Feature_extraction_stvae.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c6b0b00e-bbf0-4da6-84d6-508bd7c2c386",
   "metadata": {},
   "source": [
    "## Feature extraction from TabularFM"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d5ddb6b-f820-477c-8dd3-ed0b68c6b80e",
   "metadata": {},
   "source": [
    "This notebook demonstrates how to extract features from pretrained TabularFM in a zero-shot settings and use those features as additional information for regression or classification downstream tasks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a0a9546-395b-432b-9a59-e9e53a64921a",
   "metadata": {},
   "source": [
    "### Feature extraction from a pretrained models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f8c3c8c-d884-46ea-b4ab-ca0e7b38ecf4",
   "metadata": {},
   "source": [
    "### Download models and datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0e543046-2995-43e2-8d4c-0a0c36a49e10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "mkdir: cannot create directory ‘datasets’: File exists\n",
      "--2024-08-12 09:07:41--  http://gittables_v4.zip/\n",
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      "--2024-08-12 09:07:41--  https://huggingface.co/datasets/lamthuy/TabularFM-GitTables/resolve/main/gittables_v4.zip\n",
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      "   creating: datasets/gittables_v4/growth_rate_csv_licensed_Angola/\n",
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      "   creating: datasets/gittables_v4/id_csv_licensed_Chris/\n",
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      "   creating: datasets/gittables_v4/id_csv_licensed_Configuration3/\n",
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      "   creating: datasets/gittables_v4/growth_rate_csv_licensed_Kazakhstan/\n",
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      "   creating: datasets/gittables_v4/id_csv_licensed_AssessmentResults/\n",
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      "   creating: datasets/gittables_v4/growth_rate_csv_licensed_South%20Korea/\n",
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      "   creating: datasets/gittables_v4/thing_csv_licensed_Day/\n",
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      "   creating: datasets/gittables_v4/whole_csv_licensed_x/\n",
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      "   creating: datasets/gittables_v4/show_time_csv_licensed_PS4/\n",
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      "   creating: datasets/gittables_v4/kilometers_per_hour_csv_licensed_links/\n",
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      "   creating: datasets/gittables_v4/show_time_csv_licensed_not/\n",
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      "   creating: datasets/gittables_v4/id_csv_licensed_DeveloperData/\n",
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      "   creating: datasets/gittables_v4/miles_per_hour_csv_licensed_2009/\n",
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      "   creating: datasets/gittables_v4/whole_csv_licensed_CU/\n",
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      "   creating: datasets/gittables_v4/thing_csv_licensed_BankOfAmerica/\n",
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      "   creating: datasets/gittables_v4/neonatal_mortality_csv_licensed_project-40/\n",
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      "   creating: datasets/gittables_v4/whole_csv_licensed_DanNotaggInsFasDan/\n",
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      "   creating: datasets/gittables_v4/thing_csv_licensed_Tush/\n",
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      "   creating: datasets/gittables_v4/real_time_csv_licensed_fracnetwork014/\n",
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      "   creating: datasets/gittables_v4/id_csv_licensed_GroupB/\n",
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      "   creating: datasets/gittables_v4/id_csv_licensed_GroupA/\n",
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      "   creating: datasets/gittables_v4/real_time_csv_licensed_O/\n",
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      "   creating: datasets/gittables_v4/whole_csv_licensed_3dcd17e2bf20e2b4c4fd6d46b0b8bec12dbd0ebe011a9ed4d5987ba217888d2d/\n",
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      "   creating: datasets/gittables_v4/growth_rate_csv_licensed_Uganda/\n",
