test_recbole.ipynb

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      "/home/adhi/epm-recsys-dev/.env-epm/lib/python3.10/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",
      "2023-12-20 11:26:40,120\tINFO util.py:159 -- Missing packages: ['ipywidgets']. Run `pip install -U ipywidgets`, then restart the notebook server for rich notebook output.\n",
      "2023-12-20 11:26:40,262\tINFO util.py:159 -- Missing packages: ['ipywidgets']. Run `pip install -U ipywidgets`, then restart the notebook server for rich notebook output.\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from recbole.utils.case_study import full_sort_topk, full_sort_scores\n",
    "from recbole.quick_start import load_data_and_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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     "text": [
      "20 Dec 11:26    INFO  \n",
      "General Hyper Parameters:\n",
      "gpu_id = 0\n",
      "use_gpu = True\n",
      "seed = 2020\n",
      "state = INFO\n",
      "reproducibility = True\n",
      "data_path = dataset/epm\n",
      "checkpoint_dir = saved/\n",
      "show_progress = True\n",
      "save_dataset = False\n",
      "dataset_save_path = None\n",
      "save_dataloaders = False\n",
      "dataloaders_save_path = None\n",
      "log_wandb = True\n",
      "\n",
      "Training Hyper Parameters:\n",
      "epochs = 600\n",
      "train_batch_size = 4096\n",
      "learner = adam\n",
      "learning_rate = 0.001\n",
      "train_neg_sample_args = {'distribution': 'uniform', 'sample_num': 1, 'alpha': 1.0, 'dynamic': False, 'candidate_num': 0}\n",
      "eval_step = 1\n",
      "stopping_step = 10\n",
      "clip_grad_norm = None\n",
      "weight_decay = 0.0\n",
      "loss_decimal_place = 4\n",
      "\n",
      "Evaluation Hyper Parameters:\n",
      "eval_args = {'split': {'RS': [0.8, 0.1, 0.1]}, 'order': 'RO', 'group_by': 'user', 'mode': {'valid': 'full', 'test': 'full'}}\n",
      "repeatable = False\n",
      "metrics = ['Recall', 'MRR', 'NDCG', 'Hit', 'Precision']\n",
      "topk = [10]\n",
      "valid_metric = MRR@10\n",
      "valid_metric_bigger = True\n",
      "eval_batch_size = 4096\n",
      "metric_decimal_place = 4\n",
      "\n",
      "Dataset Hyper Parameters:\n",
      "field_separator = \t\n",
      "seq_separator =  \n",
      "USER_ID_FIELD = user_id\n",
      "ITEM_ID_FIELD = item_id\n",
      "RATING_FIELD = rating\n",
      "TIME_FIELD = timestamp\n",
      "seq_len = None\n",
      "LABEL_FIELD = label\n",
      "threshold = None\n",
      "NEG_PREFIX = neg_\n",
      "load_col = {'inter': ['user_id', 'item_id']}\n",
      "unload_col = None\n",
      "unused_col = None\n",
      "additional_feat_suffix = None\n",
      "rm_dup_inter = None\n",
      "val_interval = None\n",
      "filter_inter_by_user_or_item = True\n",
      "user_inter_num_interval = [0,inf)\n",
      "item_inter_num_interval = [0,inf)\n",
      "alias_of_user_id = None\n",
      "alias_of_item_id = None\n",
      "alias_of_entity_id = None\n",
      "alias_of_relation_id = None\n",
      "preload_weight = None\n",
      "normalize_field = None\n",
      "normalize_all = None\n",
      "ITEM_LIST_LENGTH_FIELD = item_length\n",
      "LIST_SUFFIX = _list\n",
      "MAX_ITEM_LIST_LENGTH = 50\n",
      "POSITION_FIELD = position_id\n",
      "HEAD_ENTITY_ID_FIELD = head_id\n",
      "TAIL_ENTITY_ID_FIELD = tail_id\n",
      "RELATION_ID_FIELD = relation_id\n",
      "ENTITY_ID_FIELD = entity_id\n",
      "benchmark_filename = None\n",
      "\n",
      "Other Hyper Parameters: \n",
      "worker = 16\n",
      "wandb_project = ncf-epm-rank\n",
      "shuffle = True\n",
      "require_pow = False\n",
      "enable_amp = False\n",
      "enable_scaler = False\n",
      "transform = None\n",
      "mf_embedding_size = 32\n",
      "mlp_embedding_size = 32\n",
      "mlp_hidden_size = [256, 128, 64, 32]\n",
      "dropout_prob = 0.1\n",
      "mf_train = True\n",
      "mlp_train = True\n",
      "use_pretrain = False\n",
      "mf_pretrain_path = None\n",
      "mlp_pretrain_path = None\n",
      "numerical_features = []\n",
      "discretization = None\n",
      "kg_reverse_r = False\n",
