Deep_Active_Learning_using_DeepAL.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "gpuClass": "standard",
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/esraa9322/Deep_Active_Learning_DeepAL/blob/main/Deep_Active_Learning_using_DeepAL.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Imports ,Installations and Setup**"
      ],
      "metadata": {
        "id": "Lnj2nkyMI1WV"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xss1UcsG_iZ9",
        "outputId": "a780422b-3015-4b37-bc3b-d0e3f125f046"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.0.0+cu118)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (0.15.1+cu118)\n",
            "Requirement already satisfied: torchaudio in /usr/local/lib/python3.10/dist-packages (2.0.1+cu118)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.2)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.1)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.11.1)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch) (2.0.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.12.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch) (4.5.0)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch) (16.0.2)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch) (3.25.2)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision) (8.4.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torchvision) (1.22.4)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from torchvision) (2.27.1)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.2)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (1.26.15)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (3.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (2022.12.7)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n"
          ]
        }
      ],
      "source": [
        "! pip3 install torch torchvision torchaudio "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install utils"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C7Wm-RvT_z6x",
        "outputId": "a86af7aa-c4a7-45d6-b816-bea08999ccb1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: utils in /usr/local/lib/python3.10/dist-packages (1.0.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import sys\n",
        "import numpy as np \n",
        "import pandas as pd\n",
        "import torch\n",
        "import torch.nn.functional as F\n",
        "import torch.nn as nn\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import torchvision\n",
        "import os\n",
        "\n",
        "from random import randrange\n",
        "from functools import partial\n",
        "from torchvision import datasets,transforms\n",
        "from torch import nn\n",
        "from tqdm import tqdm \n",
        "from torch.nn.modules import transformer\n",
        "from torchvision import transforms"
      ],
      "metadata": {
        "id": "gQ86sydTAAQj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/ej0cl6/deep-active-learning"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pIMn8A3NAM9B",
        "outputId": "90466cd9-810c-4688-bac5-02f3f341154e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "fatal: destination path 'deep-active-learning' already exists and is not an empty directory.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')"
      ],
      "metadata": {
        "id": "44kJ9ahhATtK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "os.chdir('/content/deep-active-learning')\n",
        "\n",
        "sys.path.append('/content/deep-active-learning') "
      ],
      "metadata": {
        "id": "lXHla2iGAd2R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open('/content/deep-active-learning/data.py', 'r') as f:\n",
        "    DATA = f.read()\n",
        "exec(DATA)\n",
        "\n",
        "with open('/content/deep-active-learning/handlers.py', 'r') as f:\n",
        "    Handler = f.read()\n",
        "exec(Handler)\n",
        "with open('/content/deep-active-learning/nets.py', 'r') as f:\n",
        "    Nets = f.read()\n",
        "exec(Nets)"
      ],
      "metadata": {
        "id": "nr-xJ86lAlDl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **CIFAR10 Dataset Loading and Visualization**"
      ],
      "metadata": {
        "id": "f63RjiuKKBrT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the following section CIFAR10 dataset is loaded from pytorch built-in libraries, then visualized in a way that showes how balanced it is , then the class CIFAR10NET is initialized which contains the default convolutional architecture for CIFAR10 dataset in deepAL library , also it's most important parameters are initialized  "
      ],
      "metadata": {
        "id": "2O25VYxPTNVX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "trainset = torchvision.datasets.CIFAR10(\n",
        "    root='./data', train=True, download=True, transform=transforms.ToTensor()\n",
        ")\n",
        "\n",
        "testset = torchvision.datasets.CIFAR10(\n",
