{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "RzW5oRp1Cqal",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "91a45e84-ad05-48fe-a55b-69536ba7cfba"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: biopython in /usr/local/lib/python3.11/dist-packages (1.85)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from biopython) (2.0.2)\n"
          ]
        }
      ],
      "source": [
        "!pip3 install biopython"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "id": "mmwEH_DFGiTb",
        "outputId": "0065f8a2-c71d-44b0-92c9-96ecb8338b5b"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-13b00741-462f-4f96-aaed-03b7cc564e24\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-13b00741-462f-4f96-aaed-03b7cc564e24\">\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>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving uniprot_sprot.fasta to uniprot_sprot.fasta\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from Bio import SeqIO\n",
        "import gzip\n",
        "import pandas as pd\n",
        "\n",
        "sequences = []\n",
        "\n",
        "with open(\"uniprot_sprot.fasta\", \"r\") as handle:\n",
        "  for record in SeqIO.parse(handle, \"fasta\"):\n",
        "    uniprot_id = record.id.split('|')[1] if '|' in record.id else record.uniprot_id\n",
        "    sequence = str(record.seq)\n",
        "    sequences.append({\"id\": uniprot_id, \"sequence\": sequence})\n",
        "\n",
        "df = pd.DataFrame(sequences)\n",
        "df.to_csv(\"protein_sequences.csv\", index=False)\n",
        "\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "1uhDA6IgG6dF",
        "outputId": "dcacc64a-ef3a-4deb-a39a-c95cf043020c"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       id                                           sequence\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "      <th>id</th>\n",
              "      <th>sequence</th>\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9562eb5a-84aa-458e-a949-b209ddeac471 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-9562eb5a-84aa-458e-a949-b209ddeac471');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
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              "                title=\"Suggest charts\"\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-70066a55-c01a-4e91-99ab-1344ed1ccf0c button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git config --global user.email \"lionelrozario98@gmail.com\"\n",
        "!git config --global user.name \"midnightoatmeal\""
      ],
      "metadata": {
        "id": "S7CxyQ-YK7CA"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/midnightoatmeal/prot2func.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FoaaMJkLLIfD",
        "outputId": "8b9375ba-3bb6-4807-a39e-d328772e9b0f"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'prot2func'...\n",
            "remote: Enumerating objects: 8, done.\u001b[K\n",
            "remote: Counting objects: 100% (8/8), done.\u001b[K\n",
            "remote: Compressing objects: 100% (7/7), done.\u001b[K\n",
            "remote: Total 8 (delta 0), reused 4 (delta 0), pack-reused 0 (from 0)\u001b[K\n",
            "Receiving objects: 100% (8/8), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import shutil\n",
        "shutil.move(\"protein_sequences.csv\", \"/content/prot2func/protein_sequences.csv\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "vItepcLcRI38",
        "outputId": "1c580446-8caa-4b52-938d-23e95bbbae75"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/prot2func/protein_sequences.csv'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/prot2func/\n",
        "!git add .\n",
        "!git commit -m \"Add protein sequence data\"\n",
        "!git push"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OjuqwNvFRpNh",
        "outputId": "7321711a-3b07-4bbb-9dd1-5ffd1f2c5bda"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/prot2func\n",
            "warning: adding embedded git repository: prot2func\n",
            "\u001b[33mhint: You've added another git repository inside your current repository.\u001b[m\n",
            "\u001b[33mhint: Clones of the outer repository will not contain the contents of\u001b[m\n",
            "\u001b[33mhint: the embedded repository and will not know how to obtain it.\u001b[m\n",
            "\u001b[33mhint: If you meant to add a submodule, use:\u001b[m\n",
            "\u001b[33mhint: \u001b[m\n",
            "\u001b[33mhint: \tgit submodule add <url> prot2func\u001b[m\n",
            "\u001b[33mhint: \u001b[m\n",
            "\u001b[33mhint: If you added this path by mistake, you can remove it from the\u001b[m\n",
            "\u001b[33mhint: index with:\u001b[m\n",
            "\u001b[33mhint: \u001b[m\n",
            "\u001b[33mhint: \tgit rm --cached prot2func\u001b[m\n",
            "\u001b[33mhint: \u001b[m\n",
            "\u001b[33mhint: See \"git help submodule\" for more information.\u001b[m\n",
            "[main 1c36b4d] Add protein sequence data\n",
            " 6 files changed, 4323928 insertions(+)\n",
            " create mode 100644 labeled_protein_data.csv\n",
            " create mode 100644 labeled_protein_data_2.csv\n",
            " create mode 100644 labled_protein_data.csv\n",
            " create mode 160000 prot2func\n",
            " create mode 100644 protein_sequences_with_labels.csv\n",
            " create mode 100644 uniprot_sprot.fasta\n",
            "fatal: could not read Username for 'https://github.com': No such device or address\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash\n",
        "!sudo apt-get install git-lfs\n",
        "!git lfs install"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Sg0-YcfUl14",
        "outputId": "0c800584-fb3c-4dfe-a888-a87b89331047"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Detected operating system as Ubuntu/jammy.\n",
            "Checking for curl...\n",
            "Detected curl...\n",
            "Checking for gpg...\n",
            "Detected gpg...\n",
