src/load_data.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P8Y_rm5GqtVm",
        "outputId": "2db84de7-788e-4f78-b133-7cb1eb01c86c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Directory seminar-dlmb-2024-winter-public exists and is non-empty.\n"
          ]
        }
      ],
      "source": [
        "# Downloading our library \n",
        "import os\n",
        "directory = \"seminar-dlmb-2024-winter-public\"\n",
        "\n",
        "if os.path.exists(directory) and os.listdir(directory):\n",
        "    print(f\"Directory {directory} exists and is non-empty.\")\n",
        "else:\n",
        "    print(f\"Directory {directory} does not exist or is empty.\")\n",
        "    !!git clone https://github.com/hzi-bifo/seminar-dlmb-2024-winter-public.git\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T16KLTe-rbiN",
        "outputId": "713dc62f-59b3-4872-ab28-c578eb08c320"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: biopython in /home/hforoughmand/miniforge3/envs/dlmb2/lib/python3.12/site-packages (1.84)\n",
            "Requirement already satisfied: numpy in /home/hforoughmand/miniforge3/envs/dlmb2/lib/python3.12/site-packages (from biopython) (2.1.2)\n",
            "Note: you may need to restart the kernel to use updated packages.\n",
            "Requirement already satisfied: seaborn in /home/hforoughmand/miniforge3/envs/dlmb2/lib/python3.12/site-packages (0.13.2)\n",
            "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /home/hforoughmand/miniforge3/envs/dlmb2/lib/python3.12/site-packages (from seaborn) (2.1.2)\n",
            "Requirement already satisfied: pandas>=1.2 in /home/hforoughmand/miniforge3/envs/dlmb2/lib/python3.12/site-packages (from seaborn) (2.2.3)\n",
            "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /home/hforoughmand/miniforge3/envs/dlmb2/lib/python3.12/site-packages (from seaborn) (3.9.2)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /home/hforoughmand/miniforge3/envs/dlmb2/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.3.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /home/hforoughmand/miniforge3/envs/dlmb2/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /home/hforoughmand/miniforge3/envs/dlmb2/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.54.1)\n",
            "Requirement already satisfied: kiwisolver>=1.3.1 in /home/hforoughmand/miniforge3/envs/dlmb2/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.7)\n",
            "Requirement already satisfied: packaging>=20.0 in /home/hforoughmand/miniforge3/envs/dlmb2/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (24.1)\n",
            "Requirement already satisfied: pillow>=8 in /home/hforoughmand/miniforge3/envs/dlmb2/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /home/hforoughmand/miniforge3/envs/dlmb2/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.1.4)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /home/hforoughmand/miniforge3/envs/dlmb2/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.9.0)\n",
            "Requirement already satisfied: pytz>=2020.1 in /home/hforoughmand/miniforge3/envs/dlmb2/lib/python3.12/site-packages (from pandas>=1.2->seaborn) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /home/hforoughmand/miniforge3/envs/dlmb2/lib/python3.12/site-packages (from pandas>=1.2->seaborn) (2024.2)\n",
            "Requirement already satisfied: six>=1.5 in /home/hforoughmand/miniforge3/envs/dlmb2/lib/python3.12/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n",
            "Note: you may need to restart the kernel to use updated packages.\n"
          ]
        }
      ],
      "source": [
        "%pip install biopython\n",
        "%pip install seaborn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "zLOTRXcMreim"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "sys.path.append('seminar-dlmb-2024-winter-public/src/')\n",
        "# from utility.file_utility import FileUtility\n",
        "from amr.amr_utility import load_gene_data, create_gene_datasets\n",
        "\n",
        "# Creating our dataset\n",
        "create_gene_datasets(\"seminar-dlmb-2024-winter-public/\", \"seminar-dlmb-2024-winter-public/data/ds1\")\n",
        "\n",
        "# Loading the AMR prediction data\n",
        "ds = load_gene_data(\"seminar-dlmb-2024-winter-public/data/ds1\", \"Staphylococcus_aureus_cefoxitin\", \"pbp4\")\n",
        "\n",
        "seq_train = [x[1] for x in ds[\"train\"]]\n",
