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{
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      "Connecting to zenodo.org (zenodo.org)|137.138.76.77|:443... connected.\n",
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      "\n",
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      "2022-04-16 11:57:59 (50.8 MB/s) - ‘train.h5’ saved [1038496555/1038496555]\n",
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      "--2022-04-16 11:58:03--  https://zenodo.org/record/2603256/files/val.h5\n",
      "Resolving zenodo.org (zenodo.org)... 137.138.76.77\n",
      "Connecting to zenodo.org (zenodo.org)|137.138.76.77|:443... connected.\n",
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      "Length: 347378076 (331M) [application/octet-stream]\n",
      "Saving to: ‘validation.h5’\n",
      "\n",
      "validation.h5       100%[===================>] 331.29M  41.8MB/s    in 9.5s    \n",
      "\n",
      "2022-04-16 11:58:13 (34.7 MB/s) - ‘validation.h5’ saved [347378076/347378076]\n",
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      "--2022-04-16 11:58:15--  https://zenodo.org/record/2603256/files/test.h5\n",
      "Resolving zenodo.org (zenodo.org)... 137.138.76.77\n",
      "Connecting to zenodo.org (zenodo.org)|137.138.76.77|:443... connected.\n",
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      "Length: 347849376 (332M) [application/octet-stream]\n",
      "Saving to: ‘test.h5’\n",
      "\n",
      "test.h5             100%[===================>] 331.73M  14.9MB/s    in 21s     \n",
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      "2022-04-16 11:58:37 (15.9 MB/s) - ‘test.h5’ saved [347849376/347849376]\n",
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    "!wget -O test.h5 https://zenodo.org/record/2603256/files/test.h5"
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  {
   "cell_type": "markdown",
   "id": "58c66318-07f9-442e-bc8a-3496fe20ab5a",
   "metadata": {},
   "source": [
    "## Convert to parquet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1a06b45e-a7b8-4bd0-859a-46de3cf90081",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_to_parquet(split):\n",
    "    df = pd.read_hdf(f\"{split}.h5\", key=\"table\")\n",
    "    df.to_parquet(f\"{split}.parquet\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3e1a0cdc-436f-495a-96e7-ae1eda7ceaae",
   "metadata": {},
   "outputs": [],
   "source": [
    "for split in [\"train\", \"validation\", \"test\"]:\n",
    "    convert_to_parquet(split)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48696caa-7f47-43ef-8056-9ff45ec9c8d2",
   "metadata": {
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   "source": [
    "## Check loading script"
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   "execution_count": 12,
   "id": "1e4e06f8-c5cf-49e1-b932-833e3dd85b03",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using custom data configuration dl4phys--top_quark_tagging-d8129c455ccac2e9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset parquet/dl4phys--top_quark_tagging to /Users/lewtun/.cache/huggingface/datasets/parquet/dl4phys--top_quark_tagging-d8129c455ccac2e9/0.0.0/0b6d5799bb726b24ad7fc7be720c170d8e497f575d02d47537de9a5bac074901...\n"
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       "Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]"
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       "Downloading data:   0%|          | 0.00/1.28G [00:00<?, ?B/s]"
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       "model_id": "fa9e68e401b840f19849c274f9b3e563",
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       "Downloading data:   0%|          | 0.00/509M [00:00<?, ?B/s]"
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       "model_id": "22e5f87e05b240308d6badc7f66ea62f",
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      "text/plain": [
       "Downloading data:   0%|          | 0.00/508M [00:00<?, ?B/s]"
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       "model_id": "bb0e92a6386846eda5af7c056fe74dd4",
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      "text/plain": [