            "Detected apt version as 2.4.14\n",
            "Running apt-get update... done.\n",
            "Installing apt-transport-https... done.\n",
            "Installing /etc/apt/sources.list.d/github_git-lfs.list...done.\n",
            "Importing packagecloud gpg key... Packagecloud gpg key imported to /etc/apt/keyrings/github_git-lfs-archive-keyring.gpg\n",
            "done.\n",
            "Running apt-get update... done.\n",
            "\n",
            "The repository is setup! You can now install packages.\n",
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "git-lfs is already the newest version (3.6.1).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 36 not upgraded.\n",
            "Updated Git hooks.\n",
            "Git LFS initialized.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git rm --cached protein_sequences.csv"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WGDbmEikWusb",
        "outputId": "991eeaa5-c35b-4cb2-efbc-06e4373e8c06"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "rm 'protein_sequences.csv'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git reset --hard HEAD~2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O2yrGUhMXIiC",
        "outputId": "a10b5ebd-2043-4d01-c870-36f4811ec53e"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "warning: unable to rmdir 'prot2func': Directory not empty\n",
            "HEAD is now at e66ef5c Initial commit\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git lfs install\n",
        "!git lfs track \"protein_sequences.csv\"\n",
        "!git add .gitattributes\n",
        "!git add protein_sequences.csv\n",
        "!git commit -m \"Add protein sequences using git lfs\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JQKs61DoXNZJ",
        "outputId": "f50dabf3-e182-4328-ab61-8b8559ad6013"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Updated Git hooks.\n",
            "Git LFS initialized.\n",
            "Tracking \"protein_sequences.csv\"\n",
            "[main 9598725] Add protein sequences using git lfs\n",
            " 2 files changed, 4 insertions(+)\n",
            " create mode 100644 .gitattributes\n",
            " create mode 100644 protein_sequences.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "\n",
        "def fetch_uniprot_info(uniprot_id):\n",
        "  url = f\"https://rest.uniprot.org/uniprotkb/{uniprot_id}.json\"\n",
        "  response = requests.get(url)\n",
        "  if response.status_code != 200:\n",
        "    return None\n",
        "  data = response.json()\n",
        "\n",
        "  # try to extract enzyme classification or functional annotation\n",
        "  ec_numbers = []\n",
        "  for comment in data.get('comments', []):\n",
        "    if comment['commentType'] == 'FUNCTION':\n",
        "      text = comment['texts'][0]['value']\n",
        "      return text\n",
        "  return None\n",
        ""
      ],
      "metadata": {
        "id": "cYPxIAKnXq-p"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "url = \"https://rest.uniprot.org/uniprotkb/P00374.json\"  # Alcohol dehydrogenase\n",
        "r = requests.get(url)\n",
        "data = r.json()\n",
        "\n",
        "for comment in data.get('comments', []):\n",
        "    print(comment.get('commentType'))\n",
        "    if comment.get('commentType') == 'CATALYTIC ACTIVITY':\n",
        "        print(comment)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l-_eowJLrNCt",
        "outputId": "f3ee903e-5f04-40bd-992f-870091c8fdac"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "FUNCTION\n",
            "CATALYTIC ACTIVITY\n",
            "{'commentType': 'CATALYTIC ACTIVITY', 'reaction': {'name': '(6S)-5,6,7,8-tetrahydrofolate + NADP(+) = 7,8-dihydrofolate + NADPH + H(+)', 'reactionCrossReferences': [{'database': 'Rhea', 'id': 'RHEA:15009'}, {'database': 'ChEBI', 'id': 'CHEBI:15378'}, {'database': 'ChEBI', 'id': 'CHEBI:57451'}, {'database': 'ChEBI', 'id': 'CHEBI:57453'}, {'database': 'ChEBI', 'id': 'CHEBI:57783'}, {'database': 'ChEBI', 'id': 'CHEBI:58349'}], 'ecNumber': '1.5.1.3', 'evidences': [{'evidenceCode': 'ECO:0000255', 'source': 'PROSITE-ProRule', 'id': 'PRU00660'}, {'evidenceCode': 'ECO:0000269', 'source': 'PubMed', 'id': '12096917'}, {'evidenceCode': 'ECO:0000269', 'source': 'PubMed', 'id': '15039552'}, {'evidenceCode': 'ECO:0000269', 'source': 'PubMed', 'id': '17569517'}, {'evidenceCode': 'ECO:0000269', 'source': 'PubMed', 'id': '19196009'}, {'evidenceCode': 'ECO:0000269', 'source': 'PubMed', 'id': '19478082'}, {'evidenceCode': 'ECO:0000269', 'source': 'PubMed', 'id': '21876184'}, {'evidenceCode': 'ECO:0000269', 'source': 'PubMed', 'id': '9719595'}]}}\n",
            "BIOPHYSICOCHEMICAL PROPERTIES\n",
            "PATHWAY\n",
            "SUBUNIT\n",
            "SUBCELLULAR LOCATION\n",
            "ALTERNATIVE PRODUCTS\n",
            "TISSUE SPECIFICITY\n",
            "DISEASE\n",
            "SIMILARITY\n",
            "WEB RESOURCE\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "\n",
        "def is_enzyme(uniprot_id):\n",
        "    url = f\"https://rest.uniprot.org/uniprotkb/{uniprot_id}.json\"\n",
        "    try:\n",
        "        r = requests.get(url)\n",
        "        if r.status_code != 200:\n",
        "            return None\n",
        "        data = r.json()\n",
        "\n",
        "        for comment in data.get('comments', []):\n",
        "            if comment.get('commentType') == 'CATALYTIC ACTIVITY':\n",
        "                if 'reaction' in comment:\n",
        "                    ec_number = comment['reaction'].get('ecNumber')\n",
        "                    if ec_number:\n",
        "                        return 1  # Enzyme\n",
        "        return 0  # Not an enzyme\n",
        "    except Exception as e:\n",
        "        return None"
      ],
      "metadata": {
        "id": "NnPVL4txsQGT"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm import tqdm\n",
        "import time\n",
        "\n",
        "df_sample = df.head(500)\n",
