hipom_data_mapping/translation/t5/1.data_process_concat.ipynb

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      "text/plain": [
       "Saving the dataset (0/1 shards):   0%|          | 0/6260 [00:00<?, ? examples/s]"
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      "text/plain": [
       "Saving the dataset (0/1 shards):   0%|          | 0/12969 [00:00<?, ? examples/s]"
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      "text/plain": [
       "Saving the dataset (0/1 shards):   0%|          | 0/2087 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset saved to 'combined_data'\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import json\n",
    "from datasets import Dataset, DatasetDict\n",
    "\n",
    "group_number = 5\n",
    "mode = 'td_unit'\n",
    "\n",
    "def load_group_data(group_number):\n",
    "    group_folder = os.path.join('../../data_preprocess/dataset', str(group_number))\n",
    "    train_file_path = os.path.join(group_folder, 'train.csv')\n",
    "    valid_file_path = os.path.join(group_folder, 'valid.csv')\n",
    "    test_file_path = os.path.join(group_folder, 'test.csv')\n",
    "    \n",
    "    if not os.path.exists(train_file_path) or not os.path.exists(valid_file_path) or not os.path.exists(test_file_path):\n",
    "        raise FileNotFoundError(f\"Files for group {group_number} not found.\")\n",
    "    \n",
    "    return pd.read_csv(train_file_path), pd.read_csv(valid_file_path), pd.read_csv(test_file_path)\n",
    "\n",
    "def process_df(df, mode='only_td'):\n",
    "    output_list = []\n",
    "    for idx, row in df.iterrows():\n",
    "        try:\n",
    "            if mode == 'only_td':\n",
    "                input_str = f\"<TD_START>{row['tag_description']}<TD_END>\"\n",
    "            elif mode == 'tn_td':\n",
    "                input_str = f\"<TN_START>{row['tag_name']}<TN_END><TD_START>{row['tag_description']}<TD_END>\"\n",
    "            elif mode == 'tn_td_min_max':\n",
    "                input_str = f\"<TN_START>{row['tag_name']}<TN_END><TD_START>{row['tag_description']}<TD_END><MIN_START>{row['min']}<MIN_END><MAX_START>{row['max']}<MAX_END>\"\n",
    "            elif mode == 'td_min_max':\n",
    "                input_str = f\"<TD_START>{row['tag_description']}<TD_END><MIN_START>{row['min']}<MIN_END><MAX_START>{row['max']}<MAX_END>\"\n",
    "            elif mode == 'td_unit':\n",
    "                input_str = f\"<TD_START>{row['tag_description']}<TD_END><UNIT_START>{row['unit']}<UNIT_END>\"\n",
    "            elif mode == 'tn_td_unit':\n",
    "                input_str = f\"<TN_START>{row['tag_name']}<TN_END><TD_START>{row['tag_description']}<TD_END><UNIT_START>{row['unit']}<UNIT_END>\"\n",
    "            elif mode == 'td_min_max_unit':\n",
    "                input_str = f\"<TD_START>{row['tag_description']}<TD_END><MIN_START>{row['min']}<MIN_END><MAX_START>{row['max']}<MAX_END><UNIT_START>{row['unit']}<UNIT_END>\"\n",
    "            else:\n",
    "                raise ValueError(\"Invalid mode specified\")\n",
    "            \n",
    "            output_list.append({\n",
    "                'translation': {\n",
    "                    'ships_idx': row['ships_idx'],\n",
    "                    'input': input_str,\n",
    "                    'thing_property': f\"<THING_START>{row['thing']}<THING_END><PROPERTY_START>{row['property']}<PROPERTY_END>\",\n",
    "                    'answer': f\"{row['thing']} {row['property']}\",\n",
    "                }\n",
    "            })\n",
    "        except Exception as e:\n",
    "            print(f\"Error processing row at index {idx}: {e}\")\n",
    "    return output_list\n",
    "\n",
    "train_data, valid_data, test_data = load_group_data(group_number)\n",
    "combined_dict = {\"mode\": mode, \"fold_group\": group_number}\n",
    "with open(\"mode.json\", \"w\") as json_file:\n",
    "    json.dump(combined_dict, json_file)\n",
    "\n",
    "combined_data = DatasetDict({\n",
    "    'train': Dataset.from_list(process_df(train_data, mode=mode)),\n",
    "    'test': Dataset.from_list(process_df(test_data, mode=mode)),\n",
    "    'validation': Dataset.from_list(process_df(valid_data, mode=mode)),\n",
    "})\n",
    "\n",
    "combined_data.save_to_disk(f\"combined_data/{mode}/{group_number}\")\n",
    "print(\"Dataset saved to 'combined_data'\")\n"
   ]
  }
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