       "Extracting data files:   0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset parquet downloaded and prepared to /Users/lewtun/.cache/huggingface/datasets/parquet/dl4phys--top_quark_tagging-d8129c455ccac2e9/0.0.0/0b6d5799bb726b24ad7fc7be720c170d8e497f575d02d47537de9a5bac074901. Subsequent calls will reuse this data.\n"
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      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['E_0', 'PX_0', 'PY_0', 'PZ_0', 'E_1', 'PX_1', 'PY_1', 'PZ_1', 'E_2', 'PX_2', 'PY_2', 'PZ_2', 'E_3', 'PX_3', 'PY_3', 'PZ_3', 'E_4', 'PX_4', 'PY_4', 'PZ_4', 'E_5', 'PX_5', 'PY_5', 'PZ_5', 'E_6', 'PX_6', 'PY_6', 'PZ_6', 'E_7', 'PX_7', 'PY_7', 'PZ_7', 'E_8', 'PX_8', 'PY_8', 'PZ_8', 'E_9', 'PX_9', 'PY_9', 'PZ_9', 'E_10', 'PX_10', 'PY_10', 'PZ_10', 'E_11', 'PX_11', 'PY_11', 'PZ_11', 'E_12', 'PX_12', 'PY_12', 'PZ_12', 'E_13', 'PX_13', 'PY_13', 'PZ_13', 'E_14', 'PX_14', 'PY_14', 'PZ_14', 'E_15', 'PX_15', 'PY_15', 'PZ_15', 'E_16', 'PX_16', 'PY_16', 'PZ_16', 'E_17', 'PX_17', 'PY_17', 'PZ_17', 'E_18', 'PX_18', 'PY_18', 'PZ_18', 'E_19', 'PX_19', 'PY_19', 'PZ_19', 'E_20', 'PX_20', 'PY_20', 'PZ_20', 'E_21', 'PX_21', 'PY_21', 'PZ_21', 'E_22', 'PX_22', 'PY_22', 'PZ_22', 'E_23', 'PX_23', 'PY_23', 'PZ_23', 'E_24', 'PX_24', 'PY_24', 'PZ_24', 'E_25', 'PX_25', 'PY_25', 'PZ_25', 'E_26', 'PX_26', 'PY_26', 'PZ_26', 'E_27', 'PX_27', 'PY_27', 'PZ_27', 'E_28', 'PX_28', 'PY_28', 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       "        num_rows: 1211000\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['E_0', 'PX_0', 'PY_0', 'PZ_0', 'E_1', 'PX_1', 'PY_1', 'PZ_1', 'E_2', 'PX_2', 'PY_2', 'PZ_2', 'E_3', 'PX_3', 'PY_3', 'PZ_3', 'E_4', 'PX_4', 'PY_4', 'PZ_4', 'E_5', 'PX_5', 'PY_5', 'PZ_5', 'E_6', 'PX_6', 'PY_6', 'PZ_6', 'E_7', 'PX_7', 'PY_7', 'PZ_7', 'E_8', 'PX_8', 'PY_8', 'PZ_8', 'E_9', 'PX_9', 'PY_9', 'PZ_9', 'E_10', 'PX_10', 'PY_10', 'PZ_10', 'E_11', 'PX_11', 'PY_11', 'PZ_11', 'E_12', 'PX_12', 'PY_12', 'PZ_12', 'E_13', 'PX_13', 'PY_13', 'PZ_13', 'E_14', 'PX_14', 'PY_14', 'PZ_14', 'E_15', 'PX_15', 'PY_15', 'PZ_15', 'E_16', 'PX_16', 'PY_16', 'PZ_16', 'E_17', 'PX_17', 'PY_17', 'PZ_17', 'E_18', 'PX_18', 'PY_18', 'PZ_18', 'E_19', 'PX_19', 'PY_19', 'PZ_19', 'E_20', 'PX_20', 'PY_20', 'PZ_20', 'E_21', 'PX_21', 'PY_21', 'PZ_21', 'E_22', 'PX_22', 'PY_22', 'PZ_22', 'E_23', 'PX_23', 'PY_23', 'PZ_23', 'E_24', 'PX_24', 'PY_24', 'PZ_24', 'E_25', 'PX_25', 'PY_25', 'PZ_25', 'E_26', 'PX_26', 'PY_26', 'PZ_26', 'E_27', 'PX_27', 'PY_27', 'PZ_27', 'E_28', 'PX_28', 'PY_28', 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       "        num_rows: 404000\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['E_0', 'PX_0', 'PY_0', 'PZ_0', 'E_1', 'PX_1', 'PY_1', 'PZ_1', 'E_2', 'PX_2', 'PY_2', 'PZ_2', 'E_3', 'PX_3', 'PY_3', 'PZ_3', 'E_4', 'PX_4', 'PY_4', 'PZ_4', 'E_5', 'PX_5', 'PY_5', 'PZ_5', 'E_6', 'PX_6', 'PY_6', 'PZ_6', 'E_7', 'PX_7', 'PY_7', 'PZ_7', 'E_8', 'PX_8', 'PY_8', 'PZ_8', 'E_9', 'PX_9', 'PY_9', 'PZ_9', 'E_10', 'PX_10', 'PY_10', 'PZ_10', 'E_11', 'PX_11', 'PY_11', 'PZ_11', 'E_12', 'PX_12', 'PY_12', 'PZ_12', 'E_13', 'PX_13', 'PY_13', 'PZ_13', 'E_14', 'PX_14', 'PY_14', 'PZ_14', 'E_15', 'PX_15', 'PY_15', 'PZ_15', 'E_16', 'PX_16', 'PY_16', 'PZ_16', 'E_17', 'PX_17', 'PY_17', 'PZ_17', 'E_18', 'PX_18', 'PY_18', 'PZ_18', 'E_19', 'PX_19', 'PY_19', 'PZ_19', 'E_20', 'PX_20', 'PY_20', 'PZ_20', 'E_21', 'PX_21', 'PY_21', 'PZ_21', 'E_22', 'PX_22', 'PY_22', 'PZ_22', 'E_23', 'PX_23', 'PY_23', 'PZ_23', 'E_24', 'PX_24', 'PY_24', 'PZ_24', 'E_25', 'PX_25', 'PY_25', 'PZ_25', 'E_26', 'PX_26', 'PY_26', 'PZ_26', 'E_27', 'PX_27', 'PY_27', 'PZ_27', 'E_28', 'PX_28', 'PY_28', 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       "        num_rows: 403000\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = load_dataset(\"dl4phys/top_quark_tagging\")\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5cff9490-ed63-4590-8e2e-cd9cfe6f02c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'E_0': 474.0711364746094,\n",
       " 'PX_0': -250.34703063964844,\n",
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       " 'PZ_5': -14.228776931762695,\n",
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