | 0.1042 |  0.1500 |       0.1110 |    0.1195 |    1.3102 |   0.7666
+
+
+========================================
diff --git a/project_notebook.ipynb b/project_notebook.ipynb
new file mode 100644
index 00000000..d8ec5555
--- /dev/null
+++ b/project_notebook.ipynb
@@ -0,0 +1,421 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Recommendation Systems Project\n",
+    "\n",
+    "By : ADJAL Mehdi Zakaria - BENBETKA Rachid - YAMANI Mohammed Kamel - Rami Boukaroura\n",
+    "\n",
+    "You will find the report in the following link : "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## How to run this notebook :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mehdiz/miniconda3/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "from datetime import datetime\n",
+    "import numpy as np\n",
+    "import cornac\n",
+    "from cornac.models import GlobalLocalKernel\n",
+    "from cornac.eval_methods import RatioSplit\n",
+    "from cornac.metrics import MAE, RMSE, Precision, Recall, NDCG, AUC, MAP\n",
+    "from cornac.models import MF, PMF, BPR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Function to find the next available log file name\n",
+    "def get_next_log_file(base_name=\"experiment_log\", ext=\".txt\"):\n",
+    "    counter = 1\n",
+    "    while os.path.exists(f\"{base_name}_{counter}{ext}\"):\n",
+    "        counter += 1\n",
+    "    return f\"{base_name}_{counter}{ext}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Function to log results\n",
+    "def log_results(log_file, test_results, model_instance):\n",
+    "    with open(log_file, \"a\") as f:\n",
+    "        f.write(\"\\n\" + \"=\" * 40 + \"\\n\")\n",
+    "        f.write(f\"Experiment conducted on: {datetime.now()}\\n\")\n",
+    "        f.write(\"\\nHyperparameters:\\n\")\n",
+    "        for attr, value in vars(model_instance).items():\n",
+    "            f.write(f\"{attr}: {value}\\n\")\n",
+    "        f.write(\"\\nTest Results:\\n\")\n",
+    "        f.write(test_results)\n",
+    "        f.write(\"\\n\" + \"=\" * 40 + \"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the MovieLens 100K dataset\n",
+    "ml_100k = cornac.datasets.movielens.load_feedback()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Split the data\n",
+    "rs = RatioSplit(data=ml_100k, test_size=0.2, rating_threshold=4.0, seed=123)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data matrix loaded\n",
+      "Number of users: 943\n",
+      "Number of movies: 1656\n",
+      "Number of training ratings: 80000\n",
+      "Number of test ratings: 19971\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get the total number of users and items in the subset\n",
+    "n_u = rs.total_users\n",
+    "n_m = rs.total_items\n",
+    "\n",
+    "print('Data matrix loaded')\n",
+    "print('Number of users: {}'.format(n_u))\n",
+    "print('Number of movies: {}'.format(n_m))\n",
+    "print('Number of training ratings: {}'.format(len(rs.train_set.uir_tuple[2])))\n",
+    "print('Number of test ratings: {}'.format(len(rs.test_set.uir_tuple[2])))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize your model\n",
+    "my_model = GlobalLocalKernel(\n",
+    "    # Example hyperparameters\n",
+    "    n_hid=10, \n",
+    "    n_dim=2, \n",
+    "    max_epoch_p=500, \n",
+    "    max_epoch_f=1000,\n",
+    "    lr_p=0.1,\n",
+    "    lr_f=0.01, \n",
+    "    verbose=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Models to compare with\n",
+    "mf = MF(k=10, max_iter=25, learning_rate=0.01, lambda_reg=0.02, use_bias=True, seed=123)\n",
+    "pmf = PMF(k=10, max_iter=100, learning_rate=0.001, lambda_reg=0.001, seed=123)\n",
+    "bpr = BPR(k=10, max_iter=200, learning_rate=0.001, lambda_reg=0.01, seed=123)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define some basic metrics\n",
+    "metrics = [MAE(), RMSE(), Precision(k=10), Recall(k=10), NDCG(k=10), AUC(), MAP()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Early stopping fine-tuning at epoch: 237\n",
+      "Early stopping fine-tuning at epoch: 241\n",
+      "Early stopping fine-tuning at epoch: 251\n",
+      "Early stopping fine-tuning at epoch: 261\n",
+      "Early stopping fine-tuning at epoch: 271\n",
+      "Early stopping fine-tuning at epoch: 281\n",
+      "Early stopping fine-tuning at epoch: 420\n",
+      "Early stopping fine-tuning at epoch: 421\n",
+      "Early stopping fine-tuning at epoch: 900\n",
+      "\n",
+      "TEST:\n",
+      "...\n",
+      "                  |    MAE |   RMSE |    AUC |    MAP | NDCG@10 | Precision@10 | Recall@10 | Train (s) | Test (s)\n",