        "    root='./data', train=False, download=True, transform=transforms.ToTensor() \n",
        ") "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tCIFvfIJAZxw",
        "outputId": "a88439cb-59e8-4f30-9dc3-de3fd98d6c14"
      },
      "execution_count": null,
      "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": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 170498071/170498071 [00:13<00:00, 12926104.95it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/cifar-10-python.tar.gz to ./data\n",
            "Files already downloaded and verified\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "IDX_TO_LABEL = {v: k for k, v in trainset.class_to_idx.items()} "
      ],
      "metadata": {
        "id": "3n5c5UnARUyV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cols = 6\n",
        "rows = 4\n",
        "fig, axs = plt.subplots(rows, cols, figsize=(12, 9))\n",
        "\n",
        "for x in range(rows):\n",
        "    for y in range(cols):\n",
        "        rnd_idx = randrange(len(trainset.data))\n",
        "        \n",
        "        axs[x, y].set_title(IDX_TO_LABEL[trainset.targets[rnd_idx]])\n",
        "        axs[x, y].imshow(trainset.data[rnd_idx])\n",
        "        axs[x, y].set_axis_off()\n",
        "\n",
        "plt.show()   "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 734
        },
        "id": "u9EMsm4SRWwk",
        "outputId": "228313ad-fd43-4cff-88b8-54065ad9f4fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x900 with 24 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(16, 6))\n",
        "\n",
        "sns.countplot(\n",
        "    x=pd.Series(trainset.targets).map(IDX_TO_LABEL), \n",
        "    order=IDX_TO_LABEL.values(),\n",
        ").set_title('Training set distribution')\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "1-fPkG_fRwXQ",
        "outputId": "95d3a84f-78b5-4423-a1b6-e56ba972d9c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(16, 6))\n",
        "\n",
        "sns.countplot(\n",
        "    x=pd.Series(testset.targets).map(IDX_TO_LABEL), \n",
        "    order=IDX_TO_LABEL.values(),\n",
        ").set_title('Test set distribution')\n",
        "\n",
        "plt.show() "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "KC6KO47URzsw",
        "outputId": "a4ac71dd-d7d6-4db8-bae2-0c99ac09495f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#load dataset\n",
        "dataset1=Data(trainset.data[:40000], torch.LongTensor(trainset.targets)[:40000], testset.data[:40000], torch.LongTensor(testset.targets)[:40000], CIFAR10_Handler)"
      ],
      "metadata": {
        "id": "zXIg7fagArA-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class CIFAR10_Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(CIFAR10_Net, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(3, 32, kernel_size=5)\n",
        "        self.conv2 = nn.Conv2d(32, 32, kernel_size=5)\n",
        "        self.conv3 = nn.Conv2d(32, 64, kernel_size=5)\n",
        "        self.fc1 = nn.Linear(1024, 50)\n",
        "        self.fc2 = nn.Linear(50, 10)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = F.relu(self.conv1(x))\n",
        "        x = F.relu(F.max_pool2d(self.conv2(x), 2))\n",
        "        x = F.relu(F.max_pool2d(self.conv3(x), 2))\n",
        "        x = x.view(-1, 1024)\n",
        "        e1 = F.relu(self.fc1(x))\n",
        "        x = F.dropout(e1, training=self.training)\n",
        "        x = self.fc2(x)\n",
        "        return x, e1\n",
        "    def get_embedding_dim(self):\n",
        "        return 50"
      ],
      "metadata": {
        "id": "aYlYLux6Aykd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# defining parameters of the net \n",
        "CIFAR10={'n_epoch': 20, \n",
        "               'train_args':{'batch_size': 64, 'num_workers': 1},\n",
        "               'test_args':{'batch_size': 1000, 'num_workers': 1},\n",
        "               'optimizer_args':{'lr': 0.05, 'momentum': 0.3}}\n",
        "# load network \n",
        "net= Net(CIFAR10_Net, CIFAR10, device)                "
      ],
      "metadata": {
        "id": "K_nvqYvOA92V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Testing different Strategies on CIFAR10 dataset**"
      ],
      "metadata": {
        "id": "sJN2bUxnSdtd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from query_strategies.strategy import Strategy \n",
        "from query_strategies.margin_sampling import MarginSampling \n",
        "from query_strategies.entropy_sampling import EntropySampling\n",
        "from query_strategies.least_confidence import LeastConfidence\n",
        "from query_strategies.random_sampling import RandomSampling"
      ],
      "metadata": {
        "id": "wmcKDFDFUvxv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "strategy1=RandomSampling(dataset1,net)\n",
        "strategy2=MarginSampling(dataset1,net)\n",
        "strategy3=EntropySampling(dataset1,net)\n",
        "strategy4=LeastConfidence(dataset1,net)"
