notebooks/microarray_augmentation.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "microarray_augmentation.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPJYy7Nmt2n1du0ubGjS/tk",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/PsorTheDoctor/microarray-data/blob/main/notebooks/microarray_augmentation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bakLL6CELver"
      },
      "source": [
        "#Augmentacja danych mikromacierzowych: CutOut, CutMix, MixUp"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "maTPFYtJEUzY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d2e1fe57-069d-4e7a-e63b-ab7eb9d43edb"
      },
      "source": [
        "!git clone https://github.com/PsorTheDoctor/microarray-data.git"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'microarray-data'...\n",
            "remote: Enumerating objects: 124, done.\u001b[K\n",
            "remote: Counting objects: 100% (124/124), done.\u001b[K\n",
            "remote: Compressing objects: 100% (116/116), done.\u001b[K\n",
            "remote: Total 124 (delta 39), reused 0 (delta 0), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (124/124), 3.32 MiB | 4.01 MiB/s, done.\n",
            "Resolving deltas: 100% (39/39), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zBcKcIl4WcSi",
        "outputId": "b70467f3-a347-4f41-d503-5560f7bb468f"
      },
      "source": [
        "%cd microarray-data/"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/microarray-data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iYsgVB44GCdI"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from utils.load_data import load_data\n",
        "from utils.augmentation import *"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OrUklpSiJfxY",
        "outputId": "30c5b076-01a0-4292-d0ab-b8b375dea597"
      },
      "source": [
        "(X_train, y_train), (X_test, y_test) = load_data()\n",
        "\n",
        "print(X_train.shape)\n",
        "print(X_test.shape)\n",
        "print(y_train.shape)\n",
        "print(y_test.shape)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(857, 32, 32)\n",
            "(200, 32, 32)\n",
            "(857,)\n",
            "(200,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "THki9qw-XP2l",
        "outputId": "052dd459-e073-4b45-d888-b3674b3b91c7"
      },
      "source": [
        "plt.imshow(X_train[0], cmap='viridis')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fc130021150>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "uGKFyKdWX351",
        "outputId": "60c8bcc9-16af-488e-e4e9-df0b7b1887d9"
      },
      "source": [
        "plt.imshow(X_train[1], cmap='viridis')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fc1303ea190>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6OfbCJqDN3f3"
      },
      "source": [
        "##CutOut"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "HAsAN21vR13F",
        "outputId": "076c2e35-79a6-40e1-a625-1fc4655e6e77"
      },
      "source": [
        "(X_aug, y_aug) = augment(X_train[:10], y_train[:10], method='cutout')\n",
        "\n",
        "plt.imshow(X_aug[0], cmap='viridis')"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fc130281b10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EtZAJxOTOL3T"
      },
      "source": [
        "#CutMix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "cqFegHuYJxET",
        "outputId": "7bfdc90e-6d44-4e33-d01a-f27e4e08c738"
      },
      "source": [
        "(X_aug, y_aug) = augment(X_train[:10], y_train[:10], method='cutmix')\n",
        "\n",
        "plt.imshow(X_aug[0], cmap='viridis')"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fc13022e410>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N9EruJH2Y-lT"
      },
      "source": [
        "#MixUp"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GmdJ7c9Aa64z"
      },
      "source": [
        "(X_aug, y_aug) = augment(X_train[:10], y_train[:10], method='mixup')\n",
        "\n",
        "plt.imshow(X_aug[0], cmap='viridis')\n",
        "print(y_aug[0])"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}