examples/notebooks/02-active-learning-with-stopping-criteria.ipynb

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    "# Active Learning for Text Classification using Small-Text: Stopping Criteria\n",
    "\n",
    "*Notebook 2*  \n",
    "This tutorial shows how to use stopping criteria when performing active learning for text classification using [small-text](https://github.com/webis-de/small-text). If you have read the previous notebook already, you can also directly jump to the [relevant section regarding stopping criteria](#nb2-part4-stopping).\n",
    "\n",
    "----\n",
    "\n",
    "## Overview\n",
    "\n",
    "- [Part I: Installation](#nb2-part1-installation)\n",
    "- [Part II: Loading and Preparing the Data](#nb2-part2-data)\n",
    "- [Part III: Active Learning](#nb2-part3-active-learning)\n",
    "- [Part IV: Stopping Criterion](#nb2-part4-stopping)\n",
    "- [Part V: Visualization](#nb2-part5-visualization)\n",
    "\n",
    "----\n",
    "<a id=\"nb2-part1-installation\"></a>\n",
    "## I. Installation\n",
    "\n",
    "Besides small-text, we also install [datasets](https://github.com/huggingface/datasets) to load an example dataset and [matptlotlib](https://matplotlib.org/) to plot the learning curves at the end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cf5355d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mDEPRECATION: git+https://github.com/webis-de/small-text.git@v1.4.x#egg=small-text[transformers] contains an egg fragment with a non-PEP 508 name pip 25.0 will enforce this behaviour change. A possible replacement is to use the req @ url syntax, and remove the egg fragment. Discussion can be found at https://github.com/pypa/pip/issues/11617\u001b[0m\u001b[33m\n",
      "\u001b[0mCollecting small-text[transformers]\n",
      "  Cloning https://github.com/webis-de/small-text.git (to revision v1.4.x) to /tmp/pip-install-jqcghh9f/small-text_c224a8e235f04e349ce325ef5180ca7c\n",
      "  Running command git clone --filter=blob:none --quiet https://github.com/webis-de/small-text.git /tmp/pip-install-jqcghh9f/small-text_c224a8e235f04e349ce325ef5180ca7c\n",
      "  Running command git checkout -b v1.4.x --track origin/v1.4.x\n",
      "  Switched to a new branch 'v1.4.x'\n",
      "  branch 'v1.4.x' set up to track 'origin/v1.4.x'.\n",
      "  Resolved https://github.com/webis-de/small-text.git to commit 1193e540f1317ef589393541b428b2cb4716cc3a\n",
      "  Installing build dependencies ... \u001b[?25ldone\n",
      "\u001b[?25h  Getting requirements to build wheel ... \u001b[?25ldone\n",
      "\u001b[?25h  Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n",
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      "\n",
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      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3.9 -m pip install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
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      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3.9 -m pip install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install small-text[transformers]==1.4.1\n",
    "\n",
    "# additional dependencies for this example\n",
    "%pip install datasets matplotlib \"seaborn>=0.13.0\" pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7813fe3",
   "metadata": {},
   "source": [
    "The code in the following cell sets the logging level and progress bars to make it suitable for notebooks. Moreover, we set a fixed random seed, so you get the same output every time you run this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cd157fbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datasets\n",
    "import torch\n",
    "import warnings\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from matplotlib import rcParams\n",
    "\n",
    "datasets.logging.set_verbosity_error()\n",
    "\n",
    "# disables the progress bar for notebooks: https://github.com/huggingface/datasets/issues/2651\n",
    "datasets.logging.get_verbosity = lambda: logging.NOTSET\n",
    "\n",
    "# set matplotlib params\n",
    "rcParams.update({'xtick.labelsize': 14, 'ytick.labelsize': 14, 'axes.labelsize': 16})\n",
    "\n",
    "# fix the random seed\n",
    "seed = 2022\n",
    "torch.manual_seed(seed)\n",
    "np.random.seed(seed)\n",
    "\n",
