“MetabolomicsInference_ipynb”的副本.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/kevinluolk/Blogdown/blob/master/%E2%80%9CMetabolomicsInference_ipynb%E2%80%9D%E7%9A%84%E5%89%AF%E6%9C%AC.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "# Inference on the Metabolomic State Model\n",
        "This notebook allows you to run inference on your own data using the Metabolomic State Model (MSM). Please note: The data must be NMR Metabolomics data from [Nightingale Health](https://nightingalehealth.com). Data must be provided as `.csv` file with one sample per row and one metabolite measure per column. The order of the metabolite columns should follow the [example data](https://github.com/thbuerg/MetabolomicsCommonDiseases/blob/main/analysis/examples/sample.csv).\n",
        "\n",
        "To run inference on your data, execute each cell of this notebook, and upload your data as `.csv` file where prompted.\n",
        "\n",
        "Happy inference!"
      ],
      "metadata": {
        "id": "gnuNUSt9mKBg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Install dependencies and download model weights from zenodo\n",
        "\n",
        "#@markdown Please execute this cell by pressing the _Play_ button\n",
        "#@markdown on the left to install dependencies and download model weights\n",
        "#@markdown in this Colab notebook.\n",
        "\n",
        "import os\n",
        "import tqdm.notebook\n",
        "from IPython.utils import io\n",
        "\n",
        "model_files = [f\"model_{i}.onnx\" for i in range(22)]\n",
        "download_dir = 'downloads/'\n",
        "os.makedirs(download_dir, exist_ok=True)\n",
        "\n",
        "TQDM_BAR_FORMAT =\\\n",
        "'{l_bar}{bar}| {n_fmt}/{total_fmt} [elapsed: {elapsed} remaining: {remaining}]'\n",
        "\n",
        "\n",
        "with tqdm.notebook.tqdm(total=100, bar_format=TQDM_BAR_FORMAT) as pbar:\n",
        "    with io.capture_output() as captured:\n",
        "\n",
        "      %shell pip install \\\n",
        "      onnx onnxruntime pickle5 numpy matplotlib wget scikit-learn==0.24.2\n",
        "\n",
        "      pbar.update(25)\n",
        "\n",
        "      import wget\n",
        "\n",
        "      for f in [\"metabolites_metadata.csv\", \"scaler_dict.p\"]:\n",
        "        if not os.path.isfile(f):\n",
        "          wget.download(\n",
        "              f\"https://zenodo.org/record/6200202/files/{f}?download=1\",\n",
        "              out=os.path.join(download_dir, f))\n",
        "\n",
        "      pbar.update(25)\n",
        "\n",
        "      for model_file in model_files:\n",
        "        if not os.path.isfile(model_file):\n",
        "          wget.download(\n",
        "              \"https://zenodo.org/record/6200202/files/\"\\\n",
        "              +model_file+\"?download=1\",\n",
        "               out=os.path.join(download_dir, model_file))\n",
        "        pbar.update(50//len(model_files))\n",
        "    pbar.update(6)"
      ],
      "metadata": {
        "colab": {
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          "height": 49,
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        "id": "X72PCwYalo3O",
        "outputId": "b24fbd28-d2b0-4eb4-e150-37fd657c874e"
      },
      "execution_count": null,
      "outputs": [
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    {
      "cell_type": "code",
      "source": [
        "#@title Define necessary functions\n",
        "\n",
        "#@markdown Please execute this cell by pressing the _Play_ button\n",
        "#@markdown on the left to install dependencies and download model weights\n",
        "#@markdown in this Colab notebook.\n",
        "\n",
        "import pickle5 as pickle\n",
        "import onnxruntime\n",
        "import pandas as pd\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "\n",
        "endpoints = ['M_MACE',\n",
        " 'M_all_cause_dementia',\n",
        " 'M_type_2_diabetes',\n",
        " 'M_liver_disease',\n",
        " 'M_renal_disease',\n",