+      "----------------- + ------ + ------ + ------ + ------ + ------- + ------------ + --------- + --------- + --------\n",
+      "GlobalLocalKernel | 0.8029 | 0.9731 | 0.4371 | 0.0158 |  0.0168 |       0.0137 |    0.0110 |   55.5029 | 232.3715\n",
+      "MF                | 0.7430 | 0.8998 | 0.7445 | 0.0548 |  0.0761 |       0.0675 |    0.0463 |    0.0302 |   0.8123\n",
+      "PMF               | 0.7534 | 0.9138 | 0.7744 | 0.0671 |  0.0969 |       0.0813 |    0.0639 |    1.4669 |   1.2117\n",
+      "BPR               | 2.0143 | 2.2267 | 0.8695 | 0.1042 |  0.1500 |       0.1110 |    0.1195 |    1.3102 |   0.7666\n",
+      "\n",
+      "\n",
+      "Experiment results and hyperparameters saved to experiment_log_3.txt\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Redirect Cornac output to capture experiment results\n",
+    "from io import StringIO\n",
+    "import sys\n",
+    "\n",
+    "# Get the next available log file name\n",
+    "log_file = get_next_log_file()\n",
+    "temp = sys.stdout  # Store original stdout object for later\n",
+    "sys.stdout = StringIO()  # Redirect stdout to capture results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epochs 1-10 (Pre-Training): 100%|██████████| 10/10 [00:01<00:00,  7.78it/s, Train RMSE=1.0664]\n",
+      "Epochs 11-20 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 32.31it/s, Train RMSE=1.1418]\n",
+      "Epochs 21-30 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 31.31it/s, Train RMSE=1.0929]\n",
+      "Epochs 31-40 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 34.31it/s, Train RMSE=1.0234]\n",
+      "Epochs 41-50 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 33.04it/s, Train RMSE=1.0055]\n",
+      "Epochs 51-60 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 33.87it/s, Train RMSE=0.9866]\n",
+      "Epochs 61-70 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 30.84it/s, Train RMSE=0.9950]\n",
+      "Epochs 71-80 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 27.34it/s, Train RMSE=0.9840]\n",
+      "Epochs 81-90 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 30.68it/s, Train RMSE=0.9846]\n",
+      "Epochs 91-100 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 32.19it/s, Train RMSE=0.9751]\n",
+      "Epochs 101-110 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 34.04it/s, Train RMSE=0.9751]\n",
+      "Epochs 111-120 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 30.87it/s, Train RMSE=0.9689]\n",
+      "Epochs 121-130 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 34.90it/s, Train RMSE=0.9731]\n",
+      "Epochs 131-140 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 35.47it/s, Train RMSE=0.9749]\n",
+      "Epochs 141-150 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 33.76it/s, Train RMSE=0.9686]\n",
+      "Epochs 151-160 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 27.36it/s, Train RMSE=0.9663]\n",
+      "Epochs 161-170 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 27.76it/s, Train RMSE=0.9661]\n",
+      "Epochs 171-180 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 30.29it/s, Train RMSE=0.9676]\n",
+      "Epochs 181-190 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 27.21it/s, Train RMSE=0.9617]\n",
+      "Epochs 191-200 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 27.10it/s, Train RMSE=0.9677]\n",
+      "Epochs 201-210 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 27.40it/s, Train RMSE=0.9643]\n",
+      "Epochs 211-220 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 28.24it/s, Train RMSE=0.9621]\n",
+      "Epochs 221-230 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 30.32it/s, Train RMSE=0.9631]\n",
+      "Epochs 231-240 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 31.95it/s, Train RMSE=0.9622]\n",
+      "Epochs 241-250 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 33.13it/s, Train RMSE=0.9638]\n",
+      "Epochs 251-260 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 30.76it/s, Train RMSE=0.9614]\n",
+      "Epochs 261-270 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 34.07it/s, Train RMSE=0.9603]\n",
+      "Epochs 271-280 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 32.32it/s, Train RMSE=0.9632]\n",
+      "Epochs 281-290 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 32.24it/s, Train RMSE=0.9629]\n",
+      "Epochs 291-300 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 36.41it/s, Train RMSE=0.9623]\n",
+      "Epochs 301-310 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 34.49it/s, Train RMSE=0.9590]\n",