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      "   creating: datasets/gittables_v4/thing_csv_licensed_PopHumDar/\n",
      "  inflating: datasets/gittables_v4/thing_csv_licensed_PopHumDar/metadata.json  \n",
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      "   creating: datasets/gittables_v4/id_csv_licensed_fig8/\n",
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      "   creating: datasets/gittables_v4/whole_csv_licensed_a42627cfea482a8ed81312034f8a6af8e4147bea6e1fac026e70619e2ecbd732/\n",
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      "   creating: datasets/gittables_v4/id_csv_licensed_ab/\n",
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      "   creating: datasets/gittables_v4/whole_csv_licensed_hipc/\n",
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      "   creating: datasets/gittables_v4/id_csv_licensed_xdamOutFlow/\n",
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      "   creating: datasets/gittables_v4/real_time_csv_licensed_2018-11-27-events/\n",
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      "   creating: datasets/gittables_v4/real_time_csv_licensed_2009/\n",
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      "   creating: datasets/gittables_v4/id_csv_licensed_SubInformation/\n",
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      "   creating: datasets/gittables_v4/whole_csv_licensed_1tst/\n",
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      "   creating: datasets/gittables_v4/crime_rate_csv_licensed_PropertyCrimeRates/\n",
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      "   creating: datasets/gittables_v4/growth_rate_csv_licensed_words-without-force-positions/\n",
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      "   creating: datasets/gittables_v4/id_csv_licensed_languages/\n",
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      "   creating: datasets/gittables_v4/data_rate_csv_licensed_WorkoutCustomTargetValues/\n",
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      "   creating: datasets/gittables_v4/data_rate_csv_licensed_DeveloperData/\n",
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      "   creating: datasets/gittables_v4/id_csv_licensed_education/\n",
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      "   creating: datasets/gittables_v4/growth_rate_csv_licensed_Zambia/\n",
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      "   creating: datasets/gittables_v4/id_csv_licensed_Bragantino/\n",
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      "   creating: datasets/gittables_v4/question_time_csv_licensed_reviewer/\n",
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      "   creating: datasets/gittables_v4/processing_time_csv_licensed_program/\n",
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      "   creating: datasets/gittables_v4/id_csv_licensed_09/\n",
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      "   creating: datasets/gittables_v4/growth_rate_csv_licensed_Nicaragua/\n",
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      "   creating: datasets/gittables_v4/id_csv_licensed_arstatus/\n",
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      "   creating: datasets/gittables_v4/growth_rate_csv_licensed_2020-10-12/\n",
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      "   creating: datasets/gittables_v4/id_csv_licensed_imdb/\n",
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      "   creating: datasets/gittables_v4/id_csv_licensed_Athl%C3%A9tico-PR/\n",
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      "   creating: datasets/gittables_v4/id_csv_licensed_-1/\n",
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      "   creating: datasets/gittables_v4/id_csv_licensed_49a918dc51d8d4063061e5b0d257943aa194370ae1f07a1c1fc746f7ed7dd3b3/\n",
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      "   creating: datasets/gittables_v4/id_csv_licensed_orders/\n",
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      "   creating: datasets/gittables_v4/growth_rate_csv_licensed_Kuwait/\n",
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      "   creating: datasets/gittables_v4/growth_rate_csv_licensed_Senegal/\n",
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      "   creating: datasets/gittables_v4/inflation_rate_csv_licensed_econFreedomCovMatrix/\n",