        "enzyme_labels = []\n",
        "\n",
        "for pid in tqdm(df_sample['id']):\n",
        "  label = is_enzyme(pid)\n",
        "  enzyme_labels.append(label)\n",
        "  time.sleep(0.1)\n",
        "df_sample['is_enzyme'] = enzyme_labels\n",
        "df_sample.to_csv(\"labeled_protein_data.csv\", index=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2eB6rJIK1771",
        "outputId": "6025dfd1-15ac-46c3-8ce0-9e6f6fa552d9"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 500/500 [05:33<00:00,  1.50it/s]\n",
            "<ipython-input-72-6ec560d59fba>:11: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_sample['is_enzyme'] = enzyme_labels\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"labeled_protein_data.csv\")\n",
        "df.head()\n",
        "df.info()\n",
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "hMrjxnSG3h20",
        "outputId": "d66d7a7f-77a2-4bb2-ec7f-05424face810"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 500 entries, 0 to 499\n",
            "Data columns (total 3 columns):\n",
            " #   Column     Non-Null Count  Dtype \n",
            "---  ------     --------------  ----- \n",
            " 0   id         500 non-null    object\n",
            " 1   sequence   500 non-null    object\n",
            " 2   is_enzyme  500 non-null    int64 \n",
            "dtypes: int64(1), object(2)\n",
            "memory usage: 11.8+ KB\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        is_enzyme\n",
              "count  500.000000\n",
              "mean     0.026000\n",
              "std      0.159295\n",
              "min      0.000000\n",
              "25%      0.000000\n",
              "50%      0.000000\n",
              "75%      0.000000\n",
              "max      1.000000"
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              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-7f61ebc0-eb3c-47dc-b58b-5b6212e2d3b1 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"is_enzyme\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 176.7171624854003,\n        \"min\": 0.0,\n        \"max\": 500.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.026,\n          1.0,\n          0.15929453694021686\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['is_enzyme'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "id": "L_LJt4an5kG0",
        "outputId": "c38b848a-55c3-4707-8459-a4c78f80eb30"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "is_enzyme\n",
              "0    487\n",
              "1     13\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>is_enzyme</th>\n",
              "      <th></th>\n",
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              "      <td>13</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_more = df[500:1500].copy()\n"
      ],
      "metadata": {
        "id": "epewshg95yuI"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"protein_sequences.csv\")\n",
        "df_more = df[500:1000].copy()"
      ],
      "metadata": {
        "id": "zACPBrQd5-jh"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_sample = df_more.copy()"
      ],
      "metadata": {
        "id": "JtNRKx57emJM"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(df_sample))\n",
        "print(df_sample['id'].head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qs0HgTYMfWzP",
        "outputId": "9a9d0306-71a7-4a5c-cd31-e5e0949607db"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "500\n",
            "500    Q9MB95\n",
            "501    Q06402\n",
            "502    Q00257\n",
            "503    Q00379\n",
            "504    Q7XQ85\n",
            "Name: id, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm import tqdm\n",
        "import time\n",
        "\n",
        "df_sample = df_more.head(500).copy()\n",
        "\n",
        "# Safety check\n",
        "if 'id' not in df_sample.columns:\n",
        "    raise ValueError(\"Missing 'id' column!\")\n",
        "\n",
        "enzyme_labels = []\n",
        "\n",
        "for pid in tqdm(df_sample['id']):\n",
        "    label = is_enzyme(pid)\n",
        "    enzyme_labels.append(label)\n",
        "    time.sleep(0.1)\n",
        "\n",
        "df_sample['is_enzyme'] = enzyme_labels\n",
        "df_sample.to_csv(\"labeled_protein_data_2.csv\", index=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P8u-bz1hfZhD",
        "outputId": "c550b070-07fe-4a35-a65d-c82e85cf529a"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 500/500 [05:34<00:00,  1.50it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_more.head(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 669
        },
        "id": "emNmMU4yfc0u",
        "outputId": "47c01992-edda-4244-caff-902be7c59412"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "503      Q00379  MGFHQIDERNQALLSKIAIDDGHGENSAYFDGWKAYDNNPFHPENN...\n",
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              "507      Q42881  MKLLSEKATCNSHGQDSSYFLGWQEYEKNPYDEIQNPKGIIQMGLA...\n",
              "508      Q43309  MVQLSRKATCNSHGQVSSYFLGWEEYEKNPYDVTKNPQGIIQMGLA...\n",
              "509      Q5W6F9  MGVKLLADGCAGASSSPALSRVATSAAHGEGSPYFAGWKAYDEDPY...\n",
              "510      P29535  MDLETSEISNYKSSVVLSKLASNEQHGENSPYFDGWKAYDNDPFHL...\n",
              "511      Q37001  MKQLSTKVTSNGHGQDSSYFLGWEEYEKNPYDEIKNPNGMIQMGLA...\n",
              "512  A0A0P0UZP7  MIMSGFHIGIYTSICLYIPLPHLEPWIISSHTPKNLNLLDCLLYCS...\n",
              "513      Q9SAR0  MVAFATEKKQDLNLLSKIASGDGHGENSSYFDGWKAYEENPFHPID...\n",
              "514      Q9SNN8  MRRSGNGGAAKKKKKRSASAASERRPRADGGMRIVVPLQGVVQGRG...\n",
              "515      Q9STR4  MGLPLMMERSSNNNNVELSRVAVSDTHGEDSPYFAGWKAYDENPYD...\n",
              "516      Q9T065  MGLLSKKASCNTHGQDSSYFWGWEEYEKNPYDEIKNPDGIIQMGLA...\n",
              "517      Q9M2Y8  MKQLSRKVTSNAHGQDSSYFLGWEEYEKNPYDEIKNPNGIIQMGLA...\n",
              "518      P27486  MGSYKGVYDREILSKIATNDGHGENLEYFDGWKAYDRDPYHSTKNS...\n",
              "519      P37821  MRMLSRNATFNSHGQDSSYFLGWQEYEKNPYHEVHNTNGIIQMGLA..."