+      "Epochs 311-320 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 35.98it/s, Train RMSE=0.9647]\n",
+      "Epochs 321-330 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 36.35it/s, Train RMSE=0.9590]\n",
+      "Epochs 331-340 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 33.30it/s, Train RMSE=0.9618]\n",
+      "Epochs 341-350 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 33.56it/s, Train RMSE=0.9651]\n",
+      "Epochs 351-360 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 35.91it/s, Train RMSE=0.9625]\n",
+      "Epochs 361-370 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 37.15it/s, Train RMSE=0.9614]\n",
+      "Epochs 371-380 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 34.79it/s, Train RMSE=0.9625]\n",
+      "Epochs 381-390 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 36.42it/s, Train RMSE=0.9602]\n",
+      "Epochs 391-400 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 36.12it/s, Train RMSE=0.9674]\n",
+      "Epochs 401-410 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 33.22it/s, Train RMSE=0.9601]\n",
+      "Epochs 411-420 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 34.75it/s, Train RMSE=0.9617]\n",
+      "Epochs 421-430 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 33.84it/s, Train RMSE=0.9644]\n",
+      "Epochs 431-440 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 34.14it/s, Train RMSE=0.9650]\n",
+      "Epochs 441-450 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 35.13it/s, Train RMSE=0.9620]\n",
+      "Epochs 451-460 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 36.43it/s, Train RMSE=0.9619]\n",
+      "Epochs 461-470 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 36.61it/s, Train RMSE=0.9631]\n",
+      "Epochs 471-480 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 36.99it/s, Train RMSE=0.9624]\n",
+      "Epochs 481-490 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 34.93it/s, Train RMSE=0.9621]\n",
+      "Epochs 491-500 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 36.42it/s, Train RMSE=0.9585]\n",
+      "Epochs 1-10  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 10.38it/s, Train RMSE=1.0211]\n",
+      "Epochs 11-20  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.77it/s, Train RMSE=1.0215]\n",
+      "Epochs 21-30  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.28it/s, Train RMSE=1.0187]\n",
+      "Epochs 31-40  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 24.56it/s, Train RMSE=1.0114]\n",
+      "Epochs 41-50  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 23.77it/s, Train RMSE=1.0070]\n",
+      "Epochs 51-60  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 24.97it/s, Train RMSE=1.0043]\n",
+      "Epochs 61-70  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 24.94it/s, Train RMSE=1.0020]\n",
+      "Epochs 71-80  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.79it/s, Train RMSE=0.9985]\n",
+      "Epochs 81-90  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.09it/s, Train RMSE=0.9941]\n",
+      "Epochs 91-100  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.00it/s, Train RMSE=0.9916]\n",
+      "Epochs 101-110  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.97it/s, Train RMSE=0.9878]\n",
+      "Epochs 111-120  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.22it/s, Train RMSE=0.9862]\n",
+      "Epochs 121-130  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 24.73it/s, Train RMSE=0.9843]\n",
+      "Epochs 131-140  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.51it/s, Train RMSE=0.9835]\n",
+      "Epochs 141-150  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.59it/s, Train RMSE=0.9822]\n",
+      "Epochs 151-160  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.25it/s, Train RMSE=0.9800]\n",
+      "Epochs 161-170  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.35it/s, Train RMSE=0.9798]\n",
+      "Epochs 171-180  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 24.83it/s, Train RMSE=0.9802]\n",
+      "Epochs 181-190  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.53it/s, Train RMSE=0.9809]\n",
+      "Epochs 191-200  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.09it/s, Train RMSE=0.9786]\n",
+      "Epochs 201-210  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.20it/s, Train RMSE=0.9794]\n",
+      "Epochs 211-220  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.44it/s, Train RMSE=0.9795]\n",
+      "Epochs 221-230  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.84it/s, Train RMSE=0.9793]\n",