      "entity_kg_num_interval = [0,inf)\n",
      "relation_kg_num_interval = [0,inf)\n",
      "MODEL_TYPE = ModelType.GENERAL\n",
      "MODEL_INPUT_TYPE = InputType.POINTWISE\n",
      "eval_type = EvaluatorType.RANKING\n",
      "single_spec = True\n",
      "local_rank = 0\n",
      "device = cuda\n",
      "valid_neg_sample_args = {'distribution': 'uniform', 'sample_num': 'none'}\n",
      "test_neg_sample_args = {'distribution': 'uniform', 'sample_num': 'none'}\n",
      "\n",
      "\n",
      "20 Dec 11:26    INFO  epm\n",
      "The number of users: 489\n",
      "Average actions of users: 141.57172131147541\n",
      "The number of items: 1278\n",
      "Average actions of items: 54.101018010963195\n",
      "The number of inters: 69087\n",
      "The sparsity of the dataset: 88.94505410102057%\n",
      "Remain Fields: ['user_id', 'item_id']\n",
      "20 Dec 11:26    INFO  [Training]: train_batch_size = [4096] train_neg_sample_args: [{'distribution': 'uniform', 'sample_num': 1, 'alpha': 1.0, 'dynamic': False, 'candidate_num': 0}]\n",
      "20 Dec 11:26    INFO  [Evaluation]: eval_batch_size = [4096] eval_args: [{'split': {'RS': [0.8, 0.1, 0.1]}, 'order': 'RO', 'group_by': 'user', 'mode': {'valid': 'full', 'test': 'full'}}]\n"
     ]
    }
   ],
   "source": [
    "config, model, dataset, train_data, valid_data, test_data = load_data_and_model(\n",
    "    model_file=\"saved/[256, 128, 64, 32]_0001.pth\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Just for checking userID/ShiptoID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ship_to_id</th>\n",
       "      <th>item_code</th>\n",
       "      <th>final_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EPM_34952</td>\n",
       "      <td>TCTLB</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EPM_35444</td>\n",
       "      <td>TCTLB</td>\n",
       "      <td>2.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EPM_3238950</td>\n",
       "      <td>TCTLB</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EPM_136080</td>\n",
       "      <td>TCTLB</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EPM_4393798</td>\n",
       "      <td>TCTLB</td>\n",
       "      <td>2.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ship_to_id item_code  final_rating\n",
       "0    EPM_34952     TCTLB           2.9\n",
       "1    EPM_35444     TCTLB           2.7\n",
       "2  EPM_3238950     TCTLB           1.9\n",
       "3   EPM_136080     TCTLB           4.4\n",
       "4  EPM_4393798     TCTLB           2.7"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_parquet(\"raw_data/RatingMatrix.parquet\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loop Here for each user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# uid_series = np.array([1, 2])  # internal user id series\n",
    "# or you can use dataset.token2id to transfer external user token to internal user id\n",
    "uid_series = dataset.token2id(dataset.uid_field, [\"EPM_34952\"])\n",
    "uid_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "topk_score, topk_iid_list = full_sort_topk(\n",
    "    uid_series, model, test_data, k=10, device=config[\"device\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.9886, 0.9707, 0.9690, 0.9628, 0.9519, 0.9459, 0.9436, 0.9358, 0.9339,\n",
      "         0.9316]], device='cuda:0')\n",
      "[['TSVND' 'KLSPB' 'LDPDA' 'TCETA' 'TSKAA' 'TGMPJ' 'MYGPB' 'KFNBD' 'BBF19'\n",
      "  'TCDRA']]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(topk_score)  # scores of top 10 items\n",
    "# print(topk_iid_list)  # internal id of top 10 items\n",
    "external_item_list = dataset.id2token(dataset.iid_field, topk_iid_list.cpu())\n",
    "print(external_item_list)  # external tokens of top 10 items\n",
    "print()\n",
    "\n",
    "\n",
    "\n",
    "# score = full_sort_scores(uid_series, model, test_data, device=config[\"device\"])\n",
    "# print(score)  # score of all items\n",
    "# print(\n",
    "#     score[0, dataset.token2id(dataset.iid_field, [\"10137\"])]\n",
    "# )  # score of item ['242', '302'] for user '196'.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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