        " 'M_atrial_fibrillation',\n",
        " 'M_heart_failure',\n",
        " 'M_coronary_heart_disease',\n",
        " 'M_venous_thrombosis',\n",
        " 'M_cerebral_stroke',\n",
        " 'M_abdominal_aortic_aneurysm',\n",
        " 'M_peripheral_arterial_disease',\n",
        " 'M_asthma',\n",
        " 'M_chronic_obstructuve_pulmonary_disease',\n",
        " 'M_lung_cancer',\n",
        " 'M_non_melanoma_skin_cancer',\n",
        " 'M_colon_cancer',\n",
        " 'M_rectal_cancer',\n",
        " 'M_prostate_cancer',\n",
        " 'M_breast_cancer',\n",
        " 'M_parkinsons_disease',\n",
        " 'M_fractures',\n",
        " 'M_cataracts',\n",
        " 'M_glaucoma']\n",
        "\n",
        "metabolite_labels = [\n",
        "                     'NMR_3hydroxybutyrate',\n",
        " 'NMR_acetate',\n",
        " 'NMR_acetoacetate',\n",
        " 'NMR_acetone',\n",
        " 'NMR_alanine',\n",
        " 'NMR_albumin',\n",
        " 'NMR_apolipoprotein_a1',\n",
        " 'NMR_apolipoprotein_b',\n",
        " 'NMR_average_diameter_for_hdl_particles',\n",
        " 'NMR_average_diameter_for_ldl_particles',\n",
        " 'NMR_average_diameter_for_vldl_particles',\n",
        " 'NMR_cholesterol_in_chylomicrons_and_extremely_large_vldl',\n",
        " 'NMR_cholesterol_in_idl',\n",
        " 'NMR_cholesterol_in_large_hdl',\n",
        " 'NMR_cholesterol_in_large_ldl',\n",
        " 'NMR_cholesterol_in_large_vldl',\n",
        " 'NMR_cholesterol_in_medium_hdl',\n",
        " 'NMR_cholesterol_in_medium_ldl',\n",
        " 'NMR_cholesterol_in_medium_vldl',\n",
        " 'NMR_cholesterol_in_small_hdl',\n",
        " 'NMR_cholesterol_in_small_ldl',\n",
        " 'NMR_cholesterol_in_small_vldl',\n",
        " 'NMR_cholesterol_in_very_large_hdl',\n",
        " 'NMR_cholesterol_in_very_large_vldl',\n",
        " 'NMR_cholesterol_in_very_small_vldl',\n",
        " 'NMR_cholesteryl_esters_in_chylomicrons_and_extremely_large_vldl',\n",
        " 'NMR_cholesteryl_esters_in_hdl',\n",
        " 'NMR_cholesteryl_esters_in_idl',\n",
        " 'NMR_cholesteryl_esters_in_ldl',\n",
        " 'NMR_cholesteryl_esters_in_large_hdl',\n",
        " 'NMR_cholesteryl_esters_in_large_ldl',\n",
        " 'NMR_cholesteryl_esters_in_large_vldl',\n",
        " 'NMR_cholesteryl_esters_in_medium_hdl',\n",
        " 'NMR_cholesteryl_esters_in_medium_ldl',\n",
        " 'NMR_cholesteryl_esters_in_medium_vldl',\n",
        " 'NMR_cholesteryl_esters_in_small_hdl',\n",
        " 'NMR_cholesteryl_esters_in_small_ldl',\n",
        " 'NMR_cholesteryl_esters_in_small_vldl',\n",
        " 'NMR_cholesteryl_esters_in_vldl',\n",
        " 'NMR_cholesteryl_esters_in_very_large_hdl',\n",
        " 'NMR_cholesteryl_esters_in_very_large_vldl',\n",
        " 'NMR_cholesteryl_esters_in_very_small_vldl',\n",
        " 'NMR_citrate',\n",
        " 'NMR_clinical_ldl_cholesterol',\n",
        " 'NMR_concentration_of_chylomicrons_and_extremely_large_vldl_particles',\n",
        " 'NMR_concentration_of_hdl_particles',\n",
        " 'NMR_concentration_of_idl_particles',\n",
        " 'NMR_concentration_of_ldl_particles',\n",
        " 'NMR_concentration_of_large_hdl_particles',\n",
        " 'NMR_concentration_of_large_ldl_particles',\n",
        " 'NMR_concentration_of_large_vldl_particles',\n",
        " 'NMR_concentration_of_medium_hdl_particles',\n",
        " 'NMR_concentration_of_medium_ldl_particles',\n",
        " 'NMR_concentration_of_medium_vldl_particles',\n",
        " 'NMR_concentration_of_small_hdl_particles',\n",
        " 'NMR_concentration_of_small_ldl_particles',\n",
        " 'NMR_concentration_of_small_vldl_particles',\n",
        " 'NMR_concentration_of_vldl_particles',\n",
        " 'NMR_concentration_of_very_large_hdl_particles',\n",
        " 'NMR_concentration_of_very_large_vldl_particles',\n",
        " 'NMR_concentration_of_very_small_vldl_particles',\n",
        " 'NMR_creatinine',\n",
        " 'NMR_degree_of_unsaturation',\n",
        " 'NMR_docosahexaenoic_acid',\n",
        " 'NMR_free_cholesterol_in_chylomicrons_and_extremely_large_vldl',\n",
