examples/01_Creating_Imbalanced_Benchmark_Datasets.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZrZfz0cipGsD"
      },
      "source": [
        "# **Constructing imbalanced datasets for CIFAR10, CIFAR100, Cat_vs_Dog, STL10**\n",
        "\n",
        "**Author**: Zhuoning Yuan\n",
        "\n",
        "**Useful Resources**:\n",
        "* Website: https://libauc.org\n",
        "* Github: https://github.com/Optimization-AI/LibAUC\n",
        "\n",
        "**Reference**:  \n",
        "\n",
        "If you find this tutorial helpful in your work,  please acknowledge our library and cite the following paper:\n",
        "\n",
        "<pre>\n",
        "@inproceedings{yuan2021large,\n",
        "  title={Large-scale robust deep auc maximization: A new surrogate loss and empirical studies on medical image classification},\n",
        "  author={Yuan, Zhuoning and Yan, Yan and Sonka, Milan and Yang, Tianbao},\n",
        "  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},\n",
        "  pages={3040--3049},\n",
        "  year={2021}\n",
        "}\n",
        "</pre>\n",
        "\n",
        "<pre>\n",
        "@software{libauc2022,\n",
        "      title={LibAUC: A Deep Learning Library for X-Risk Optimization.},\n",
        "      author={Zhuoning Yuan, Zi-Hao Qiu, Gang Li, Dixian Zhu, Zhishuai Guo, Quanqi Hu, Bokun Wang, Qi Qi, Yongjian Zhong, Tianbao Yang},\n",
        "      year={2022}\n",
        "    }\n",
        "</pre>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E0wuWoaVncy4"
      },
      "source": [
        "# **01.Installing LibAUC**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sfARclceZs6e"
      },
      "source": [
        "!pip install libauc==1.2.0"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IQGm3s0naLRl"
      },
      "source": [
        "# **02. Loading Datasets**\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MpZo_rELajYZ"
      },
      "source": [
        "### CIFAR10\n",
        "* **Description**: The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.\n",
        "* **Homepage:** https://www.cs.toronto.edu/~kriz/cifar.html\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 103,
          "referenced_widgets": [
            "b20719102a594f2bb8a69529fb2decad",
            "f04ce0f9ad9348a8afda009a992dc5c6",
            "23ab51f39c4f462ab7796df181802be0",
            "20f8f670760842859444ce35821752b2",
            "848db45bf6e24f0ab94feb8a7be2886f",
            "3e252a44cf7144fc8b3cce9addfec3e7",
            "ebf45ec1ea784c8c9d1fe76eafe3c362",
            "e70561d9f51e4197936906086a391f91",
            "876824f2208d44d5a5bbe11125c0ad33",
            "6175b339caec43a1a0d30446af6b0baf",
            "c6b6ef87fa444e7499dbf9c6a2f798c4"
          ]
        },
        "id": "FtC2bU-FaKnL",
        "outputId": "52e297f5-da82-4fa4-d3dd-61cf70cd9da3"
      },
      "source": [
        "from libauc.datasets import CIFAR10\n",
        "(train_data, train_label) = CIFAR10(root='./data', train=True) \n",
        "(test_data, test_label) = CIFAR10(root='./data', train=False) "
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/170498071 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "model_id": "b20719102a594f2bb8a69529fb2decad"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/cifar-10-python.tar.gz to ./data\n",
            "Files already downloaded and verified\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cdocj-KMasfc"
      },
      "source": [
        "### CIFAR100\n",
        "* **Description**: This dataset is just like the CIFAR-10, except it has 100 classes containing 600 images each. There are 500 training images and 100 testing images per class. The 100 classes in the CIFAR-100 are grouped into 20 superclasses.\n",
        "* **Homepage:** https://www.cs.toronto.edu/~kriz/cifar.html\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 103,
          "referenced_widgets": [
            "8fcf2d2cc903419ab3ea4786b633dbe6",
            "f17fb319a22e46df9dfe3ddaa27e8b18",
            "5ea62665015f464abc12a10854be74b3",
            "c3511a44535a4a60a3fe09eec24e5c39",
            "cf1d2f37caa84fa2ac9aea6778fb7cff",
            "c8f3f8927dcc440da0a95252da8365d1",
            "79a63a91b94f440086388093d9125ad3",
            "ad67a988b6b04b63a99f7fbf975505fa",
            "3ddc25c73107413aaba21fb6ab5b9be5",
            "51ec546ab31f49e1894efe55b159b67e",
            "a0b5e458725a45699ad99054366b16f4"
