{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Deep Pool-based Active Learning: Scikit-activeml with Skorch" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> **_Google Colab Note:_** If the notebook fails to run after installing the needed packages, try to restart the runtime (Ctrl + M) under Runtime -> Restart session.\n", "\n", "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/scikit-activeml/scikit-activeml-docs/blob/gh-pages/latest/generated/tutorials_colab//01_deep_pool_al_with_skorch.ipynb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Notebook Dependencies**\n", "\n", "Uncomment the following cell to install all dependencies for this tutorial." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# !pip install scikit-activeml skorch torch torchvision torchaudio tqdm pandas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this brief tutorial, we show an example use-case of our package `skactiveml` with the Python package [skorch](https://skorch.readthedocs.io/en/stable/), which is a scikit-learn wrapper for Pytorch models. This way, we are able to implement and test deep learning models in combination with query strategies implemented in our framework." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import matplotlib as mlp\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import torch\n", "import warnings\n", "\n", "from copy import deepcopy\n", "from skactiveml.classifier import SklearnClassifier\n", "from skactiveml.pool import UncertaintySampling, QueryByCommittee, RandomSampling, BatchBALD, DiscriminativeAL\n", "from skactiveml.utils import call_func, unlabeled_indices\n", "from sklearn.datasets import fetch_openml\n", "from sklearn.ensemble import VotingClassifier\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import StandardScaler\n", "from skorch import NeuralNetClassifier\n", "from torch import nn\n", "import torch.nn.functional as F\n", "from torchvision.transforms import transforms\n", "from tqdm import tqdm\n", "\n", "mlp.rcParams[\"figure.facecolor\"] = \"white\"\n", "\n", "MISSING_LABEL = -1\n", "RANDOM_STATE = 0\n", "FONTSIZE = 12\n", "\n", "np.random.seed(RANDOM_STATE)\n", "torch.manual_seed(RANDOM_STATE)\n", "torch.cuda.manual_seed(RANDOM_STATE)\n", "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n", "\n", "warnings.filterwarnings(\"ignore\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading Digit Data Set\n", "For simplicity, we use the `sklearn` function `fetch_openml` to load the MNIST dataset. The dataset contains 28 x 28px images of handwritten digits from 0 to 9 and the task is to recognize the digits in the images." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Load digit data set.\n", "X, y_true = fetch_openml(\"mnist_784\", version=1, return_X_y=True, as_frame=False)\n", "\n", "# Visualize first 10 images.\n", "fig, axes = plt.subplots(figsize=(15, 5), nrows=1, ncols=10)\n", "for i in range(10):\n", " axes[i].set_title(f'Digit: {y_true[i]}', fontsize=FONTSIZE)\n", " axes[i].imshow(X[i].reshape(28,28))\n", "plt.show()\n", "\n", "# Standardize data.\n", "X = StandardScaler().fit_transform(X)\n", "\n", "# Reshape samples to n_samples x n_channels x width x height to fit skorch\n", "# requirements.\n", "X = X.reshape((len(X), 1, 28, 28))\n", "\n", "# Set data types according to skorch requirements.\n", "X, y_true = X.astype(np.float32), y_true.astype(np.int64)\n", "\n", "# Identify list of possible classes.\n", "classes = np.unique(y_true)\n", "\n", "# Make a 66-34 train-test split.