            ],
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              "      <th>500</th>\n",
              "      <td>Q9MB95</td>\n",
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              "      <td>MGFHQIDERNQALLSKIAIDDGHGENSAYFDGWKAYDNNPFHPENN...</td>\n",
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              "      <th>506</th>\n",
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              "      <td>MVGRMLSSPEPTLSTMAMSAAHGEDSPYFAGWRAYDEDPYDPITNP...</td>\n",
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              "      <th>507</th>\n",
              "      <td>Q42881</td>\n",
              "      <td>MKLLSEKATCNSHGQDSSYFLGWQEYEKNPYDEIQNPKGIIQMGLA...</td>\n",
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              "      <th>508</th>\n",
              "      <td>Q43309</td>\n",
              "      <td>MVQLSRKATCNSHGQVSSYFLGWEEYEKNPYDVTKNPQGIIQMGLA...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>509</th>\n",
              "      <td>Q5W6F9</td>\n",
              "      <td>MGVKLLADGCAGASSSPALSRVATSAAHGEGSPYFAGWKAYDEDPY...</td>\n",
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              "      <th>510</th>\n",
              "      <td>P29535</td>\n",
              "      <td>MDLETSEISNYKSSVVLSKLASNEQHGENSPYFDGWKAYDNDPFHL...</td>\n",
              "    </tr>\n",
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              "      <th>511</th>\n",
              "      <td>Q37001</td>\n",
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              "      <th>512</th>\n",
              "      <td>A0A0P0UZP7</td>\n",
              "      <td>MIMSGFHIGIYTSICLYIPLPHLEPWIISSHTPKNLNLLDCLLYCS...</td>\n",
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              "      <th>513</th>\n",
              "      <td>Q9SAR0</td>\n",
              "      <td>MVAFATEKKQDLNLLSKIASGDGHGENSSYFDGWKAYEENPFHPID...</td>\n",
              "    </tr>\n",
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              "      <th>514</th>\n",
              "      <td>Q9SNN8</td>\n",
              "      <td>MRRSGNGGAAKKKKKRSASAASERRPRADGGMRIVVPLQGVVQGRG...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>515</th>\n",
              "      <td>Q9STR4</td>\n",
              "      <td>MGLPLMMERSSNNNNVELSRVAVSDTHGEDSPYFAGWKAYDENPYD...</td>\n",
              "    </tr>\n",
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              "      <th>516</th>\n",
              "      <td>Q9T065</td>\n",
              "      <td>MGLLSKKASCNTHGQDSSYFWGWEEYEKNPYDEIKNPDGIIQMGLA...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>517</th>\n",
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              "      <td>MKQLSRKVTSNAHGQDSSYFLGWEEYEKNPYDEIKNPNGIIQMGLA...</td>\n",
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              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ccc136ab-c3e8-47d2-8f34-e5833ce0c300')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ccc136ab-c3e8-47d2-8f34-e5833ce0c300 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ccc136ab-c3e8-47d2-8f34-e5833ce0c300');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    border-radius: 50%;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "  }\n",
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              "    border-color: transparent;\n",
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              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_more",
              "summary": "{\n  \"name\": \"df_more\",\n  \"rows\": 500,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 500,\n        \"samples\": [\n          \"C0HMB7\",\n          \"Q9WY68\",\n          \"Q65123\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sequence\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 470,\n        \"samples\": [\n          \"MNLQRFPRYPLTFGPTPIQPLARLSKHLGGKVHLYAKREDCNSGLAFGGNKTRKLEYLIPEALAQGCDTLVSIGGIQSNQTRQVAAVAAHLGMKCVLVQENWVNYSDAVYDRVGNIQMSRILGADVRLVPDGFDIGFRRSWEDALESVRAAGGKPYAIPAGCSDHPLGGLGFVGFAEEVRAQEAELGFKFDYVVVCSVTGSTQAGMVVGFAADGRADRVIGVDASAKPAQTREQITRIARQTAEKVGLERDIMRADVVLDERFAGPEYGLPNEGTLEAIRLCARTEGMLTDPVYEGKSMHGMIEMVRNGEFPEGSRVLYAHLGGVPALNGYSFIFRDG\",\n          \"MSHRSDTLPVPSGQRRGRVPRDHSIYTQLLEITLHLQQAMTEHFVQLTSRQGLSLEERRHTEAICEHEALLSRLICRMINLLQSGAASGLELQVPLPSEDSRGDVRYGQRAQLSGQPDPVPQLSDCEAAFVNRDLSIRGIDISVFYQSSFQDYNAYQKDKYHKDKNTLGFINLGTSENKLCMDLMTERLQESDMNCIEDTLLQYPDWRGQPFLREEVARFLTYYCRAPTRLDPENVVVLNGCCSVFCALAMVLCDPGEAFLVPAPFYGGFAFSSRLYAKVELIPVHLESEVTVTNTHPFQLTVDKLEEALLEARLEGKKVRGLVLINPQNPLGDIYSPDSLMKYLEFAKRYNLHVIIDEIYMLSVFDESITFHSILSMKSLPDSNRTHVIWGTSKDFGISGFRFGALYTHNKEVASAVSAFGYLHSISGITQHKLCQLLQNTEWIDKVYLPTNCYRLREAHKYITAELKALEIPFHNRSSGLYVWINLKKYLDPCTFEEERLLYCRFLDNKLLLSRGKTYMCKEPGWFCLIFADELPRLKLAMRRFCDVLQEQKEALIVKQLEDAMRE\",\n          \"MNLQRFPRYPLTFGPTPIQPLKRLSEHLGGKVELYAKREDCNSGLAFGGNKTRKLEYLIPDALEQRADTLVSIGGVQSNQTRQVAAVAAHLGMKCVLVQEHWVNYDDPVYDRVGNIQLSRMMGADVRLVPDGFDIGIRRSWEEALESVKQAGGRPYPIPAGCSEHPLGGLGFVGFAEEVRAQEAQFGLRFDYIVVCSVTGSTQAGMIVGFAADGRADRVIGIDASATPARTREQITRIARHTAELVDLGRDITDADVVLDTRYAGPEYGLPNEGTLEAIRLCARLEGVLTDPVYEGKSMHGMIDKVRRGEFEPGSKVLYAHLGGVPALSAYSAIFADG\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"protein_sequences.csv\")\n",
        "print(df.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9m_zNv1vg5PP",