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      "   creating: datasets/gittables_v4/real_time_csv_licensed_25/\n",
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      "   creating: datasets/gittables_v4/growth_rate_csv_licensed_Haiti/\n",
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      "   creating: datasets/gittables_v4/id_csv_licensed_procedure/\n",
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      "   creating: datasets/gittables_v4/halftime_csv_licensed_HipAggBriInsSlo/\n",
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      "   creating: datasets/gittables_v4/growth_rate_csv_licensed_Ethiopia/\n",
      "  inflating: datasets/gittables_v4/growth_rate_csv_licensed_Ethiopia/metadata.json  \n",
      "  inflating: datasets/gittables_v4/growth_rate_csv_licensed_Ethiopia/growth_rate_csv_licensed_Ethiopia.csv  \n",
      "  inflating: datasets/gittables_v4/growth_rate_csv_licensed_Ethiopia/transformer_v3.pkl  \n",
      "  inflating: datasets/gittables_v4/growth_rate_csv_licensed_Ethiopia/clean_info.json  \n",
      "   creating: datasets/gittables_v4/id_csv_licensed_all/\n",
      "  inflating: datasets/gittables_v4/id_csv_licensed_all/metadata.json  \n",
      "  inflating: datasets/gittables_v4/id_csv_licensed_all/transformer_v3.pkl  \n",
      "  inflating: datasets/gittables_v4/id_csv_licensed_all/clean_info.json  \n",
      "  inflating: datasets/gittables_v4/id_csv_licensed_all/id_csv_licensed_all.csv  \n",
      "   creating: datasets/gittables_v4/thing_csv_licensed_A/\n",
      "  inflating: datasets/gittables_v4/thing_csv_licensed_A/metadata.json  \n",
      "  inflating: datasets/gittables_v4/thing_csv_licensed_A/transformer_v3.pkl  \n",
      "  inflating: datasets/gittables_v4/thing_csv_licensed_A/clean_info.json  \n",
      "  inflating: datasets/gittables_v4/thing_csv_licensed_A/thing_csv_licensed_A.csv  \n",
      "   creating: datasets/gittables_v4/growth_rate_csv_licensed_Iraq/\n",
      "  inflating: datasets/gittables_v4/growth_rate_csv_licensed_Iraq/metadata.json  \n",
      "  inflating: datasets/gittables_v4/growth_rate_csv_licensed_Iraq/growth_rate_csv_licensed_Iraq.csv  \n",
      "  inflating: datasets/gittables_v4/growth_rate_csv_licensed_Iraq/transformer_v3.pkl  \n",
      "  inflating: datasets/gittables_v4/growth_rate_csv_licensed_Iraq/clean_info.json  \n",
      "   creating: datasets/gittables_v4/whole_csv_licensed_e151de92bb42a72ed4620b55ede559687f34c98752bd9446ad2390bddcd82834/\n",
      "  inflating: datasets/gittables_v4/whole_csv_licensed_e151de92bb42a72ed4620b55ede559687f34c98752bd9446ad2390bddcd82834/metadata.json  \n",
      "  inflating: datasets/gittables_v4/whole_csv_licensed_e151de92bb42a72ed4620b55ede559687f34c98752bd9446ad2390bddcd82834/transformer_v3.pkl  \n",
      "  inflating: datasets/gittables_v4/whole_csv_licensed_e151de92bb42a72ed4620b55ede559687f34c98752bd9446ad2390bddcd82834/clean_info.json  \n",
      "  inflating: datasets/gittables_v4/whole_csv_licensed_e151de92bb42a72ed4620b55ede559687f34c98752bd9446ad2390bddcd82834/whole_csv_licensed_e151de92bb42a72ed4620b55ede559687f34c98752bd9446ad2390bddcd82834.csv  \n",
      "   creating: datasets/gittables_v4/id_csv_licensed_1acc207e89a6a1ea460d438c9d4cbcdb2c629c4691d623ffa87df13773259587/\n",
      "  inflating: datasets/gittables_v4/id_csv_licensed_1acc207e89a6a1ea460d438c9d4cbcdb2c629c4691d623ffa87df13773259587/metadata.json  \n",
      "  inflating: datasets/gittables_v4/id_csv_licensed_1acc207e89a6a1ea460d438c9d4cbcdb2c629c4691d623ffa87df13773259587/transformer_v3.pkl  \n",
      "  inflating: datasets/gittables_v4/id_csv_licensed_1acc207e89a6a1ea460d438c9d4cbcdb2c629c4691d623ffa87df13773259587/clean_info.json  \n",
      "  inflating: datasets/gittables_v4/id_csv_licensed_1acc207e89a6a1ea460d438c9d4cbcdb2c629c4691d623ffa87df13773259587/id_csv_licensed_1acc207e89a6a1ea460d438c9d4cbcdb2c629c4691d623ffa87df13773259587.csv  \n",
      "   creating: datasets/gittables_v4/thing_csv_licensed_Come/\n",
      "  inflating: datasets/gittables_v4/thing_csv_licensed_Come/metadata.json  \n",
      "  inflating: datasets/gittables_v4/thing_csv_licensed_Come/thing_csv_licensed_Come.csv  \n",
      "  inflating: datasets/gittables_v4/thing_csv_licensed_Come/transformer_v3.pkl  \n",
      "  inflating: datasets/gittables_v4/thing_csv_licensed_Come/clean_info.json  \n",