      ],
      "metadata": {
        "id": "QGiDxdPWBMxv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "##### RANDOM SAMPLING STRATEGY\n",
        "for rd in range(1,11):\n",
        "    print(f\"Round {rd}\")\n",
        "\n",
        "    # query\n",
        "    query_idxs = strategy1.query(500)\n",
        "\n",
        "    # update labels\n",
        "    strategy1.update(query_idxs)\n",
        "    strategy1.train()\n",
        "\n",
        "    # calculate accuracy\n",
        "    preds = strategy1.predict(dataset1.get_test_data())\n",
        "    print(f\"Round {rd} testing accuracy: {dataset1.cal_test_acc(preds)}\") "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wxU5tChCFMn3",
        "outputId": "9af06aa5-6237-4465-d632-8ec34090900c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 20/20 [00:11<00:00,  1.73it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 1 testing accuracy: 0.2731\n",
            "Round 2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 20/20 [00:09<00:00,  2.15it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 2 testing accuracy: 0.3442\n",
            "Round 3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 20/20 [00:12<00:00,  1.56it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 3 testing accuracy: 0.3842\n",
            "Round 4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 20/20 [00:16<00:00,  1.23it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 4 testing accuracy: 0.3863\n",
            "Round 5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 20/20 [00:20<00:00,  1.02s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 5 testing accuracy: 0.3583\n",
            "Round 6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 20/20 [00:23<00:00,  1.18s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 6 testing accuracy: 0.4529\n",
            "Round 7\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 20/20 [00:27<00:00,  1.37s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 7 testing accuracy: 0.4706\n",
            "Round 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 20/20 [00:30<00:00,  1.51s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 8 testing accuracy: 0.4851\n",
            "Round 9\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 20/20 [00:32<00:00,  1.65s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 9 testing accuracy: 0.4284\n",
            "Round 10\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 20/20 [00:37<00:00,  1.87s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 10 testing accuracy: 0.5085\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##### MARGIN SAMPLING STRATEGY\n",
        "for rd in range(1,11):\n",
        "    print(f\"Round {rd}\")\n",
        "\n",
        "    # query\n",
        "    query_idxs = strategy2.query(500)\n",
        "\n",
        "    # update labels\n",
        "    strategy2.update(query_idxs)\n",
        "    strategy2.train()\n",
        "\n",
        "    # calculate accuracy\n",
        "    preds = strategy2.predict(dataset1.get_test_data())\n",
        "    print(f\"Round {rd} testing accuracy: {dataset1.cal_test_acc(preds)}\") "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "54gD8PqpFG_C",
        "outputId": "243e5b71-98eb-4c71-c571-0b8662fabe0a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 20/20 [00:40<00:00,  2.04s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 1 testing accuracy: 0.5129\n",
            "Round 2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 20/20 [00:44<00:00,  2.24s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 2 testing accuracy: 0.5471\n",
            "Round 3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 20/20 [00:50<00:00,  2.54s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 3 testing accuracy: 0.5311\n",
            "Round 4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 20/20 [00:56<00:00,  2.84s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 4 testing accuracy: 0.5042\n",
            "Round 5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 20/20 [01:00<00:00,  3.00s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 5 testing accuracy: 0.5249\n",
            "Round 6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 20/20 [01:02<00:00,  3.15s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 6 testing accuracy: 0.5521\n",
            "Round 7\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 20/20 [01:05<00:00,  3.29s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 7 testing accuracy: 0.5776\n",
            "Round 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 20/20 [01:06<00:00,  3.32s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 8 testing accuracy: 0.5408\n",
            "Round 9\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 20/20 [01:09<00:00,  3.49s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 9 testing accuracy: 0.561\n",