          ]
        },
        "id": "VLgDWQu6a1Gh",
        "outputId": "95fa25e0-c5de-4683-e8a1-4824b475a664"
      },
      "source": [
        "from libauc.datasets import CIFAR100\n",
        "(train_data, train_label) = CIFAR100(root='./data', train=True) \n",
        "(test_data, test_label) = CIFAR100(root='./data', train=False) "
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz to ./data/cifar-100-python.tar.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/169001437 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8fcf2d2cc903419ab3ea4786b633dbe6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/cifar-100-python.tar.gz to ./data\n",
            "Files already downloaded and verified\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t6a4mrJ4a4m1"
      },
      "source": [
        "### CAT_vs_DOG\n",
        "* **Description**: The training archive contains 25,000 images of dogs and cats. Train your algorithm on these files and predict the labels for 1 = dog, 0 = cat.\n",
        "* **Homepage:** https://www.kaggle.com/c/dogs-vs-cats/data\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QN0ZmBp3c5K8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 103,
          "referenced_widgets": [
            "75fce923c6e648e2bcc5d63dd196378a",
            "c3f2374dc901458596a4e540d91cff10",
            "3d6cddd24d9142cf8c0915b685e087a1",
            "210e1b19721b4233b160bfe727fd0864",
            "915784693d0c440fb61734be18b0e4e7",
            "f0a8fc36be0b45eca7434ca3e2b77d93",
            "830cf5038b92489ebe2a53f728c6eb5a",
            "2512d2796e634145b6765fd99682642b",
            "4ca5101bcb744db5aeb9aff5f4adb4ed",
            "efe25953f71b4b7ea02222e36f1f11f8",
            "605587c34d3649f4a474d994486ea523"
          ]
        },
        "outputId": "47b7afaa-2130-4515-de34-23bc56875d88"
      },
      "source": [
        "from libauc.datasets import CAT_VS_DOG\n",
        "\n",
        "(train_data, train_label) = CAT_VS_DOG('./data/', train=True)\n",
        "(test_data, test_label) = CAT_VS_DOG('./data/', train=False)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://homepage.divms.uiowa.edu/~zhuoning/datasets/cat_vs_dog.tar.gz to ./data/cat_vs_dog.tar.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/233417984 [00:00<?, ?it/s]"
            ],
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              "model_id": "75fce923c6e648e2bcc5d63dd196378a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/cat_vs_dog.tar.gz to ./data/\n",
            "Files already downloaded and verified\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZPIMHwJEc8IU"
      },
      "source": [
        "\n",
        "### STL10\n",
        "* **Description**: The STL-10 dataset consists of 5000 96x96 colour images in 10 classes, with 500 images per class. There are 8000 test images, with 800 images per class. \n",
        "* **Homepage:**: https://ai.stanford.edu/~acoates/stl10/\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 103,
          "referenced_widgets": [
            "10dd67b9851e4401a1befe19338d4992",
            "9252941faede4756b337e13388434671",
            "e54dc32c44d94e669694bfd078438ecb",
            "db411617ebee4f83879e7c95dad4c408",
            "2dcf91a8d1f142c3a26ea02bff0c1066",
            "939f47e9f56d4196a82fea222f322a7d",
            "495b42796e644b149f767142e10fd9e3",
            "2dac1c064a0049578251d255e9a80c5b",
            "b8e1b8b797dd48028b908ab622c1d710",
            "1f47d42aa87a4537990555c4c3faf918",
            "8e82ebe5885f40c394bf68a1e15e68fa"
          ]
        },
        "id": "n01-eT0Yc7sm",
        "outputId": "a18c7fba-db58-479c-925d-556b2fa4a841"
      },
      "source": [
        "from libauc.datasets import STL10\n",
        "(train_data, train_label) = STL10(root='./data/', split='train') # return numpy array\n",
        "(test_data, test_label) = STL10(root='./data/', split='test') # return numpy array"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://ai.stanford.edu/~acoates/stl10/stl10_binary.tar.gz to ./data/stl10_binary.tar.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2640397119 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "10dd67b9851e4401a1befe19338d4992"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/stl10_binary.tar.gz to ./data/\n",
            "Files already downloaded and verified\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H0fg05gle6IK"