\n", "X_train, X_test, y_train, y_test = train_test_split(\n", " X, y_true, train_size=0.66, random_state=RANDOM_STATE\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Augmentation\n", "Data augmentation is used to artificially generate data from existing data points. In case of image data, this can be done by rotating or scaling the image. Other basic transformations are:\n", "* padding,\n", "* vertical and horizontal flipping,\n", "* translation,\n", "* cropping,\n", "* darkening, brightening, color modification,\n", "* grayscaling,\n", "* changing contrast,\n", "* adding noise,\n", "* random erasing.\n", "\n", "For more information on data augmentation take a look at the [Data Augmentation Tutorial](https://www.tensorflow.org/tutorials/images/data_augmentation) of [TensorFlow](https://www.tensorflow.org) or visit [Transforming And Augmenting Images](https://pytorch.org/vision/stable/transforms.html) on [PyTorch](pytorch.org)." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "transform = transforms.Compose(\n", " [\n", " transforms.RandomAffine(degrees=(-10, 10), translate=(0.05, 0.05), scale=(0.9, 1.1)),\n", " transforms.RandomPerspective(distortion_scale=0.2, p=1.0),\n", " transforms.GaussianBlur(kernel_size=(3, 3), sigma=(0.01, 0.1)),\n", " ]\n", ")\n", "\n", "\n", "# Visualize 30 augmented images.\n", "for i in range(3):\n", " X_aug = transform(torch.from_numpy(X_train[:10]))\n", " fig, axes = plt.subplots(figsize=(15, 5), nrows=1, ncols=10)\n", " for j in range(10):\n", " if j==0:\n", " axes[j].set_title(f'Original', fontsize=FONTSIZE)\n", " axes[j].imshow(X_train[i].reshape(28,28))\n", " else:\n", " axes[j].set_title(f'Augmented', fontsize=FONTSIZE)\n", " axes[j].imshow(transform(torch.from_numpy(X_train[i])).reshape(28,28))\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Convolutional Neural Network Ensemble\n", "In the next step, we define a convolutional neural network (CNN) ensemble consisting of ten base CNNs with different initializations." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Define base module.\n", "class ClassifierModule(nn.Module):\n", " def __init__(self, n_classes, dropout=0.5, transform=None):\n", " super(ClassifierModule, self).__init__()\n", " self.conv1 = nn.Conv2d(1, 32, kernel_size=3)\n", " self.conv2 = nn.Conv2d(32, 64, kernel_size=3)\n", " self.conv2_drop = nn.Dropout2d(p=dropout)\n", " self.fc1 = nn.Linear(1600, 100) # 1600 = number channels * width * height\n", " self.fc2 = nn.Linear(100, n_classes)\n", " self.fc1_drop = nn.Dropout(p=dropout)\n", " self.transform = transform or transforms.Compose([])\n", "\n", " def forward(self, x):\n", " if self.training:\n", " # Usually, transforms are part of the datasets.\n", " with torch.no_grad():\n", " x = self.transform(x)\n", " x = torch.relu(F.max_pool2d(self.conv1(x), 2))\n", " x = torch.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))\n", "\n", " # flatten over channel, height and width = 1600\n", " x = x.view(x.size(0), -1)\n", "\n", " x = torch.relu(self.fc1_drop(self.fc1(x)))\n", " x = torch.softmax(self.fc2(x), dim=-1)\n", " return x\n", "\n", "# Create list of ten base CNNs.\n", "estimators = []\n", "for i in range(5):\n", " net = NeuralNetClassifier(\n", " ClassifierModule,\n", " module__n_classes=10,\n", " max_epochs=100,\n", " batch_size=128,\n", " lr=0.01,\n", " verbose=False,\n", " optimizer=torch.optim.SGD,\n", " optimizer__momentum=0.9,\n", " optimizer__weight_decay=3e-3,\n", " train_split=False,\n", " device=device,\n", " )\n", " net.initialize()\n", " clf = SklearnClassifier(\n", " estimator=net,\n", " missing_label=MISSING_LABEL,\n", " random_state=i,\n", " classes=classes,\n", " )\n", " estimators.append((f'clf {i}', clf))\n", "\n", "# Creat voting ensemble out of given ensemble list.