        " 'NMR_free_cholesterol_in_hdl',\n",
        " 'NMR_free_cholesterol_in_idl',\n",
        " 'NMR_free_cholesterol_in_ldl',\n",
        " 'NMR_free_cholesterol_in_large_hdl',\n",
        " 'NMR_free_cholesterol_in_large_ldl',\n",
        " 'NMR_free_cholesterol_in_large_vldl',\n",
        " 'NMR_free_cholesterol_in_medium_hdl',\n",
        " 'NMR_free_cholesterol_in_medium_ldl',\n",
        " 'NMR_free_cholesterol_in_medium_vldl',\n",
        " 'NMR_free_cholesterol_in_small_hdl',\n",
        " 'NMR_free_cholesterol_in_small_ldl',\n",
        " 'NMR_free_cholesterol_in_small_vldl',\n",
        " 'NMR_free_cholesterol_in_vldl',\n",
        " 'NMR_free_cholesterol_in_very_large_hdl',\n",
        " 'NMR_free_cholesterol_in_very_large_vldl',\n",
        " 'NMR_free_cholesterol_in_very_small_vldl',\n",
        " 'NMR_glucose',\n",
        " 'NMR_glutamine',\n",
        " 'NMR_glycine',\n",
        " 'NMR_glycoprotein_acetyls',\n",
        " 'NMR_hdl_cholesterol',\n",
        " 'NMR_histidine',\n",
        " 'NMR_isoleucine',\n",
        " 'NMR_ldl_cholesterol',\n",
        " 'NMR_lactate',\n",
        " 'NMR_leucine',\n",
        " 'NMR_linoleic_acid',\n",
        " 'NMR_monounsaturated_fatty_acids',\n",
        " 'NMR_omega3_fatty_acids',\n",
        " 'NMR_omega6_fatty_acids',\n",
        " 'NMR_phenylalanine',\n",
        " 'NMR_phosphatidylcholines',\n",
        " 'NMR_phosphoglycerides',\n",
        " 'NMR_phospholipids_in_chylomicrons_and_extremely_large_vldl',\n",
        " 'NMR_phospholipids_in_hdl',\n",
        " 'NMR_phospholipids_in_idl',\n",
        " 'NMR_phospholipids_in_ldl',\n",
        " 'NMR_phospholipids_in_large_hdl',\n",
        " 'NMR_phospholipids_in_large_ldl',\n",
        " 'NMR_phospholipids_in_large_vldl',\n",
        " 'NMR_phospholipids_in_medium_hdl',\n",
        " 'NMR_phospholipids_in_medium_ldl',\n",
        " 'NMR_phospholipids_in_medium_vldl',\n",
        " 'NMR_phospholipids_in_small_hdl',\n",
        " 'NMR_phospholipids_in_small_ldl',\n",
        " 'NMR_phospholipids_in_small_vldl',\n",
        " 'NMR_phospholipids_in_vldl',\n",
        " 'NMR_phospholipids_in_very_large_hdl',\n",
        " 'NMR_phospholipids_in_very_large_vldl',\n",
        " 'NMR_phospholipids_in_very_small_vldl',\n",
        " 'NMR_polyunsaturated_fatty_acids',\n",
        " 'NMR_pyruvate',\n",
        " 'NMR_remnant_cholesterol_nonhdl_nonldl_cholesterol',\n",
        " 'NMR_saturated_fatty_acids',\n",
        " 'NMR_sphingomyelins',\n",
        " 'NMR_total_cholesterol',\n",
        " 'NMR_total_cholesterol_minus_hdlc',\n",
        " 'NMR_total_cholines',\n",
        " 'NMR_total_concentration_of_branchedchain_amino_acids_leucine_isoleucine_valine',\n",
        " 'NMR_total_concentration_of_lipoprotein_particles',\n",
        " 'NMR_total_esterified_cholesterol',\n",
        " 'NMR_total_fatty_acids',\n",
        " 'NMR_total_free_cholesterol',\n",
        " 'NMR_total_lipids_in_chylomicrons_and_extremely_large_vldl',\n",
        " 'NMR_total_lipids_in_hdl',\n",
        " 'NMR_total_lipids_in_idl',\n",
        " 'NMR_total_lipids_in_ldl',\n",
        " 'NMR_total_lipids_in_large_hdl',\n",
        " 'NMR_total_lipids_in_large_ldl',\n",
        " 'NMR_total_lipids_in_large_vldl',\n",
        " 'NMR_total_lipids_in_lipoprotein_particles',\n",
        " 'NMR_total_lipids_in_medium_hdl',\n",
        " 'NMR_total_lipids_in_medium_ldl',\n",
        " 'NMR_total_lipids_in_medium_vldl',\n",
        " 'NMR_total_lipids_in_small_hdl',\n",
        " 'NMR_total_lipids_in_small_ldl',\n",
        " 'NMR_total_lipids_in_small_vldl',\n",
        " 'NMR_total_lipids_in_vldl',\n",
        " 'NMR_total_lipids_in_very_large_hdl',\n",
        " 'NMR_total_lipids_in_very_large_vldl',\n",
        " 'NMR_total_lipids_in_very_small_vldl',\n",
        " 'NMR_total_phospholipids_in_lipoprotein_particles',\n",
        " 'NMR_total_triglycerides',\n",
        " 'NMR_triglycerides_in_chylomicrons_and_extremely_large_vldl',\n",
        " 'NMR_triglycerides_in_hdl',\n",
        " 'NMR_triglycerides_in_idl',\n",
        " 'NMR_triglycerides_in_ldl',\n",