            "Round 10\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 20/20 [01:13<00:00,  3.66s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 10 testing accuracy: 0.5675\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##### ENTROPY SAMPLING STRATEGY\n",
        "for rd in range(1,11):\n",
        "    print(f\"Round {rd}\")\n",
        "\n",
        "    # query\n",
        "    query_idxs = strategy3.query(500)\n",
        "\n",
        "    # update labels\n",
        "    strategy3.update(query_idxs)\n",
        "    strategy3.train()\n",
        "\n",
        "    # calculate accuracy\n",
        "    preds = strategy3.predict(dataset1.get_test_data())\n",
        "    print(f\"Round {rd} testing accuracy: {dataset1.cal_test_acc(preds)}\") "
      ],
      "metadata": {
        "id": "k2cDYCDSXhcJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "157ce231-932e-43ee-9288-b7113351de1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 20/20 [01:16<00:00,  3.83s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 1 testing accuracy: 0.4463\n",
            "Round 2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 20/20 [01:19<00:00,  3.96s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 2 testing accuracy: 0.5841\n",
            "Round 3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 20/20 [01:23<00:00,  4.17s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 3 testing accuracy: 0.5685\n",
            "Round 4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 20/20 [01:26<00:00,  4.33s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 4 testing accuracy: 0.5736\n",
            "Round 5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 20/20 [01:29<00:00,  4.48s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 5 testing accuracy: 0.5349\n",
            "Round 6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 20/20 [01:33<00:00,  4.67s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 6 testing accuracy: 0.5795\n",
            "Round 7\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 20/20 [01:36<00:00,  4.82s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 7 testing accuracy: 0.5757\n",
            "Round 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 20/20 [01:40<00:00,  5.04s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 8 testing accuracy: 0.6033\n",
            "Round 9\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 20/20 [01:43<00:00,  5.17s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 9 testing accuracy: 0.6132\n",
            "Round 10\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 20/20 [01:47<00:00,  5.38s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 10 testing accuracy: 0.6036\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##### LEAST CONFIDENCE STRATEGY\n",
        "for rd in range(1,11):\n",
        "    print(f\"Round {rd}\")\n",
        "\n",
        "    # query\n",
        "    query_idxs = strategy4.query(500)\n",
        "\n",
        "    # update labels\n",
        "    strategy4.update(query_idxs)\n",
        "    strategy4.train()\n",
        "\n",
        "    # calculate accuracy\n",
        "    preds = strategy4.predict(dataset1.get_test_data())\n",
        "    print(f\"Round {rd} testing accuracy: {dataset1.cal_test_acc(preds)}\") "
      ],
      "metadata": {
        "id": "ik_Y4mOGX24O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "97404fda-4b7f-4b44-dba2-ebec8ee5df08"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 20/20 [01:51<00:00,  5.55s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 1 testing accuracy: 0.6203\n",
            "Round 2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 20/20 [01:54<00:00,  5.74s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 2 testing accuracy: 0.6085\n",
            "Round 3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 20/20 [01:58<00:00,  5.94s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 3 testing accuracy: 0.6093\n",
            "Round 4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 20/20 [02:01<00:00,  6.08s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 4 testing accuracy: 0.6189\n",
            "Round 5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 20/20 [02:05<00:00,  6.26s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 5 testing accuracy: 0.6063\n",
            "Round 6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 20/20 [02:08<00:00,  6.45s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 6 testing accuracy: 0.5842\n",
            "Round 7\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 20/20 [02:12<00:00,  6.61s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 7 testing accuracy: 0.5703\n",
            "Round 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 20/20 [02:16<00:00,  6.82s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 8 testing accuracy: 0.6277\n",