\n", "ensemble_init = SklearnClassifier(\n", " estimator=VotingClassifier(estimators=estimators, voting='soft'),\n", " missing_label=MISSING_LABEL,\n", " random_state=RANDOM_STATE,\n", " classes=classes,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Active Classification\n", "For our ensemble, we evaluate four different query strategies, i.e., random sampling, uncertainty sampling, query-by-committee, and batch BALD, regarding their sample selection. For this purpose, we start with ten labels and make 30 iterations of an active learning cycle with a batch size of 32." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Execute active learning using random sampling.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 30/30 [04:05<00:00, 8.18s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Execute active learning using uncertainty sampling.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 30/30 [04:08<00:00, 8.28s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Execute active learning using query-by-committee.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 30/30 [04:09<00:00, 8.32s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Execute active learning using batch BALD.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 30/30 [04:05<00:00, 8.19s/it]\n" ] } ], "source": [ "# Define setup.\n", "n_cycles = 30\n", "batch_size = 32\n", "n_sub_set = 1000\n", "qs_dict = {\n", " 'random sampling': RandomSampling(random_state=RANDOM_STATE, missing_label=MISSING_LABEL),\n", " 'uncertainty sampling': UncertaintySampling(random_state=RANDOM_STATE, missing_label=MISSING_LABEL),\n", " 'query-by-committee': QueryByCommittee(random_state=RANDOM_STATE, missing_label=MISSING_LABEL),\n", " 'batch-bald': BatchBALD(random_state=RANDOM_STATE, missing_label=MISSING_LABEL),\n", "}\n", "acc_dict = {key: np.zeros(n_cycles + 1) for key in qs_dict}\n", "\n", "# Perform active learning with each query strategy.\n", "for qs_name, qs in qs_dict.items():\n", " torch.manual_seed(RANDOM_STATE)\n", " torch.cuda.manual_seed(RANDOM_STATE)\n", "\n", " print(f'Execute active learning using {qs_name}.')\n", "\n", " # Copy initial ensemble model.\n", " ensemble = deepcopy(ensemble_init)\n", "\n", " # Create array of missing labels as initial labels.\n", " y = np.full_like(y_train, fill_value=MISSING_LABEL, dtype=np.int64)\n", " X = X_train\n", "\n", " # Label one sample from each class for warm start.\n", " for i in range(10):\n", " y[np.argwhere(y_train==i)[0]] = i\n", "\n", " # Execute active learning cycle.\n", " for c in tqdm(range(n_cycles)):\n", " # Fit and evaluate ensemble.\n", " acc = ensemble.fit(X, y).score(X_test, y_test)\n", " acc_dict[qs_name][c] = acc\n", "\n", " ulbd_indices = np.random.choice(unlabeled_indices(y, MISSING_LABEL), n_sub_set)\n", "\n", " # Select and update training data.\n", " query_idx = call_func(\n", " qs.query,\n", " X=X[ulbd_indices],\n", " y=y[ulbd_indices],\n", " clf=ensemble,\n", " fit_clf=False,\n", " ensemble=ensemble,\n", " fit_ensemble=False,\n", " batch_size=batch_size,\n", " )\n", " query_idx = ulbd_indices[query_idx]\n", " y[query_idx] = y_train[query_idx]\n", "\n", " # Fit and evaluate ensemble.\n", " ensemble.fit(X, y)\n", " acc_dict[qs_name][n_cycles] = ensemble.score(X_test, y_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualize Results\n", "In the following, we plot the obtained learning curves including the area under learning curve (AULC) scores per query strategy." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cycles = np.arange(n_cycles + 1, dtype=int)\n", "plt.figure(figsize=(16, 9))\n", "for qs_name, acc in acc_dict.items():\n", " plt.plot(cycles, acc, label=f'{qs_name}: AULC={round(acc.mean(), 2)}')\n", "plt.xticks(cycles, fontsize=FONTSIZE)\n", "plt.yticks(fontsize=FONTSIZE)\n", "plt.xlabel('# cycle', fontsize=FONTSIZE)\n", "plt.ylabel('test accuracy', fontsize=FONTSIZE)\n", "plt.legend(loc='lower right', fontsize='x-large')\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.9" } }, "nbformat": 4, "nbformat_minor": 4 }