        " 'NMR_triglycerides_in_large_hdl',\n",
        " 'NMR_triglycerides_in_large_ldl',\n",
        " 'NMR_triglycerides_in_large_vldl',\n",
        " 'NMR_triglycerides_in_medium_hdl',\n",
        " 'NMR_triglycerides_in_medium_ldl',\n",
        " 'NMR_triglycerides_in_medium_vldl',\n",
        " 'NMR_triglycerides_in_small_hdl',\n",
        " 'NMR_triglycerides_in_small_ldl',\n",
        " 'NMR_triglycerides_in_small_vldl',\n",
        " 'NMR_triglycerides_in_vldl',\n",
        " 'NMR_triglycerides_in_very_large_hdl',\n",
        " 'NMR_triglycerides_in_very_large_vldl',\n",
        " 'NMR_triglycerides_in_very_small_vldl',\n",
        " 'NMR_tyrosine',\n",
        " 'NMR_vldl_cholesterol',\n",
        " 'NMR_valine']\n",
        "\n",
        "# load metabolite metadata and get machine readable format\n",
        "metadata = pd.read_csv(\n",
        "    os.path.join(download_dir, \"metabolites_metadata.csv\")).assign(\n",
        "    metabolite_label=lambda x: \"NMR_\"+x.metabolite).sort_values(\n",
        "      \"metabolite_label\")\n",
        "\n",
        "metabolite_labels_mr = metadata.set_index(\n",
        "    \"metabolite_label\").loc[metabolite_labels].machine_readable_name.to_list()\n",
        "\n",
        "\n",
        "results_dir = os.makedirs('results/', exist_ok=True)\n",
        "\n",
        "# load scalers\n",
        "scaler_dict = pickle.load(\n",
        "    open(os.path.join(download_dir, \"scaler_dict.p\"), \"rb\"))\n",
        "\n",
        "# define forward pass over ensemble\n",
        "def inference(x):\n",
        "  predictions = []\n",
        "  x = np.log1p(x)\n",
        "  for i, m in enumerate(model_files):\n",
        "    m = os.path.join(download_dir, m)\n",
        "\n",
        "    # transform inputs:\n",
        "    scaled = scaler_dict[i].transform(x)\n",
        "\n",
        "    # run onnx inference\n",
        "    ort_session = onnxruntime.InferenceSession(m)\n",
        "    ort_inputs = {ort_session.get_inputs()[0].name: scaled}\n",
        "    ort_outs = ort_session.run(None, ort_inputs)\n",
        "    predictions.append(pd.DataFrame(np.concatenate(ort_outs[0::3], axis=1),\n",
        "                                    columns=endpoints)\\\n",
        "                      .apply(np.exp)\\\n",
        "                      .assign(model_id=i)\n",
        "                      )\n",
        "  predictions = pd.concat(predictions, axis=0)\n",
        "  return predictions"
      ],
      "metadata": {
        "id": "SfL-EPPEzeoq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Upload data for inference\n",
        "\n",
        "#@markdown Please execute this cell by pressing the _Play_ button\n",
        "#@markdown on the left to install dependencies and download model weights\n",
        "#@markdown in this Colab notebook.\n",
        "\n",
        "#@markdown **Note**: The data must be in [the supported format](https://github.com/thbuerg/MetabolomicsCommonDiseases/blob/main/analysis/examples/sample.csv).\n",
        "from google.colab import files\n",
        "\n",
        "input_files = [*files.upload().keys()]\n",
        "input_raw_list = [pd.read_csv(f, engine='python', on_bad_lines='skip', index_col=0) for f in input_files]\n",
        "\n",
        "for df in input_raw_list:\n",
        "  assert df.shape[1] == len(metabolite_labels_mr)\n",
        "\n",
        "input_raw = pd.concat(input_raw_list, axis=0)\n",
        "\n",
        "print(input_raw.head())"
      ],
      "metadata": {
        "id": "oppKKFjgUsa2",
        "outputId": "02065d60-0d4f-40b1-f525-a15bef0168ba",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-1479dc57-40fd-4549-995d-1bedb22e5152\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-1479dc57-40fd-4549-995d-1bedb22e5152\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving sample_clean2.csv to sample_clean2 (3).csv\n",
            "          NMR_3hydroxybutyrate  NMR_acetate  NMR_acetoacetate  NMR_acetone  \\\n",
            "sample_0              1.726763     0.509777          0.297323     0.468845   \n",