            "Round 9\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 20/20 [02:19<00:00,  6.98s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 9 testing accuracy: 0.6249\n",
            "Round 10\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 20/20 [02:22<00:00,  7.13s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 10 testing accuracy: 0.6358\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **MNIST Dataset Loading and Visualization**"
      ],
      "metadata": {
        "id": "RHKsBcPlYrw1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#MNIST\n",
        "trainset2 = torchvision.datasets.MNIST(\n",
        "    root='./data', train=True, download=True, transform=transforms.ToTensor()\n",
        ")\n",
        "\n",
        "testset2 = torchvision.datasets.MNIST(\n",
        "    root='./data', train=False, download=True, transform=transforms.ToTensor() \n",
        ") "
      ],
      "metadata": {
        "id": "oF1SSF_VY3WX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d946b20b-2b6e-4867-835e-4a88ed74c1a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 9912422/9912422 [00:00<00:00, 362764477.56it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 28881/28881 [00:00<00:00, 46716426.47it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1648877/1648877 [00:00<00:00, 193138164.56it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 4542/4542 [00:00<00:00, 6096169.21it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "IDX_TO_LABEL2 = {v: k for k, v in trainset2.class_to_idx.items()}"
      ],
      "metadata": {
        "id": "Z3bA497u5od4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(16, 6))\n",
        "\n",
        "sns.countplot(\n",
        "    x=pd.Series(trainset2.targets).map(IDX_TO_LABEL2), \n",
        "    order=IDX_TO_LABEL2.values(),\n",
        ").set_title('Training set distribution')\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "srMXVZ8x7XBN",
        "outputId": "a5b4604b-0c59-48ec-8b6c-9d94addf8836"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(16, 6))\n",
        "\n",
        "sns.countplot(\n",
        "    x=pd.Series(testset2.targets).map(IDX_TO_LABEL2), \n",
        "    order=IDX_TO_LABEL2.values(),\n",
        ").set_title('Test set distribution')\n",
        "\n",
        "plt.show() "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 565
        },
        "id": "oGYpRvgr7scB",
        "outputId": "86d39f88-5fb4-472c-c178-f90608dc3950"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import data"
      ],
      "metadata": {
        "id": "DrNsftuUVi0o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset2b=get_MNIST(MNIST_Handler)"
      ],
      "metadata": {
        "id": "uJjKIDhdVm_X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class MNIST_Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(MNIST_Net, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)\n",
        "        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)\n",
        "        self.conv2_drop = nn.Dropout2d()\n",
        "        self.fc1 = nn.Linear(320, 50)\n",
        "        self.fc2 = nn.Linear(50, 10)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = F.relu(F.max_pool2d(self.conv1(x), 2))\n",
        "        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))\n",
        "        x = x.view(-1, 320)\n",
        "        e1 = F.relu(self.fc1(x))\n",
        "        x = F.dropout(e1, training=self.training)\n",
        "        x = self.fc2(x)\n",
        "        return x, e1\n",
        "    def get_embedding_dim(self):\n",
        "        return 50"
      ],
      "metadata": {
        "id": "bOPkfCgyEXDN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#initializing net parameters\n",
        "MNIST=     {'n_epoch': 10, \n",
        "               'train_args':{'batch_size': 64, 'num_workers': 1},\n",
        "               'test_args':{'batch_size': 1000, 'num_workers': 1},\n",
        "               'optimizer_args':{'lr': 0.01, 'momentum': 0.5}}"
      ],
      "metadata": {
        "id": "MrjSyURrFsO6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "net2= Net(MNIST_Net, MNIST, device)"
      ],
      "metadata": {
        "id": "brAft3fGSCha"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Testing different strategies on MNIST dataset**"
      ],
      "metadata": {
        "id": "FAwFr-F2hkff"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "strategy1b=RandomSampling(dataset2b,net2)\n",
        "strategy2b=MarginSampling(dataset2b,net2)\n",
        "strategy3b=EntropySampling(dataset2b,net2)\n",
        "strategy4b=LeastConfidence(dataset2b,net2)"
      ],
      "metadata": {
        "id": "NFaNAkeOR9Yv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "##### RANDOM SAMPLING STRATEGY\n",
        "for rd in range(1,11):\n",
        "    print(f\"Round {rd}\")\n",
        "\n",
        "    # query\n",
        "    query_idxs = strategy1b.query(500)\n",
        "\n",
        "    # update labels\n",