      "   creating: datasets/gittables_v4/growth_rate_csv_licensed_Mongolia/\n",
      "  inflating: datasets/gittables_v4/growth_rate_csv_licensed_Mongolia/metadata.json  \n",
      "  inflating: datasets/gittables_v4/growth_rate_csv_licensed_Mongolia/transformer_v3.pkl  \n",
      "  inflating: datasets/gittables_v4/growth_rate_csv_licensed_Mongolia/clean_info.json  \n",
      "  inflating: datasets/gittables_v4/growth_rate_csv_licensed_Mongolia/growth_rate_csv_licensed_Mongolia.csv  \n",
      "   creating: datasets/gittables_v4/thing_csv_licensed_Lady/\n",
      "  inflating: datasets/gittables_v4/thing_csv_licensed_Lady/metadata.json  \n",
      "  inflating: datasets/gittables_v4/thing_csv_licensed_Lady/transformer_v3.pkl  \n",
      "  inflating: datasets/gittables_v4/thing_csv_licensed_Lady/thing_csv_licensed_Lady.csv  \n",
      "  inflating: datasets/gittables_v4/thing_csv_licensed_Lady/clean_info.json  \n",
      "   creating: datasets/gittables_v4/id_csv_licensed_forrest-0/\n",
      "  inflating: datasets/gittables_v4/id_csv_licensed_forrest-0/metadata.json  \n",
      "  inflating: datasets/gittables_v4/id_csv_licensed_forrest-0/transformer_v3.pkl  \n",
      "  inflating: datasets/gittables_v4/id_csv_licensed_forrest-0/clean_info.json  \n",
      "  inflating: datasets/gittables_v4/id_csv_licensed_forrest-0/id_csv_licensed_forrest-0.csv  \n",
      "   creating: datasets/gittables_v4/show_time_csv_licensed_Magnification.dll/\n",
      "  inflating: datasets/gittables_v4/show_time_csv_licensed_Magnification.dll/metadata.json  \n",
      "  inflating: datasets/gittables_v4/show_time_csv_licensed_Magnification.dll/transformer_v3.pkl  \n",
      "  inflating: datasets/gittables_v4/show_time_csv_licensed_Magnification.dll/clean_info.json  \n",
      "  inflating: datasets/gittables_v4/show_time_csv_licensed_Magnification.dll/show_time_csv_licensed_Magnification.dll.csv  \n",
      "   creating: datasets/gittables_v4/growth_rate_csv_licensed_Denmark/\n",
      "  inflating: datasets/gittables_v4/growth_rate_csv_licensed_Denmark/metadata.json  \n",
      "  inflating: datasets/gittables_v4/growth_rate_csv_licensed_Denmark/transformer_v3.pkl  \n",
      "  inflating: datasets/gittables_v4/growth_rate_csv_licensed_Denmark/clean_info.json  \n",
      "  inflating: datasets/gittables_v4/growth_rate_csv_licensed_Denmark/growth_rate_csv_licensed_Denmark.csv  \n",
      "   creating: datasets/gittables_v4/id_csv_licensed_Allen2020ipf/\n",
      "  inflating: datasets/gittables_v4/id_csv_licensed_Allen2020ipf/metadata.json  \n",
      "  inflating: datasets/gittables_v4/id_csv_licensed_Allen2020ipf/id_csv_licensed_Allen2020ipf.csv  \n",
      "  inflating: datasets/gittables_v4/id_csv_licensed_Allen2020ipf/transformer_v3.pkl  \n",
      "  inflating: datasets/gittables_v4/id_csv_licensed_Allen2020ipf/clean_info.json  \n"
     ]
    }
   ],
   "source": [
    "# download model\n",
    "! mkdir models\n",
    "! wget stvae_gittables.zip \"https://huggingface.co/lamthuy/stvae_gittables/resolve/main/stvae_gittables.zip\" -P models/\n",
    "! unzip -o models/stvae_gittables.zip -d models/\n",
    "\n",
    "# download dataset\n",
    "! mkdir datasets\n",
    "! wget gittables_v4.zip https://huggingface.co/datasets/lamthuy/TabularFM-GitTables/resolve/main/gittables_v4.zip -P datasets/\n",
    "! unzip -o datasets/gittables_v4.zip -d datasets/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c87af98e-44b3-443c-afc1-fc7aafa5726c",
   "metadata": {},
   "source": [
    "### Load models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e901a813-7af8-4091-8dfb-c1e56953f421",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kdts33/.local/lib/python3.9/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",
      "/home/kdts33/tabsyn_project/official_tabularfm/TabularFM/tabularfm/utils/processing.py:412: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  model.encoder.load_state_dict(torch.load(os.path.join(path, 'encoder_weights.pt'), map_location=torch.device('cpu')))\n",
      "/home/kdts33/tabsyn_project/official_tabularfm/TabularFM/tabularfm/utils/processing.py:413: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  model.decoder.load_state_dict(torch.load(os.path.join(path, 'decoder_weights.pt'), map_location=torch.device('cpu')))\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.append('tabularfm')\n",
    "\n",