      },
      "source": [
        "# **03. Constructing Imbalanced Datasets**\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a6qn_PzQfZRH"
      },
      "source": [
        "Import **`ImbalanceDataGenerator`** function, which will help you convert  input dataset to customized imbalanced binary dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fq0wTGP7e5qX"
      },
      "source": [
        "from libauc.utils import ImbalancedDataGenerator"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hXgMrGfif1vL"
      },
      "source": [
        "Set `random_seed=123` and ``imbalance_ratio=0.1``"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vgAX-iwGf9Po"
      },
      "source": [
        "SEED = 123\n",
        "imratio = 0.1 # postive_samples/(total_samples)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fhSh3jy4f-3S"
      },
      "source": [
        "We have the new imbalanced datasets, consisting of 2777 positive images and 25000 negative images for training set. For testing set, we keep them unchanged."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U06_n4Znf1YA",
        "outputId": "790146e2-7e45-4f98-fcd4-9e44ae84debe"
      },
      "source": [
        "from libauc.datasets import CIFAR10\n",
        "(train_data, train_label) = CIFAR10(root='./data', train=True) \n",
        "(test_data, test_label) = CIFAR10(root='./data', train=False) \n",
        "\n",
        "generator = ImbalancedDataGenerator(verbose=True, random_seed=0)\n",
        "(train_images, train_labels) = generator.transform(train_data, train_label, imratio=imratio)\n",
        "(test_images, test_labels) = generator.transform(test_data, test_label, imratio=0.5) \n"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n",
            "#SAMPLES: [27777], POS:NEG: [2777 : 25000], POS RATIO: 0.1000\n",
            "#SAMPLES: [10000], POS:NEG: [5000 : 5000], POS RATIO: 0.5000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fTaTzcgdg3Zz"
      },
      "source": [
        "# **04. Preparing datasets for training with DataLoaders**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MjKOgKKtg5ox"
      },
      "source": [
        "import torch\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import torchvision.transforms as transforms\n",
        "import numpy as np\n",
        "from PIL import Image\n",
        "\n",
        "class ImageDataset(Dataset):\n",
        "    def __init__(self, images, targets, image_size=32, crop_size=30, mode='train'):\n",
        "       self.images = images.astype(np.uint8)\n",
        "       self.targets = targets\n",
        "       self.mode = mode\n",
        "       self.transform_train = transforms.Compose([                                                \n",
        "                              transforms.ToTensor(),\n",
        "                              transforms.RandomCrop((crop_size, crop_size), padding=None),\n",
        "                              transforms.RandomHorizontalFlip(),\n",
        "                              transforms.Resize((image_size, image_size)),\n",
        "                              ])\n",
        "       self.transform_test = transforms.Compose([\n",
        "                             transforms.ToTensor(),\n",
        "                             transforms.Resize((image_size, image_size)),\n",
        "                              ])\n",
        "    def __len__(self):\n",
        "        return len(self.images)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        image = self.images[idx]\n",
        "        target = self.targets[idx]\n",
        "        image = Image.fromarray(image.astype('uint8'))\n",
        "        if self.mode == 'train':\n",
        "            image = self.transform_train(image)\n",
        "        else:\n",
        "            image = self.transform_test(image)\n",
        "        return image, target\n",
        "  \n",
        "\n",
        "trainloader = DataLoader(ImageDataset(train_images, train_labels, mode='train'), batch_size=128, shuffle=True, num_workers=2, pin_memory=True)\n",
        "testloader = DataLoader(ImageDataset(test_images, test_labels, mode='test'), batch_size=128, shuffle=False, num_workers=2,  pin_memory=True)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o7Yuvnn8gb0s"
      },
      "source": [
        "Now, we are ready to train models using the new imbalanced dataset. "
      ]
    }
  ]
}