        "outputId": "fb9d72e1-a164-4f39-be2a-5325306b681f"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(573230, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_more = df[500:1000].copy()"
      ],
      "metadata": {
        "id": "JDovIYbGhY_C"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm import tqdm\n",
        "import time\n",
        "\n",
        "enzyme_labels = []\n",
        "\n",
        "for pid in tqdm(df_more['id']):\n",
        "  label = is_enzyme(pid)\n",
        "  enzyme_labels.append(label)\n",
        "  time.sleep(0.1)\n",
        "df_more['is_enzyme'] = enzyme_labels\n",
        "df_more.to_csv(\"labeled_protein_data_2.csv\", index=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dks74rt9hd_W",
        "outputId": "05809966-54e2-4491-d2e9-5ea9253c3573"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 500/500 [05:26<00:00,  1.53it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_more['is_enzyme'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "id": "1S8X5wPdh7wa",
        "outputId": "644f6470-2098-4f09-c111-c5fd5658a82e"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "is_enzyme\n",
              "0    360\n",
              "1    140\n",
              "Name: count, dtype: int64"
            ],
            "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>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>is_enzyme</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>140</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"protein_sequences.csv\")\n",
        "df_sample = df.head(500).copy()"
      ],
      "metadata": {
        "id": "PXaaLEpwjgfN"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm import tqdm\n",
        "import time\n",
        "\n",
        "enzyme_labels = []\n",
        "for pid in tqdm(df_sample['id']):\n",
        "  label = is_enzyme(pid)\n",
        "  enzyme_labels.append(label)\n",
        "  time.sleep(0.1)\n",
        "df_sample['is_enzyme'] = enzyme_labels\n",
        "df_sample.to_csv(\"labled_protein_data.csv\", index=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jg8FDPwfj6Ei",
        "outputId": "4be20a34-3592-4ee8-e6ec-bd9823dbe72a"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 500/500 [05:26<00:00,  1.53it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = pd.read_csv(\"labled_protein_data.csv\")\n",
        "df2 = pd.read_csv(\"labeled_protein_data_2.csv\")\n",
        "\n",
        "print(df1.shape)\n",
        "print(df2.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wB7OtHuclKJr",
        "outputId": "f44a07d6-56b1-4dce-e75c-ab8970ffb54c"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(500, 3)\n",
            "(500, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = pd.read_csv(\"labeled_protein_data.csv\")\n",
        "df2 = pd.read_csv(\"labeled_protein_data_2.csv\")\n",
        "df_combined = pd.concat([df1, df2], ignore_index=True)\n",
        "df_combined = df_combined.dropna(subset=[\"is_enzyme\"])\n",
        "df_combined[\"is_enzyme\"] = df_combined[\"is_enzyme\"].astype(int)"
      ],
      "metadata": {
        "id": "LOlQVxjwm4y0"
      },
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import time\n",
        "from tqdm import tqdm\n",
        "\n",
        "# Load full dataset\n",
        "df = pd.read_csv(\"protein_sequences.csv\")\n",
        "\n",
        "# Take the first 500 and copy\n",
        "df_sample = df.head(500).copy()\n",
        "\n",
        "# Reuse your is_enzyme() function\n",
        "enzyme_labels = []\n",
        "\n",
        "for pid in tqdm(df_sample['id']):\n",
        "    label = is_enzyme(pid)\n",
        "    enzyme_labels.append(label)\n",
        "    time.sleep(0.1)\n",
        "\n",
        "df_sample['is_enzyme'] = enzyme_labels\n",
        "\n",
        "# Save the newly labeled file\n",
        "df_sample.to_csv(\"labeled_protein_data.csv\", index=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VEtggRwqoCVe",
        "outputId": "48e4541b-dc42-49ab-db7a-96baffd0e41e"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 500/500 [05:25<00:00,  1.53it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = pd.read_csv(\"labeled_protein_data.csv\")\n",
        "df2 = pd.read_csv(\"labeled_protein_data_2.csv\")\n",
        "df_combined = pd.concat([df1, df2], ignore_index=True)\n",
        "print(df_combined.shape)  # should be (1000, 3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GZCAwm07oDbM",
        "outputId": "3ce0fd57-9bb8-4ca1-8dbb-f931224a67ef"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1000, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from collections import Counter\n",
        "import pandas as pd\n",
        "\n",
        "AMINO_ACIDS = list(\"ACDEFGHIKLMNPQRSTVWY\")\n",
        "\n",
        "def aa_composition(seq):\n",
        "  count = Counter(seq)\n",
        "  total = len(seq)\n",
        "  return [count.get(aa, 0) / total for aa in AMINO_ACIDS]\n",
        "\n",
        "df_features = df_combined.copy()\n",
        "df_features[AMINO_ACIDS] = df_features[\"sequence\"].apply(lambda x: pd.Series(aa_composition(x)))\n"
      ],
      "metadata": {
        "id": "GnCD_hWaqDaN"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "sns.countplot(x=\"is_enzyme\", data=df_features)\n",