        "    strategy1b.update(query_idxs)\n",
        "    strategy1b.train()\n",
        "\n",
        "    # calculate accuracy\n",
        "    preds = strategy1b.predict(dataset2b.get_test_data())\n",
        "    print(f\"Round {rd} testing accuracy: {dataset2b.cal_test_acc(preds)}\") "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JEO5MfXDSiV_",
        "outputId": "9676b7e1-0957-4df8-bc98-cf9d3cdb6fae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 10/10 [00:02<00:00,  3.90it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 1 testing accuracy: 0.5307\n",
            "Round 2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 10/10 [00:04<00:00,  2.20it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 2 testing accuracy: 0.7951\n",
            "Round 3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 10/10 [00:05<00:00,  1.83it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 3 testing accuracy: 0.8606\n",
            "Round 4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 10/10 [00:07<00:00,  1.38it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 4 testing accuracy: 0.8764\n",
            "Round 5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 10/10 [00:10<00:00,  1.03s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 5 testing accuracy: 0.9085\n",
            "Round 6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 10/10 [00:10<00:00,  1.01s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 6 testing accuracy: 0.9271\n",
            "Round 7\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 10/10 [00:12<00:00,  1.22s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 7 testing accuracy: 0.9201\n",
            "Round 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 10/10 [00:13<00:00,  1.33s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 8 testing accuracy: 0.9286\n",
            "Round 9\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 10/10 [00:15<00:00,  1.54s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 9 testing accuracy: 0.937\n",
            "Round 10\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 10/10 [00:16<00:00,  1.62s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 10 testing accuracy: 0.9286\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##### MARGIN SAMPLING STRATEGY\n",
        "for rd in range(1,11):\n",
        "    print(f\"Round {rd}\")\n",
        "\n",
        "    # query\n",
        "    query_idxs = strategy2b.query(500)\n",
        "\n",
        "    # update labels\n",
        "    strategy2b.update(query_idxs)\n",
        "    strategy2b.train()\n",
        "\n",
        "    # calculate accuracy\n",
        "    preds = strategy2b.predict(dataset2b.get_test_data())\n",
        "    print(f\"Round {rd} testing accuracy: {dataset2b.cal_test_acc(preds)}\") "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l0dklaHgWfmS",
        "outputId": "3981c4da-f52a-4cf3-9360-7b4b2b2e4c61"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 10/10 [00:17<00:00,  1.78s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 1 testing accuracy: 0.9488\n",
            "Round 2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 10/10 [00:24<00:00,  2.48s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 2 testing accuracy: 0.9521\n",
            "Round 3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 10/10 [00:22<00:00,  2.23s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 3 testing accuracy: 0.9642\n",
            "Round 4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 10/10 [00:23<00:00,  2.31s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 4 testing accuracy: 0.9596\n",
            "Round 5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 10/10 [00:24<00:00,  2.41s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 5 testing accuracy: 0.9664\n",
            "Round 6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 10/10 [00:25<00:00,  2.54s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 6 testing accuracy: 0.9679\n",
            "Round 7\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 10/10 [00:26<00:00,  2.67s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 7 testing accuracy: 0.9715\n",
            "Round 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 10/10 [00:29<00:00,  2.91s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 8 testing accuracy: 0.9656\n",
            "Round 9\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 10/10 [00:30<00:00,  3.03s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 9 testing accuracy: 0.9745\n",
            "Round 10\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 10/10 [00:31<00:00,  3.20s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 10 testing accuracy: 0.9741\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##### ENTROPY SAMPLING STRATEGY\n",
        "for rd in range(1,11):\n",
        "    print(f\"Round {rd}\")\n",