    "from tabularfm.utils.cli import get_config, create_model, create_model_config\n",
    "from tabularfm.utils.processing import load_model_weights\n",
    "\n",
    "# configs\n",
    "model_type = 'stvae'\n",
    "config_path = 'configs/stvae.yaml'\n",
    "# data_path =  # kaggle path\n",
    "data_path = 'datasets/gittables_v4' # gittables\n",
    "finetune_path = 'models/stvae_gittables/'\n",
    "\n",
    "# load and create config\n",
    "configs = get_config(config_path)\n",
    "model_config_finetune = create_model_config(data_path, configs, model_type, config_type = \"finetune\")\n",
    "\n",
    "# load model\n",
    "stvae_model = create_model(model_type, model_config_finetune)\n",
    "stvae_model = load_model_weights(model_type, stvae_model, finetune_path, suffix=None)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4b9621d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # download downstream datasets\n",
    "# ! mkdir downstream_datasets\n",
    "# ! wget gittables_v4.zip https://huggingface.co/datasets/lamthuy/TabularFM-GitTables/resolve/main/gittables_v4.zip -P downstream_datasets/\n",
    "# ! unzip -o downstream_datasets/gittables_v4.zip -d downstream_datasets/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11869f34",
   "metadata": {},
   "source": [
    "### Transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b2024dcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import pandas as pd\n",
    "from tabularfm.utils.processing import get_df\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tabularfm.utils.processing import get_metadata, add_padding\n",
    "from tabularfm.ctgan.data_transformer import DataTransformerV2\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "def transform(path, input_dim, cat_encoder='label', test_size=0.3, random_state=121):\n",
    "    \n",
    "    df = get_df(path)\n",
    "    \n",
    "    data = df.drop(columns=df.columns[-1])\n",
    "    labels = df[[df.columns[-1]]]\n",
    "\n",
    "    categorical_columns = data.select_dtypes(include=['object', 'category']).columns\n",
    "\n",
    "    ori_data = data\n",
    "\n",
    "    # transform original data\n",
    "    for col in categorical_columns:\n",
    "        ori_data[col] = LabelEncoder().fit_transform(ori_data[col])\n",
    "\n",
    "    transformer = DataTransformerV2()\n",
    "    metadata = get_metadata(path)\n",
    "    \n",
    "    discrete_cols = [k for k,v in metadata['columns'].items() if v['sdtype'] != 'numerical']\n",
    "    \n",
    "    transformer.fit(data, discrete_columns=discrete_cols)\n",
    "\n",
    "    tabfm_data = transformer.transform(data)\n",
    "    \n",
    "    tabfm_train_data, tabfm_val_data, train_labels, val_labels = train_test_split(tabfm_data, labels, test_size=0.3, random_state=121)\n",
    "    train_data, val_data, _, _ = train_test_split(ori_data, labels, test_size=0.3, random_state=121)\n",
    "\n",
    "    tabfm_train_data = add_padding(tabfm_train_data, max_dim=input_dim)\n",
    "    tabfm_val_data = add_padding(tabfm_val_data, max_dim=input_dim)\n",
    "    \n",
    "    return (train_data, val_data), (tabfm_train_data, tabfm_val_data), (train_labels, val_labels), transformer\n",
    "\n",
    "def extract_tvae_based(model, train_data, val_data):\n",
    "    \n",
    "    model.encoder.eval()\n",
    "    train_embedding = model.encoder.seq(torch.from_numpy(train_data).float()).detach().cpu()\n",
    "    val_embedding = model.encoder.seq(torch.from_numpy(val_data).float()).detach().cpu()\n",
    "    \n",
    "    return (train_embedding, val_embedding)\n",
    "\n",
    "def prepare_data(ori_train_data, ori_val_data, train_embedding, val_embedding):\n",
    "    # original data\n",
    "    ori_train_data = torch.from_numpy(ori_train_data.values).float()\n",
    "    ori_val_data = torch.from_numpy(ori_val_data.values).float()\n",
    "\n",
    "    # concat with embedding extracted from preptrained model\n",
    "    concat_train_data = torch.concat([ori_train_data, train_embedding], dim=1).float()\n",
    "    concat_val_data = torch.concat([ori_val_data, val_embedding], dim=1).float()\n",
    "    \n",
    "    return (ori_train_data, ori_val_data), (concat_train_data, concat_val_data)\n",
    "\n",
    "def standard_scale(data):\n",
    "    return (data - data.mean()) / data.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "059da340",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# path to csv dataset folder\n",