        "plt.title(\"Enzyme vs Non-Enzyme Count\")\n",
        "plt.xlabel(\"Is Enzyme\")\n",
        "plt.ylabel(\"Count\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "KuaIedLMZt2u",
        "outputId": "44ec7386-2732-49da-ba25-66de1bf86a11"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_features[AMINO_ACIDS].hist(figsize=(15, 10), bins=20)\n",
        "plt.suptitle(\"Amino Acid Composition Distributions\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 594
        },
        "id": "xsEgmPZgaG5D",
        "outputId": "e45ff4b9-487d-4eff-921b-8c34c9c31121"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 20 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "avg_comp = df_features.groupby(\"is_enzyme\")[AMINO_ACIDS].mean().T\n",
        "avg_comp.plot(kind=\"bar\", figsize=(15, 5))\n",
        "plt.title(\"Average Amino Acid Composition by Class\")\n",
        "plt.xlabel(\"Amino Acid\")\n",
        "plt.ylabel(\"Average Frequency\")\n",
        "plt.legend([\"Non-Enzyme\", \"Enzyme\"])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "id": "ZQY3l6sfalBe",
        "outputId": "17bdf5b5-d6a8-44d4-d0e4-a7481e76eb28"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X = df_features[AMINO_ACIDS]\n",
        "y = df_features[\"is_enzyme\"]\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,\n",
        "                                                    y,\n",
        "                                                    test_size=0.2,\n",
        "                                                    random_state=42,\n",
        "                                                    stratify=y)"
      ],
      "metadata": {
        "id": "M55A9wGPa5TN"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "model = LogisticRegression(max_iter=1000)\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "y_pred = model.predict(X_test)\n",
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2fo6VUtZaoPl",
        "outputId": "710f2d5e-b963-4c8d-ebf5-6d0f9237c2f9"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      1.00      0.92       169\n",
            "           1       0.00      0.00      0.00        31\n",
            "\n",
            "    accuracy                           0.84       200\n",
            "   macro avg       0.42      0.50      0.46       200\n",
            "weighted avg       0.71      0.84      0.77       200\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
            "/usr/local/lib/python3.11/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
            "/usr/local/lib/python3.11/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert data to tensors\n",
        "\n",
        "import torch\n",
        "from torch.utils.data import TensorDataset, DataLoader\n",
        "\n",
        "X_tensor = torch.tensor(X.values, dtype=torch.float32)\n",
        "y_tensor = torch.tensor(y.values, dtype=torch.long)\n",
        "\n",
        "dataset = TensorDataset(X_tensor, y_tensor)\n",
        "train_size = int(0.8 * len(dataset))\n",
        "test_size = len(dataset) - train_size\n",
        "\n",
        "train_dataset, test_dataset = torch.utils.data.random_split(dataset, [train_size, test_size])\n",
        "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
        "test_loader = DataLoader(test_dataset, batch_size=32)"
      ],
      "metadata": {
        "id": "J0lOv_kIcCVw"
      },
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "class EnzymeClassifier(nn.Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.fc1 = nn.Linear(20, 64)\n",
        "    self.fc2 = nn.Linear(64, 32)\n",
        "    self.out = nn.Linear(32, 2)\n",
        "\n",
        "  def forward(self, x):\n",
        "    x = F.relu(self.fc1(x))\n",
        "    x = F.relu(self.fc2(x))\n",
        "    return self.out(x)\n",
        ""
      ],
      "metadata": {
        "id": "0KdLM-kncCTS"
      },
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_2 = EnzymeClassifier()\n",
        "model_2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VUIzfyJAcCRC",
        "outputId": "d6fcda6d-7912-4930-8f07-055ec1f7be0e"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "EnzymeClassifier(\n",
              "  (fc1): Linear(in_features=20, out_features=64, bias=True)\n",
              "  (fc2): Linear(in_features=64, out_features=32, bias=True)\n",
              "  (out): Linear(in_features=32, out_features=2, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_2.state_dict()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "coVQGIlFcCOv",
        "outputId": "1a9c46a0-928d-44fa-a873-dee2b6be71dc"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OrderedDict([('fc1.weight',\n",
              "              tensor([[ 0.0410, -0.1476,  0.2080,  ..., -0.0241, -0.1369,  0.1540],\n",
              "                      [-0.1473, -0.0169,  0.2216,  ..., -0.0375, -0.1823, -0.0753],\n",
              "                      [ 0.0144,  0.0473, -0.0789,  ..., -0.2203, -0.1355, -0.0128],\n",
              "                      ...,\n",
              "                      [-0.0488, -0.0619,  0.2074,  ..., -0.1802,  0.1857,  0.1643],\n",
              "                      [-0.2169, -0.1247,  0.0188,  ..., -0.0551, -0.1546,  0.0597],\n",