    "warnings.filterwarnings(action='ignore', category=FutureWarning, message=r'.*`clean_up_tokenization_spaces` was not set..*')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a52b467",
   "metadata": {},
   "source": [
    "----\n",
    "\n",
    "<a id=\"nb2-part2-data\"></a>\n",
    "## II. Loading and Preparing the Data\n",
    "\n",
    "Again, we load the rotten tomatoes dataset and build a TransformersDataset object which is then used for active learning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a894d142",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cschroeder/.pyenv/versions/3.9.18/envs/notebooks/lib/python3.9/site-packages/small_text/utils/annotations.py:67: ExperimentalWarning: The function from_arrays is experimental and maybe subject to change soon.\n",
      "  warnings.warn(f'The {subject} {func_or_class.__name__} is experimental '\n"
     ]
    }
   ],
   "source": [
    "import logging\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "from transformers import AutoTokenizer\n",
    "\n",
    "from small_text import TransformersDataset\n",
    "\n",
    "\n",
    "raw_dataset = datasets.load_dataset('rotten_tomatoes')\n",
    "num_classes = raw_dataset['train'].features['label'].num_classes\n",
    "\n",
    "transformer_model_name = 'bert-base-uncased'\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(\n",
    "    transformer_model_name\n",
    ")\n",
    "\n",
    "\n",
    "target_labels = np.arange(num_classes)\n",
    "\n",
    "train = TransformersDataset.from_arrays(raw_dataset['train']['text'],\n",
    "                                        raw_dataset['train']['label'],\n",
    "                                        tokenizer,\n",
    "                                        max_length=60,\n",
    "                                        target_labels=target_labels)\n",
    "test = TransformersDataset.from_arrays(raw_dataset['test']['text'], \n",
    "                                       raw_dataset['test']['label'],\n",
    "                                       tokenizer,\n",
    "                                       max_length=60,\n",
    "                                       target_labels=target_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e75ff407",
   "metadata": {},
   "source": [
    "----\n",
    "\n",
    "<a id=\"nb2-part3-active-learning\"></a>\n",
    "## III. Active Learning\n",
    "\n",
    "Now we set up an active learning example that uses transformer models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "92610a70",
   "metadata": {},
   "outputs": [],
   "source": [
    "from small_text import (\n",
    "    PoolBasedActiveLearner,\n",
    "    PredictionEntropy,\n",
    "    TransformerBasedClassificationFactory,\n",
    "    TransformerModelArguments,\n",
    "    random_initialization_balanced\n",
    ")\n",
    "\n",
    "\n",
    "# simulates an initial labeling to warm-start the active learning process\n",
    "def initialize_active_learner(active_learner, y_train):\n",
    "\n",
    "    indices_initial = random_initialization_balanced(y_train, n_samples=20)\n",
    "    active_learner.initialize_data(indices_initial, y_train[indices_initial])\n",
    "\n",
    "    return indices_initial\n",
    "\n",
    "\n",
    "transformer_model = TransformerModelArguments(transformer_model_name)\n",
    "clf_factory = TransformerBasedClassificationFactory(transformer_model, \n",
    "                                                    num_classes, \n",
    "                                                    kwargs={\n",
    "                                                        'device': 'cuda', \n",
    "                                                        'mini_batch_size': 32,\n",
    "                                                        'class_weight': 'balanced'\n",
    "                                                    })\n",
    "query_strategy = PredictionEntropy()\n",
    "\n",
    "active_learner = PoolBasedActiveLearner(clf_factory, query_strategy, train)\n",
    "indices_labeled = initialize_active_learner(active_learner, train.y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdf91e0e",
   "metadata": {},
   "source": [
    "----\n",
    "\n",
    "<a id=\"nb2-part4-stopping\"></a>\n",
    "## IV. Stopping Criterion\n",