    "path = 'downstream_datasets/processed_dataset/-social-networking-ads'\n",
    "\n",
    "# ori_data, tabfm_data, labels, transformer = transform(path, input_dim=stvae_model.input_dim, cat_encoder='label', test_size=0.3, random_state=121)\n",
    "ori_data, tabfm_data, labels, transformer = transform(path, input_dim=stvae_model.input_dim, cat_encoder='label', test_size=0.3, random_state=121)\n",
    "train_embedding, val_embedding = extract_tvae_based(stvae_model, tabfm_data[0], tabfm_data[1])\n",
    "\n",
    "# # standard scaling\n",
    "# ori_train_data = standard_scale(ori_data[0])\n",
    "# ori_val_data = standard_scale(ori_data[1])\n",
    "\n",
    "# (ori_train_data, ori_val_data), (concat_train_data, concat_val_data) = prepare_data(ori_train_data, ori_val_data, train_embedding, val_embedding)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2e6cea7",
   "metadata": {},
   "source": [
    "### Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f0495bd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, f1_score, mean_squared_error, r2_score\n",
    "import lightgbm as lgb\n",
    "import xgboost as xgb\n",
    "\n",
    "def evaluate_cls_lgb(train_data, train_labels, val_data, val_labels):\n",
    "    model = lgb.LGBMClassifier()\n",
    "    \n",
    "    # training\n",
    "    model.fit(train_data, train_labels)\n",
    "    \n",
    "    # predicting\n",
    "    y_pred = model.predict(val_data)\n",
    "    accuracy = accuracy_score(val_labels, y_pred)\n",
    "    f1 = f1_score(val_labels, y_pred, average='weighted')\n",
    "    \n",
    "    return accuracy, f1\n",
    "\n",
    "def evaluate_cls_xgb(train_data, train_labels, val_data, val_labels):\n",
    "    model = xgb.XGBClassifier()\n",
    "    \n",
    "    # training\n",
    "    model.fit(train_data, train_labels)\n",
    "\n",
    "    # Predict and evaluate\n",
    "    y_pred = model.predict(val_data)\n",
    "    accuracy = accuracy_score(val_labels, y_pred)\n",
    "    f1 = f1_score(val_labels, y_pred, average='weighted')\n",
    "    \n",
    "    return accuracy, f1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "447ffb64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Number of positive: 97, number of negative: 183\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000035 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 111\n",
      "[LightGBM] [Info] Number of data points in the train set: 280, number of used features: 3\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.346429 -> initscore=-0.634775\n",
      "[LightGBM] [Info] Start training from score -0.634775\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[LightGBM] [Info] Number of positive: 97, number of negative: 183\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000102 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 3949\n",
      "[LightGBM] [Info] Number of data points in the train set: 280, number of used features: 45\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.346429 -> initscore=-0.634775\n",
      "[LightGBM] [Info] Start training from score -0.634775\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[LightGBM] [Info] Number of positive: 97, number of negative: 183\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000098 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 3838\n",
      "[LightGBM] [Info] Number of data points in the train set: 280, number of used features: 42\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.346429 -> initscore=-0.634775\n",
      "[LightGBM] [Info] Start training from score -0.634775\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "LGB\n",
      "ori results:  0.9083333333333333 0.9076989855395986\n",
      "concat results:  0.85 0.8485645933014354\n",
      "emb results:  0.7333333333333333 0.7333333333333333\n",
      "XGB\n",
      "ori results:  0.9083333333333333 0.9081372765936524\n",
      "concat results:  0.875 0.8747326499004352\n",
      "emb results:  0.7666666666666667 0.7656296296296297\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kdts33/miniconda3/envs/tabsyn_env/lib/python3.9/site-packages/sklearn/preprocessing/_label.py:97: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/home/kdts33/miniconda3/envs/tabsyn_env/lib/python3.9/site-packages/sklearn/preprocessing/_label.py:132: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, dtype=self.classes_.dtype, warn=True)\n",