+      "Epochs 231-240  (Fine-Tuning):  70%|███████   | 7/10 [00:00<00:00, 25.55it/s, Train RMSE=0.9889]\n",
+      "Epochs 241-250  (Fine-Tuning):  10%|█         | 1/10 [00:00<00:00, 24.93it/s, Train RMSE=0.9904]\n",
+      "Epochs 251-260  (Fine-Tuning):  10%|█         | 1/10 [00:00<00:00, 24.58it/s, Train RMSE=0.9919]\n",
+      "Epochs 261-270  (Fine-Tuning):  10%|█         | 1/10 [00:00<00:00, 25.29it/s, Train RMSE=0.9932]\n",
+      "Epochs 271-280  (Fine-Tuning):  10%|█         | 1/10 [00:00<00:00, 25.56it/s, Train RMSE=0.9939]\n",
+      "Epochs 281-290  (Fine-Tuning):  10%|█         | 1/10 [00:00<00:00, 24.34it/s, Train RMSE=0.9945]\n",
+      "Epochs 291-300  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.30it/s, Train RMSE=0.9963]\n",
+      "Epochs 301-310  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 24.97it/s, Train RMSE=0.9944]\n",
+      "Epochs 311-320  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.09it/s, Train RMSE=0.9927]\n",
+      "Epochs 321-330  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 24.74it/s, Train RMSE=0.9924]\n",
+      "Epochs 331-340  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.59it/s, Train RMSE=0.9921]\n",
+      "Epochs 341-350  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.34it/s, Train RMSE=0.9898]\n",
+      "Epochs 351-360  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.34it/s, Train RMSE=0.9880]\n",
+      "Epochs 361-370  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.82it/s, Train RMSE=0.9884]\n",
+      "Epochs 371-380  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.31it/s, Train RMSE=0.9881]\n",
+      "Epochs 381-390  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.86it/s, Train RMSE=0.9871]\n",
+      "Epochs 391-400  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.93it/s, Train RMSE=0.9845]\n",
+      "Epochs 401-410  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.54it/s, Train RMSE=0.9825]\n",
+      "Epochs 411-420  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.00it/s, Train RMSE=0.9951]\n",
+      "Epochs 421-430  (Fine-Tuning):  10%|█         | 1/10 [00:00<00:00, 26.12it/s, Train RMSE=0.9951]\n",
+      "Epochs 431-440  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 24.69it/s, Train RMSE=0.9976]\n",
+      "Epochs 441-450  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.06it/s, Train RMSE=0.9955]\n",
+      "Epochs 451-460  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.23it/s, Train RMSE=0.9955]\n",
+      "Epochs 461-470  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.02it/s, Train RMSE=0.9952]\n",
+      "Epochs 471-480  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.03it/s, Train RMSE=0.9927]\n",
+      "Epochs 481-490  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.06it/s, Train RMSE=0.9907]\n",
+      "Epochs 491-500  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.02it/s, Train RMSE=0.9886]\n",
+      "Epochs 501-510  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.28it/s, Train RMSE=0.9852]\n",
+      "Epochs 511-520  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.31it/s, Train RMSE=0.9826]\n",
+      "Epochs 521-530  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.07it/s, Train RMSE=0.9818]\n",
+      "Epochs 531-540  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 24.90it/s, Train RMSE=0.9806]\n",
+      "Epochs 541-550  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.27it/s, Train RMSE=0.9787]\n",
+      "Epochs 551-560  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.95it/s, Train RMSE=0.9783]\n",
+      "Epochs 561-570  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.24it/s, Train RMSE=0.9788]\n",
+      "Epochs 571-580  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.35it/s, Train RMSE=0.9777]\n",
+      "Epochs 581-590  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.06it/s, Train RMSE=0.9759]\n",
+      "Epochs 591-600  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.36it/s, Train RMSE=0.9762]\n",
+      "Epochs 601-610  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.81it/s, Train RMSE=0.9772]\n",
+      "Epochs 611-620  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.54it/s, Train RMSE=0.9769]\n",
+      "Epochs 621-630  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.09it/s, Train RMSE=0.9775]\n",
+      "Epochs 631-640  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 24.63it/s, Train RMSE=0.9789]\n",
+      "Epochs 641-650  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.54it/s, Train RMSE=0.9786]\n",