        "y_train = [x[2] for x in ds[\"train\"]]\n",
        "\n",
        "seq_test = [x[1] for x in ds[\"test\"]]\n",
        "y_test = [x[2] for x in ds[\"test\"]]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WI4sMG38rhqD",
        "outputId": "1ab47378-c90e-440f-d188-c243d4f6596c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Num of samples in train data: 135\n",
            "Num of samples in test data: 15\n"
          ]
        }
      ],
      "source": [
        "print('Num of samples in train data: {}'.format(len(seq_train)))\n",
        "print('Num of samples in test data: {}'.format(len(seq_test)))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GITz5eTbriHR",
        "outputId": "2a3dcfc8-fcb1-4da8-aa78-80d469f77faa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Input of the first element:\n",
            "TGAAAAATTTAATATCTATTATCATCATTTTATGTTTAACATTAAGTATTATGACACCATATGCACAAGCTGCTAACAGTGACGTAACCCCTGTACAAGCAGCAAATCAATATGGTTATGCAGGTTTGTCGGCTGCATACGAACCGACGAGTGCTGTTAATGTTAGTCAAACTGGACAATTACTGTATCAATACAATATCGATACTAAGTGGAATCCAGCGTCTATGACTAAATTAATGACAATGTACTTAACATTGGAAGCTGTAAATAAGGGGCAGCTTTCACTTGACGACACTGTCACAATGACGAACAAAGAATATATTATGTCTACACTACCTGAGTTGAGTAATACGAAACTATATCCTGGACAAGTATGGACAATCTCAGACCTATTACAAATTACGGTATCTAATTCTAGTAATGCCGCGGCATTAATTTTAGCTAAGAAGGTGTCAAAAAACACCAGCGATTTCGTTGATTTAATGAATAACAAAGCTAAAGCTATCGGAATGAAAAACACACATTTCGTCAATCCAACAGGTGCTGAAAATTCAAGATTACGTACATTTGCACCAACAAAGTATAAAGACCAAGAACGTACTGTAACGACTGCTAGAGACTATGCCATTTTAGATTTACACGTGATTAAAGAAACACCTAAAATATTAGACTTTACAAAGCAGTTAGCACCAACAACGCATGCAGTTACGTATTACACATTCAACTTTTCATTGGAAGGTGCAAAAATGAGTTTGCCTGGTACAGATGGTTTAAAAACTGGATCAAGTGATACAGCAAATTACAACCATACGATTACTACTAAACGAGGTAAATTTAGAATTAATCAAGTTATCATGGGTGCGGGAGACTATAAAAAACTTGGTGGCGAGAAGCAACGTAATATGATGGGGAATGCATTAATGGAACGTTCATTTGATCAGTATAAATATGTAAAAATATTGTCTAAAGGTGAGCAAAGGATAAATGGTAAGAAATATTATGTTGAAAATGATCTTTACGATGTTTTACCAAGTGATTTTAGTGAAAAAGATTATAAACTTGTAGTCGAAGATGGTAAAGTACACGCGGACTATCCAAGAGAATTTATTAATAAAGATTATGGACCTCCAACTGTAGAAGTTCATCAGCCAATTATTCAAAAGGCAAATACTGTTGCTAAAAGTATGTGGGCAGAACATCCATTATTCACTATCATTGGTGGTGCATGCCTAGTCGCTGGATTAGCACTAATTGTTCATATGATAATCAATCGTTTATTTAGAAAAAGAAAATAAA\n",
            "First label: 0 -> resistant\n"
          ]
        }
      ],
      "source": [
        "print('Input of the first element:\\n{}'.format(seq_train[0]))\n",
        "print('First label: {} -> resistant'.format(y_train[0]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "6Yx-RGFKrku8"
      },
      "outputs": [],
      "source": [
        "import seaborn as sns\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "eN3uFJqlrm8w",
        "outputId": "6300bc9f-2668-449d-90fd-6196519ced5d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Median and mean sequence length: 1296.0, 1296.0\n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "len_train = [len(s) for s in seq_train]\n",
        "sns.histplot(len_train)\n",
        "median = np.median(len_train)\n",
        "mean = np.mean(len_train)\n",
        "print('Median and mean sequence length: {}, {}'.format(median, mean))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u6p9aOHvrpEZ",
        "outputId": "3a2c4bc1-e82f-4f31-9184-4489348dc96b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Labels in training data\n",
            "Non resistant: 37\n",
            "Resistant: 98\n"
          ]
        }
      ],
      "source": [
        "uniq_vals = set(y_train)\n",
        "y_train_int = list(map(int, y_train))\n",
        "print('Labels in training data')\n",
        "print('Non resistant: {}'.format(y_train_int.count(0)))\n",
        "print('Resistant: {}'.format(y_train_int.count(1)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nq88QNXvrsLv",
        "outputId": "8469db5c-61be-42dc-a903-7eb2be012349"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Labels in test data\n",
            "Non resistant: 3\n",
            "Resistant: 12\n"
          ]
        }
      ],
      "source": [
        "uniq_vals = set(y_test)\n",
        "y_test_int = list(map(int, y_test))\n",
        "print('Labels in test data')\n",
        "print('Non resistant: {}'.format(y_test_int.count(0)))\n",
        "print('Resistant: {}'.format(y_test_int.count(1)))"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "authorship_tag": "ABX9TyO6dOwREOWKQKva2W7E2j0Z",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "dlmb2",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.12.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}