      "/home/kdts33/miniconda3/envs/tabsyn_env/lib/python3.9/site-packages/sklearn/preprocessing/_label.py:97: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/home/kdts33/miniconda3/envs/tabsyn_env/lib/python3.9/site-packages/sklearn/preprocessing/_label.py:132: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, dtype=self.classes_.dtype, warn=True)\n",
      "/home/kdts33/miniconda3/envs/tabsyn_env/lib/python3.9/site-packages/sklearn/preprocessing/_label.py:97: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/home/kdts33/miniconda3/envs/tabsyn_env/lib/python3.9/site-packages/sklearn/preprocessing/_label.py:132: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, dtype=self.classes_.dtype, warn=True)\n"
     ]
    }
   ],
   "source": [
    "train_labels, val_labels = labels[0], labels[1]\n",
    "\n",
    "ori_acc, ori_f1 = evaluate_cls_lgb(ori_train_data, train_labels, ori_val_data, val_labels)\n",
    "concat_acc, concat_f1 = evaluate_cls_lgb(concat_train_data, train_labels, concat_val_data, val_labels)\n",
    "emb_acc, emb_f1 = evaluate_cls_lgb(train_embedding, train_labels, val_embedding, val_labels)\n",
    "\n",
    "print('LGB')\n",
    "print('ori results: ', ori_acc, ori_f1)\n",
    "print('concat results: ', concat_acc, concat_f1)\n",
    "print('emb results: ', emb_acc, emb_f1)\n",
    "\n",
    "\n",
    "ori_acc, ori_f1 = evaluate_cls_xgb(ori_train_data, train_labels, ori_val_data, val_labels)\n",
    "concat_acc, concat_f1 = evaluate_cls_xgb(concat_train_data, train_labels, concat_val_data, val_labels)\n",
    "emb_acc, emb_f1 = evaluate_cls_xgb(train_embedding, train_labels, val_embedding, val_labels)\n",
    "\n",
    "print('XGB')\n",
    "print('ori results: ', ori_acc, ori_f1)\n",
    "print('concat results: ', concat_acc, concat_f1)\n",
    "print('emb results: ', emb_acc, emb_f1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca17e5ca",
   "metadata": {},
   "source": [
    "### Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3a4ff07d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import lightgbm as lgb\n",
    "import xgboost as xgb\n",
    "\n",
    "def evaluate_reg_lgb(train_data, train_labels, val_data, val_labels):\n",
    "    model = lgb.LGBMRegressor()\n",
    "    \n",
    "    # training\n",
    "    model.fit(train_data, train_labels)\n",
    "    \n",
    "    # predicting\n",
    "    y_pred = model.predict(val_data)\n",
    "    mse = mean_squared_error(val_labels, y_pred)\n",
    "    r2 = r2_score(val_labels, y_pred)\n",
    "    \n",
    "    return mse, r2\n",
    "\n",
    "def evaluate_reg_xgb(train_data, train_labels, val_data, val_labels):\n",
    "    model = xgb.XGBRegressor()\n",
    "    \n",
    "    # training\n",
    "    model.fit(train_data, train_labels)\n",
    "\n",
    "    # Predict and evaluate\n",
    "    y_pred = model.predict(val_data)\n",
    "    mse = mean_squared_error(val_labels, y_pred)\n",
    "    r2 = r2_score(val_labels, y_pred)\n",
    "    \n",
    "    return mse, r2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6ad642ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kdts33/miniconda3/envs/tabsyn_env/lib/python3.9/site-packages/sklearn/utils/validation.py:1300: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/home/kdts33/miniconda3/envs/tabsyn_env/lib/python3.9/site-packages/sklearn/utils/validation.py:1300: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/home/kdts33/miniconda3/envs/tabsyn_env/lib/python3.9/site-packages/sklearn/utils/validation.py:1300: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000076 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 110\n",
      "[LightGBM] [Info] Number of data points in the train set: 280, number of used features: 3\n",
      "[LightGBM] [Info] Start training from score 0.346429\n",
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      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 12182\n",
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      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 12072\n",
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      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "LGB\n",
      "ori results:  0.06888129332083771 0.7086102750234833\n",
      "concat results:  0.08328512983618046 0.6476774766037019\n",