+      "Epochs 651-660  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.02it/s, Train RMSE=0.9775]\n",
+      "Epochs 661-670  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.20it/s, Train RMSE=0.9778]\n",
+      "Epochs 671-680  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.15it/s, Train RMSE=0.9762]\n",
+      "Epochs 681-690  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.07it/s, Train RMSE=0.9761]\n",
+      "Epochs 691-700  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.43it/s, Train RMSE=0.9769]\n",
+      "Epochs 701-710  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.48it/s, Train RMSE=0.9753]\n",
+      "Epochs 711-720  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.60it/s, Train RMSE=0.9756]\n",
+      "Epochs 721-730  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 23.90it/s, Train RMSE=0.9748]\n",
+      "Epochs 731-740  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.89it/s, Train RMSE=0.9752]\n",
+      "Epochs 741-750  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.83it/s, Train RMSE=0.9761]\n",
+      "Epochs 751-760  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.44it/s, Train RMSE=0.9745]\n",
+      "Epochs 761-770  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.75it/s, Train RMSE=0.9765]\n",
+      "Epochs 771-780  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.28it/s, Train RMSE=0.9768]\n",
+      "Epochs 781-790  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.24it/s, Train RMSE=0.9758]\n",
+      "Epochs 791-800  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.44it/s, Train RMSE=0.9742]\n",
+      "Epochs 801-810  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 24.71it/s, Train RMSE=0.9744]\n",
+      "Epochs 811-820  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.86it/s, Train RMSE=0.9743]\n",
+      "Epochs 821-830  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.00it/s, Train RMSE=0.9756]\n",
+      "Epochs 831-840  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 24.98it/s, Train RMSE=0.9761]\n",
+      "Epochs 841-850  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.81it/s, Train RMSE=0.9756]\n",
+      "Epochs 851-860  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.08it/s, Train RMSE=0.9757]\n",
+      "Epochs 861-870  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.10it/s, Train RMSE=0.9746]\n",
+      "Epochs 871-880  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.47it/s, Train RMSE=0.9723]\n",
+      "Epochs 881-890  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.26it/s, Train RMSE=0.9714]\n",
+      "Epochs 891-900  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 24.00it/s, Train RMSE=0.9745]\n",
+      "Epochs 901-910  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.13it/s, Train RMSE=0.9725]\n",
+      "Epochs 911-920  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.75it/s, Train RMSE=0.9722]\n",
+      "Epochs 921-930  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.83it/s, Train RMSE=0.9725]\n",
+      "Epochs 931-940  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.21it/s, Train RMSE=0.9710]\n",
+      "Epochs 941-950  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.10it/s, Train RMSE=0.9738]\n",
+      "Epochs 951-960  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.54it/s, Train RMSE=0.9738]\n",
+      "Epochs 961-970  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 23.58it/s, Train RMSE=0.9715]\n",
+      "Epochs 971-980  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 24.55it/s, Train RMSE=0.9731]\n",
+      "Epochs 981-990  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.77it/s, Train RMSE=0.9722]\n",
+      "Epochs 991-1000  (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.22it/s, Train RMSE=0.9720]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Run the experiment on the smaller subset\n",
+    "cornac.Experiment(eval_method=rs, models=[my_model, mf, pmf, bpr], metrics=metrics, user_based=True).run()\n",
+    "\n",
+    "# Retrieve experiment results\n",
+    "experiment_results = sys.stdout.getvalue()\n",
+    "sys.stdout = temp # Restore stdout to original state"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Print the results to the console\n",
+    "print(experiment_results)\n",
+    "\n",
+    "# Log results to file\n",
+    "log_results(log_file, experiment_results, my_model)\n",
+    "\n",
+    "print(f\"Experiment results and hyperparameters saved to {log_file}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}