            "sample_1              1.124777     1.450595          1.536145     1.490444   \n",
            "sample_2              0.639457     0.048152          0.004128     1.301945   \n",
            "sample_3              0.448759     0.370389          0.069251     1.090154   \n",
            "sample_4              0.391587     1.429042          1.040904     0.321715   \n",
            "\n",
            "          NMR_alanine  NMR_albumin  NMR_apolipoprotein_a1  \\\n",
            "sample_0     1.491899     0.653393               0.182855   \n",
            "sample_1     1.567057     1.286438               0.077527   \n",
            "sample_2     1.516386     1.761520               1.399688   \n",
            "sample_3     1.042064     0.326632               1.401234   \n",
            "sample_4     1.690737     1.618873               0.290064   \n",
            "\n",
            "          NMR_apolipoprotein_b  NMR_average_diameter_for_hdl_particles  \\\n",
            "sample_0              1.186484                                0.646132   \n",
            "sample_1              0.481824                                1.434727   \n",
            "sample_2              0.884731                                0.027976   \n",
            "sample_3              0.492637                                1.687193   \n",
            "sample_4              1.010535                                0.614060   \n",
            "\n",
            "          NMR_average_diameter_for_ldl_particles  ...  \\\n",
            "sample_0                                0.991588  ...   \n",
            "sample_1                                0.703100  ...   \n",
            "sample_2                                1.611231  ...   \n",
            "sample_3                                1.122685  ...   \n",
            "sample_4                                0.137068  ...   \n",
            "\n",
            "          NMR_triglycerides_in_small_hdl  NMR_triglycerides_in_small_ldl  \\\n",
            "sample_0                        1.582706                        1.603521   \n",
            "sample_1                        1.333737                        1.550667   \n",
            "sample_2                        0.232838                        0.960511   \n",
            "sample_3                        0.207971                        1.572203   \n",
            "sample_4                        0.277896                        0.504176   \n",
            "\n",
            "          NMR_triglycerides_in_small_vldl  NMR_triglycerides_in_vldl  \\\n",
            "sample_0                         1.798793                   0.124354   \n",
            "sample_1                         0.866460                   0.875749   \n",
            "sample_2                         0.830418                   1.556835   \n",
            "sample_3                         0.779137                   0.773767   \n",
            "sample_4                         1.324041                   1.530703   \n",
            "\n",
            "          NMR_triglycerides_in_very_large_hdl  \\\n",
            "sample_0                             1.484782   \n",
            "sample_1                             0.415306   \n",
            "sample_2                             0.188045   \n",
            "sample_3                             1.271274   \n",
            "sample_4                             0.076987   \n",
            "\n",
            "          NMR_triglycerides_in_very_large_vldl  \\\n",
            "sample_0                              1.379147   \n",
            "sample_1                              0.025238   \n",
            "sample_2                              1.270536   \n",
            "sample_3                              0.075515   \n",
            "sample_4                              0.083316   \n",
            "\n",
            "          NMR_triglycerides_in_very_small_vldl  NMR_tyrosine  \\\n",
            "sample_0                              0.731372      1.413498   \n",