      "emb results:  0.2891519151127278 -0.2232043412524325\n",
      "XGB\n",
      "ori results:  0.08103944713113251 0.6571774269423302\n",
      "concat results:  0.10476093394451629 0.5568280115155598\n",
      "emb results:  0.26891258570845233 -0.13758555646348802\n"
     ]
    }
   ],
   "source": [
    "train_labels, val_labels = labels[0], labels[1]\n",
    "\n",
    "ori_mse, ori_r2 = evaluate_reg_lgb(ori_train_data, train_labels, ori_val_data, val_labels)\n",
    "concat_mse, concat_r2 = evaluate_reg_lgb(concat_train_data, train_labels, concat_val_data, val_labels)\n",
    "emb_mse, emb_r2 = evaluate_reg_lgb(train_embedding, train_labels, val_embedding, val_labels)\n",
    "\n",
    "print('LGB')\n",
    "print('ori results: ', ori_mse, ori_r2)\n",
    "print('concat results: ', concat_mse, concat_r2)\n",
    "print('emb results: ', emb_mse, emb_r2)\n",
    "\n",
    "ori_mse, ori_r2 = evaluate_reg_xgb(ori_train_data, train_labels, ori_val_data, val_labels)\n",
    "concat_mse, concat_r2 = evaluate_reg_xgb(concat_train_data, train_labels, concat_val_data, val_labels)\n",
    "emb_mse, emb_r2 = evaluate_reg_xgb(train_embedding, train_labels, val_embedding, val_labels)\n",
    "\n",
    "print('XGB')\n",
    "print('ori results: ', ori_mse, ori_r2)\n",
    "print('concat results: ', concat_mse, concat_r2)\n",
    "print('emb results: ', emb_mse, emb_r2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f7eaf5e-72bf-47db-a2b8-75d7fccdeb1c",
   "metadata": {},
   "source": [
    "### Regression or Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "183f24fc-a0ae-458d-9033-670c66dc14a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, f1_score, mean_squared_error, r2_score\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import xgboost as xgb\n",
    "import lightgbm as lgb\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0b0f0fa-6722-4ce1-85ef-31d14ae7a928",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load your data\n",
    "data = pd.read_csv('data.csv')\n",
    "\n",
    "# Assuming the last column is the target variable\n",
    "target_column = data.columns[-1]\n",
    "\n",
    "# Separate features and target\n",
    "X = data.drop(columns=[target_column])\n",
    "y = data[target_column]\n",
    "\n",
    "# Identify categorical columns\n",
    "categorical_columns = X.select_dtypes(include=['object', 'category']).columns\n",
    "\n",
    "# Handle categorical columns differently for XGBoost and LightGBM\n",
    "# Encode categorical columns using Label Encoding for XGBoost\n",
    "for col in categorical_columns:\n",
    "    X[col] = LabelEncoder().fit_transform(X[col])\n",
    "\n",
    "# Split the data into train/test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf373e69-b086-4e55-91ec-8159f3326715",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Determine the problem type: classification or regression\n",
    "if y.nunique() > 20 and y.dtype in [np.float64, np.float32, np.int64, np.int32]:\n",
    "    problem_type = 'regression'\n",
    "else:\n",
    "    problem_type = 'classification'\n",
    "\n",
    "# Train a model based on the problem type\n",
    "if problem_type == 'classification':\n",
    "    # Use LightGBM for classification, directly handling categorical features\n",
    "    model = lgb.LGBMClassifier()\n",
    "    \n",
    "    # For LightGBM, specify the categorical features\n",
    "    model.fit(X_train, y_train, categorical_feature=categorical_columns.tolist())\n",
    "    \n",
    "    # Predict and evaluate\n",
    "    y_pred = model.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    f1 = f1_score(y_test, y_pred, average='weighted')\n",
    "    \n",
    "    print(f'Classification Accuracy: {accuracy:.4f}')\n",
    "    print(f'Classification F1 Score: {f1:.4f}')\n",
    "\n",
    "else:\n",
    "    # Use XGBoost for regression, where categorical features are label encoded\n",
    "    model = xgb.XGBRegressor()\n",
    "    model.fit(X_train, y_train)\n",
    "    \n",
    "    # Predict and evaluate\n",
    "    y_pred = model.predict(X_test)\n",
    "    mse = mean_squared_error(y_test, y_pred)\n",
    "    r2 = r2_score(y_test, y_pred)\n",
    "    \n",
    "    print(f'Regression Mean Squared Error: {mse:.4f}')\n",
    "    print(f'Regression R^2 Score: {r2:.4f}')"
   ]
  }
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