    "\n",
    "Besides, we slightly change the evaluation: in order to make the setting more realistic, we do not evaluate on the test accuracy any longer, since you will not have a test set in real-world situations. \n",
    "\n",
    "Unlike before, we now also output the stopping response (True or False). The stopping criteria are not bound to the PoolBasedActiveLearner class because in real-world applications a user should have the final say whether the active learning process is stopped or not. Stopping criteria are intended to be an indicator, whose recommendation can be accepted or rejected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8721e2ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy: 0.65\n",
      "Test accuracy: 0.53\n",
      "---------------\n",
      "Iteration #0 (40 samples)\n",
      "Train accuracy: 0.90\n",
      "Test accuracy: 0.52\n",
      "Stop: False\n",
      "---------------\n",
      "Iteration #1 (60 samples)\n",
      "Train accuracy: 0.92\n",
      "Test accuracy: 0.60\n",
      "Stop: False\n",
      "---------------\n",
      "Iteration #2 (80 samples)\n",
      "Train accuracy: 0.99\n",
      "Test accuracy: 0.68\n",
      "Stop: False\n",
      "---------------\n",
      "Iteration #3 (100 samples)\n",
      "Train accuracy: 0.97\n",
      "Test accuracy: 0.73\n",
      "Stop: False\n",
      "---------------\n",
      "Iteration #4 (120 samples)\n",
      "Train accuracy: 0.78\n",
      "Test accuracy: 0.59\n",
      "Stop: False\n",
      "---------------\n",
      "Iteration #5 (140 samples)\n",
      "Train accuracy: 0.95\n",
      "Test accuracy: 0.70\n",
      "Stop: False\n",
      "---------------\n",
      "Iteration #6 (160 samples)\n",
      "Train accuracy: 0.96\n",
      "Test accuracy: 0.72\n",
      "Stop: False\n",
      "---------------\n",
      "Iteration #7 (180 samples)\n",
      "Train accuracy: 0.96\n",
      "Test accuracy: 0.71\n",
      "Stop: False\n",
      "---------------\n",
      "Iteration #8 (200 samples)\n",
      "Train accuracy: 0.95\n",
      "Test accuracy: 0.78\n",
      "Stop: False\n",
      "---------------\n",
      "Iteration #9 (220 samples)\n",
      "Train accuracy: 0.98\n",
      "Test accuracy: 0.77\n",
      "Stop: False\n",
      "---------------\n",
      "Iteration #10 (240 samples)\n",
      "Train accuracy: 0.97\n",
      "Test accuracy: 0.80\n",
      "Stop: False\n",
      "---------------\n",
      "Iteration #11 (260 samples)\n",
      "Train accuracy: 0.95\n",
      "Test accuracy: 0.77\n",
      "Stop: False\n",
      "---------------\n",
      "Iteration #12 (280 samples)\n",
      "Train accuracy: 0.98\n",
      "Test accuracy: 0.79\n",
      "Stop: False\n",
      "---------------\n",
      "Iteration #13 (300 samples)\n",
      "Train accuracy: 0.96\n",
      "Test accuracy: 0.79\n",
      "Stop: False\n",
      "---------------\n",
      "Iteration #14 (320 samples)\n",
      "Train accuracy: 0.97\n",
      "Test accuracy: 0.79\n",
      "Stop: False\n",
      "---------------\n",
      "Iteration #15 (340 samples)\n",
      "Train accuracy: 0.97\n",
      "Test accuracy: 0.82\n",
      "Stop: False\n",
      "---------------\n",
      "Iteration #16 (360 samples)\n",
      "Train accuracy: 0.96\n",
      "Test accuracy: 0.82\n",
      "Stop: False\n",
      "---------------\n",
      "Iteration #17 (380 samples)\n",
      "Train accuracy: 0.98\n",
      "Test accuracy: 0.81\n",
      "Stop: True\n",
      "---------------\n",
      "Iteration #18 (400 samples)\n",
      "Train accuracy: 0.98\n",
      "Test accuracy: 0.82\n",
      "Stop: True\n",
      "---------------\n",
      "Iteration #19 (420 samples)\n",
      "Train accuracy: 0.98\n",
      "Test accuracy: 0.82\n",
      "Stop: True\n",
      "---------------\n",
      "Iteration #20 (440 samples)\n",
      "Train accuracy: 0.97\n",
      "Test accuracy: 0.83\n",
      "Stop: True\n",
      "---------------\n",
      "Iteration #21 (460 samples)\n",
      "Train accuracy: 0.97\n",
      "Test accuracy: 0.82\n",
      "Stop: True\n",
      "---------------\n",
      "Iteration #22 (480 samples)\n",
      "Train accuracy: 0.97\n",
      "Test accuracy: 0.81\n",
      "Stop: True\n",
      "---------------\n",
      "Iteration #23 (500 samples)\n",