              "                      [-0.1639,  0.1408, -0.1742,  ...,  0.0760, -0.1133,  0.1541]])),\n",
              "             ('fc1.bias',\n",
              "              tensor([-0.1309,  0.0898,  0.0059, -0.0417,  0.0076, -0.0474,  0.1032,  0.0107,\n",
              "                      -0.0422,  0.1092, -0.0110, -0.0662,  0.1191, -0.0829, -0.0477, -0.0964,\n",
              "                       0.0637,  0.2169,  0.1400, -0.1837,  0.1026,  0.2030, -0.0347,  0.0665,\n",
              "                       0.2205, -0.1009,  0.0097,  0.0110, -0.0588,  0.0032,  0.1974, -0.0925,\n",
              "                       0.0532, -0.0293,  0.1615,  0.1930,  0.1386,  0.0043, -0.0231, -0.0520,\n",
              "                      -0.0197,  0.1594, -0.0794, -0.1531, -0.1223, -0.1384,  0.1953, -0.1388,\n",
              "                      -0.0298, -0.0497,  0.0439, -0.1429, -0.1219, -0.1391, -0.0532,  0.1787,\n",
              "                       0.1363, -0.1857,  0.0893,  0.0765, -0.0644,  0.0401, -0.0966,  0.0877])),\n",
              "             ('fc2.weight',\n",
              "              tensor([[-0.0899, -0.1130,  0.0197,  ..., -0.1224,  0.0717, -0.0582],\n",
              "                      [ 0.0211,  0.0454, -0.0317,  ...,  0.0948, -0.0936, -0.0913],\n",
              "                      [ 0.0673,  0.0885,  0.1090,  ..., -0.0793, -0.1180, -0.0295],\n",
              "                      ...,\n",
              "                      [ 0.0196,  0.1200,  0.0544,  ...,  0.1175,  0.0340,  0.1146],\n",
              "                      [ 0.1213, -0.0066,  0.1137,  ...,  0.0984,  0.0300,  0.0117],\n",
              "                      [-0.0632,  0.0259,  0.1151,  ...,  0.0957,  0.0925, -0.0794]])),\n",
              "             ('fc2.bias',\n",
              "              tensor([-0.0482,  0.0810, -0.0419,  0.0688,  0.1186, -0.0406,  0.0037,  0.1210,\n",
              "                       0.1234, -0.0761, -0.0849, -0.0485, -0.0360, -0.0421, -0.0280,  0.0126,\n",
              "                       0.0363, -0.0211,  0.1194, -0.0999, -0.1136, -0.0183, -0.1132, -0.0300,\n",
              "                      -0.0597, -0.0802, -0.0052, -0.0499, -0.0620, -0.0373,  0.1030, -0.0608])),\n",
              "             ('out.weight',\n",
              "              tensor([[-0.0769,  0.1325,  0.0899,  0.0475, -0.1640, -0.0764, -0.1678,  0.1567,\n",
              "                       -0.1409,  0.0330,  0.1529,  0.0310,  0.1456,  0.1646,  0.0874, -0.1520,\n",
              "                       -0.1327, -0.1322,  0.0446, -0.0214,  0.1517, -0.0761,  0.0419, -0.1600,\n",
              "                       -0.1451,  0.0538, -0.0954,  0.1679,  0.1426, -0.0949, -0.0915,  0.1232],\n",
              "                      [-0.1702, -0.0823, -0.0003, -0.0992, -0.1160,  0.0447,  0.0252,  0.0281,\n",
              "                       -0.0028,  0.0548, -0.1235, -0.1541,  0.1076,  0.1538, -0.1310, -0.1645,\n",
              "                        0.0006,  0.0612, -0.1102, -0.0599,  0.0454, -0.0632, -0.0555,  0.0351,\n",
              "                        0.1637,  0.1032,  0.0536, -0.1686,  0.1537, -0.0998,  0.1476,  0.0393]])),\n",
              "             ('out.bias', tensor([-0.1499, -0.1382]))])"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = EnzymeClassifier()\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
        "\n",
        "for epoch in range(20):\n",
        "  model.train()\n",
        "  total_loss = 0\n",
        "  for xb, yb in train_loader:\n",
        "    pred = model(xb)\n",
        "    loss = loss_fn(pred, yb)\n",
        "    optimizer.zero_grad()\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    total_loss += loss.item()\n",
        "  print(f\"Epoch {epoch+1}, loss: {total_loss:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FoY_b85PcCF3",
        "outputId": "bcb05f0e-84c8-4ab6-e28d-89c33a2a33dd"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1, loss: 15.5039\n",
            "Epoch 2, loss: 13.1869\n",
            "Epoch 3, loss: 11.1452\n",
            "Epoch 4, loss: 10.5861\n",
            "Epoch 5, loss: 10.5612\n",
            "Epoch 6, loss: 10.4999\n",
            "Epoch 7, loss: 10.4306\n",
            "Epoch 8, loss: 10.3792\n",
            "Epoch 9, loss: 10.2836\n",
            "Epoch 10, loss: 10.1962\n",
            "Epoch 11, loss: 10.0914\n",
            "Epoch 12, loss: 9.9736\n",
            "Epoch 13, loss: 9.8301\n",
            "Epoch 14, loss: 9.6838\n",
            "Epoch 15, loss: 9.5342\n",
            "Epoch 16, loss: 9.3001\n",
            "Epoch 17, loss: 9.0728\n",
            "Epoch 18, loss: 8.8039\n",
            "Epoch 19, loss: 8.5540\n",
            "Epoch 20, loss: 8.3271\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip3 install torchmetrics"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x98mplkEe31C",
        "outputId": "456f7b2a-4371-4a37-b944-c5904ec7c401"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting torchmetrics\n",
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            "Collecting lightning-utilities>=0.8.0 (from torchmetrics)\n",