        "\n",
        "    # query\n",
        "    query_idxs = strategy3b.query(500)\n",
        "\n",
        "    # update labels\n",
        "    strategy3b.update(query_idxs)\n",
        "    strategy3b.train()\n",
        "\n",
        "    # calculate accuracy\n",
        "    preds = strategy3b.predict(dataset2b.get_test_data())\n",
        "    print(f\"Round {rd} testing accuracy: {dataset2b.cal_test_acc(preds)}\") "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gk_F8GdFWxbl",
        "outputId": "ee2c7538-4c61-4ebb-cd56-18312f6f8056"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 10/10 [00:33<00:00,  3.31s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 1 testing accuracy: 0.9714\n",
            "Round 2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 10/10 [00:35<00:00,  3.57s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 2 testing accuracy: 0.9792\n",
            "Round 3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 10/10 [00:35<00:00,  3.56s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 3 testing accuracy: 0.9774\n",
            "Round 4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 10/10 [00:36<00:00,  3.68s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 4 testing accuracy: 0.9756\n",
            "Round 5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 10/10 [00:39<00:00,  3.91s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 5 testing accuracy: 0.9757\n",
            "Round 6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 10/10 [00:41<00:00,  4.14s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 6 testing accuracy: 0.9782\n",
            "Round 7\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 10/10 [00:42<00:00,  4.23s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 7 testing accuracy: 0.9792\n",
            "Round 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 10/10 [00:43<00:00,  4.37s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 8 testing accuracy: 0.9804\n",
            "Round 9\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 10/10 [00:44<00:00,  4.47s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 9 testing accuracy: 0.9792\n",
            "Round 10\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 10/10 [00:46<00:00,  4.61s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 10 testing accuracy: 0.9769\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##### LEAST CONFIDENCE STRATEGY\n",
        "for rd in range(1,11):\n",
        "    print(f\"Round {rd}\")\n",
        "\n",
        "    # query\n",
        "    query_idxs = strategy4b.query(500)\n",
        "\n",
        "    # update labels\n",
        "    strategy4b.update(query_idxs)\n",
        "    strategy4b.train()\n",
        "\n",
        "    # calculate accuracy\n",
        "    preds = strategy4b.predict(dataset2b.get_test_data())\n",
        "    print(f\"Round {rd} testing accuracy: {dataset2b.cal_test_acc(preds)}\") "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kNPQ9-0lWx0Q",
        "outputId": "2e2d357e-0cf2-45ab-d866-a82ee4b4520a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 10/10 [00:02<00:00,  4.09it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 1 testing accuracy: 0.2979\n",
            "Round 2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 10/10 [00:04<00:00,  2.37it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 2 testing accuracy: 0.5479\n",
            "Round 3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 10/10 [00:05<00:00,  1.72it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 3 testing accuracy: 0.7123\n",
            "Round 4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 10/10 [00:06<00:00,  1.49it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 4 testing accuracy: 0.8175\n",
            "Round 5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 10/10 [00:09<00:00,  1.06it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 5 testing accuracy: 0.8482\n",
            "Round 6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 10/10 [00:10<00:00,  1.01s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 6 testing accuracy: 0.8981\n",
            "Round 7\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 10/10 [00:11<00:00,  1.16s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 7 testing accuracy: 0.9121\n",
            "Round 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 10/10 [00:13<00:00,  1.31s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 8 testing accuracy: 0.9253\n",
            "Round 9\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 10/10 [00:14<00:00,  1.44s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 9 testing accuracy: 0.9459\n",
            "Round 10\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 10/10 [00:15<00:00,  1.59s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round 10 testing accuracy: 0.9447\n"
          ]
        }
      ]
    }
  ]
}