      "Train accuracy: 0.98\n",
      "Test accuracy: 0.82\n",
      "Stop: False\n",
      "---------------\n",
      "Iteration #24 (520 samples)\n",
      "Train accuracy: 0.97\n",
      "Test accuracy: 0.82\n",
      "Stop: False\n",
      "---------------\n",
      "Iteration #25 (540 samples)\n",
      "Train accuracy: 0.95\n",
      "Test accuracy: 0.80\n",
      "Stop: False\n",
      "---------------\n",
      "Iteration #26 (560 samples)\n",
      "Train accuracy: 0.96\n",
      "Test accuracy: 0.82\n",
      "Stop: False\n",
      "---------------\n",
      "Iteration #27 (580 samples)\n",
      "Train accuracy: 0.97\n",
      "Test accuracy: 0.82\n",
      "Stop: False\n",
      "---------------\n",
      "Iteration #28 (600 samples)\n",
      "Train accuracy: 0.96\n",
      "Test accuracy: 0.83\n",
      "Stop: True\n",
      "---------------\n",
      "Iteration #29 (620 samples)\n",
      "Train accuracy: 0.97\n",
      "Test accuracy: 0.81\n",
      "Stop: True\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from small_text import KappaAverage\n",
    "\n",
    "\n",
    "num_queries = 30\n",
    "stopping_criterion = KappaAverage(num_classes, kappa=0.8)\n",
    "\n",
    "\n",
    "def evaluate(active_learner, train, test):\n",
    "    y_pred = active_learner.classifier.predict(train)\n",
    "    y_pred_test = active_learner.classifier.predict(test)\n",
    "    \n",
    "    # Notice: We observe the train accuracy now.\n",
    "    train_acc = accuracy_score(y_pred, train.y)\n",
    "\n",
    "    print('Train accuracy: {:.2f}'.format(train_acc))\n",
    "    print('Test accuracy: {:.2f}'.format(accuracy_score(y_pred_test, test.y)))\n",
    "    \n",
    "    return train_acc\n",
    "\n",
    "\n",
    "results = []\n",
    "stopping_history = []\n",
    "\n",
    "results.append(evaluate(active_learner, train[indices_labeled], test))\n",
    "stopping_history.append(stopping_criterion.stop(predictions=active_learner.classifier.predict(train)))\n",
    "\n",
    "\n",
    "for i in range(num_queries):\n",
    "    # ...where each iteration consists of labelling 20 samples\n",
    "    indices_queried = active_learner.query(num_samples=20)\n",
    "\n",
    "    # Simulate user interaction here. Replace this for real-world usage.\n",
    "    y = train.y[indices_queried]\n",
    "\n",
    "    # Return the labels for the current query to the active learner.\n",
    "    active_learner.update(y)\n",
    "\n",
    "    indices_labeled = np.concatenate([indices_queried, indices_labeled])\n",
    "    \n",
    "    print('---------------')\n",
    "    print(f'Iteration #{i} ({len(indices_labeled)} samples)')\n",
    "    results.append(evaluate(active_learner, train[indices_labeled], test))\n",
    "    \n",
    "    stopping_criterion_response = stopping_criterion.stop(predictions=active_learner.classifier.predict(train))\n",
    "    print(f'Stop: {stopping_criterion_response}')\n",
    "    stopping_history.append(stopping_criterion_response)\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cbadf91",
   "metadata": {},
   "source": [
    "----\n",
    "\n",
    "<a name=\"nb2-part5-visualization\"></a>\n",
    "## V. Visualization\n",
    "\n",
    "You can use the learning curve plot, which now depicts the learning curve of the training accuracy, to visualize the earliest iteration at which the stopping criterion returned `True`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7801fcc7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize=(12, 8))\n",
    "ax = plt.axes()\n",
    "\n",
    "data = np.array([np.arange(num_queries+1), np.array(results)]).T\n",
    "df = pd.DataFrame(data, columns=['query', 'accuracy'])\n",
    "sns.lineplot(x='query', y='accuracy', data=df)\n",
    "\n",
    "plt.xlabel('number of queries', labelpad=15)\n",
    "plt.ylabel('train accuracy', labelpad=25)\n",
    "\n",
    "earliest_stopping_response = np.amin([i for i, _ in enumerate(stopping_history) if stopping_history[i] is True])\n",
    "plt.axvline(x=earliest_stopping_response, ymin=0, ymax=1, color='purple', ls='--')\n",
    "\n",
    "sns.despine()"
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "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": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}