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            "\u001b[?25hDownloading nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl (207.5 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.5/207.5 MB\u001b[0m \u001b[31m1.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m69.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, lightning-utilities, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, torchmetrics\n",
            "  Attempting uninstall: nvidia-nvjitlink-cu12\n",
            "    Found existing installation: nvidia-nvjitlink-cu12 12.5.82\n",
            "    Uninstalling nvidia-nvjitlink-cu12-12.5.82:\n",
            "      Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82\n",
            "  Attempting uninstall: nvidia-curand-cu12\n",
            "    Found existing installation: nvidia-curand-cu12 10.3.6.82\n",
            "    Uninstalling nvidia-curand-cu12-10.3.6.82:\n",
            "      Successfully uninstalled nvidia-curand-cu12-10.3.6.82\n",
            "  Attempting uninstall: nvidia-cufft-cu12\n",
            "    Found existing installation: nvidia-cufft-cu12 11.2.3.61\n",
            "    Uninstalling nvidia-cufft-cu12-11.2.3.61:\n",
            "      Successfully uninstalled nvidia-cufft-cu12-11.2.3.61\n",
            "  Attempting uninstall: nvidia-cuda-runtime-cu12\n",
            "    Found existing installation: nvidia-cuda-runtime-cu12 12.5.82\n",
            "    Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n",
            "      Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82\n",
            "  Attempting uninstall: nvidia-cuda-nvrtc-cu12\n",
            "    Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82\n",
            "    Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n",
            "      Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n",
            "  Attempting uninstall: nvidia-cuda-cupti-cu12\n",
            "    Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n",
            "    Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n",
            "      Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n",
            "  Attempting uninstall: nvidia-cublas-cu12\n",
            "    Found existing installation: nvidia-cublas-cu12 12.5.3.2\n",
            "    Uninstalling nvidia-cublas-cu12-12.5.3.2:\n",
            "      Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n",
            "  Attempting uninstall: nvidia-cusparse-cu12\n",
            "    Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n",
            "    Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n",
            "      Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n",
            "  Attempting uninstall: nvidia-cudnn-cu12\n",
            "    Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n",
            "    Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n",
            "      Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n",
            "  Attempting uninstall: nvidia-cusolver-cu12\n",
            "    Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n",
            "    Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n",
            "      Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n",
            "Successfully installed lightning-utilities-0.14.3 nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127 torchmetrics-1.7.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torchmetrics\n",
        "from torchmetrics import Accuracy, Precision, Recall, F1Score\n",
        "\n",
        "accuracy = Accuracy(task=\"binary\")\n",
        "precision = Precision(task=\"binary\")\n",
        "recall = Recall(task=\"binary\")\n",
        "f1 = F1Score(task=\"binary\")"
      ],
      "metadata": {
        "id": "kNVjqpExfGts"
      },
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_2.eval()\n",
        "\n",
        "all_preds = []\n",
        "all_labels = []\n",
        "\n",
        "with torch.inference_mode():\n",
        "  for xb, yb in test_loader:\n",
        "    logits = model_2(xb)\n",
        "    preds = torch.argmax(logits, dim=1)\n",
        "\n",
        "    all_preds.append(preds)\n",
        "    all_labels.append(yb)\n",
        "\n",
        "all_preds = torch.cat(all_preds)\n",
        "all_labels = torch.cat(all_labels)\n",
        "\n",
        "acc = accuracy(all_preds, all_labels)\n",
        "prec = precision(all_preds, all_labels)\n",
        "rec = recall(all_preds, all_labels)\n",
        "f1_score = f1(all_preds, all_labels)\n",
        "\n",
        "print(f\"Accuracy: {acc:.4f}\")\n",
        "print(f\"Precision: {prec:.4f}\")\n",
        "print(f\"Recall: {rec:.4f}\")\n",
        "print(f\"F1 Score: {f1_score:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qEGYAri4fyMU",
        "outputId": "9ef4a54b-96c1-4aa2-d8df-620d44cca592"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.2100\n",
            "Precision: 0.1640\n",
            "Recall: 1.0000\n",
            "F1 Score: 0.2818\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "T4UtTVd9fyKH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Y9_e7LcRfyHe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YAxQh4omfx_n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "l3imvBYdfx5d"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}