            "sample_1                              0.921780      1.635271   \n",
            "sample_2                              1.722808      1.783784   \n",
            "sample_3                              1.071302      1.195527   \n",
            "sample_4                              0.145731      0.903021   \n",
            "\n",
            "          NMR_vldl_cholesterol  NMR_valine  \n",
            "sample_0              0.526513    0.204291  \n",
            "sample_1              0.371166    1.192257  \n",
            "sample_2              1.738579    1.789272  \n",
            "sample_3              0.336085    1.740969  \n",
            "sample_4              1.025892    0.307677  \n",
            "\n",
            "[5 rows x 168 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Run the Metabolomic State Model\n",
        "\n",
        "#@markdown Please execute this cell by pressing the _Play_ button\n",
        "#@markdown on the left to install dependencies and download model weights\n",
        "#@markdown in this Colab notebook.\n",
        "\n",
        "#@markdown Once this cell has been executed a download with the predictions\n",
        "#@markdown will start automatically.\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "results_dir = 'results/'\n",
        "os.makedirs('results/', exist_ok=True)\n",
        "\n",
        "def split_given_size(arr, size):\n",
        "  splits = np.split(arr, np.arange(size,len(arr),size))\n",
        "  n_pad = size-splits[-1].shape[0]\n",
        "  if splits[-1].shape[0] < size:\n",
        "    splits[-1] = np.pad(splits[-1], pad_width=((0, n_pad),\n",
        "                                        (0,0)))\n",
        "  return splits, n_pad\n",
        "\n",
        "with tqdm.notebook.tqdm(total=100, bar_format=TQDM_BAR_FORMAT) as pbar:\n",
        "  with io.capture_output() as captured:\n",
        "    batches, n_padded = split_given_size(input_raw, 1024)\n",
        "    pbar.update(5)\n",
        "    predictions = []\n",
        "\n",
        "    for i, b in enumerate(batches):\n",
        "      preds = inference(b.astype('float32'))\n",
        "      if i+1 == len(batches):\n",
        "        preds = preds.iloc[:-n_padded]\n",
        "      preds.index = preds.index+i*1024\n",
        "      predictions.append(preds)\n",
        "\n",
        "      pbar.update(80//len(batches))\n",
        "\n",
        "    predictions = pd.concat(predictions, axis=0).rename_axis('id').reset_index().melt(id_vars=[\"id\", \"model_id\"], var_name=\"endpoint\", value_name=\"value\").set_index(\"id\")\n",
        "    means = predictions.groupby(['id', 'endpoint']).mean().assign(metabolomic_state=\"mean\").drop('model_id', axis=1)\n",
        "    stds = predictions.groupby(['id', 'endpoint']).std().assign(metabolomic_state=\"std\").drop('model_id', axis=1)\n",
        "    predictions_aggr = pd.concat([means, stds], axis=0).sort_index().reset_index()\n",
        "    pbar.update(10)\n",
        "\n",
        "    predictions_aggr.to_csv(os.path.join(results_dir, 'predictions.csv'))\n",
        "    pbar.update(5)\n",
        "\n",
        "print('DONE')\n",
        "\n",
        "!zip -q -r results.zip {results_dir}\n",
        "files.download(f'results.zip')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67,
          "referenced_widgets": [
            "e68640ac925146a585f5f160a5973bf1",
            "bd9f52bde73648b9b3564534811d52c3",
            "5c82bdc4b9ab4b209df97cd916984fa8",
            "88c31ff131554d69a1b13beb7a2e2476",
            "48e4dc82b2094bec9df75170d0c17e92",
            "5be0c015dd2a4b8e9f6189b059892f1e",
            "178f591734b140678ee1bef0e0e1e7e4",
            "ecbc87cb86884d409315c8e973cc1671",
            "471f413b493349c9b4c1f80e10673f46",
            "947f08722ac8457e95f1d0c8199e9d90",
            "15d1aa91dbd74fc9aab04a0aee2bd1b6"
          ]
        },
        "id": "jmxYMKs9FQ_E",
        "outputId": "93f43130-62de-426a-86b6-1f55b7da030a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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