examples/notebooks/03-active-learning-with-setfit.ipynb

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   "source": [
    "# Use SetFit for Active Learning in Small-Text\n",
    "*Notebook 3*  \n",
    "In this tutorial we demonstrate how to use [SetFit](https://github.com/huggingface/setfit) as a classifier for active learning.\n",
    "\n",
    "----\n",
    "\n",
    "## Overview\n",
    "\n",
    "- [Part I: Installation](#nb3-part1-installation)\n",
    "- [Part II: Data](#nb3-part2-data)\n",
    "- [Part III: Active Learning](#nb3-part3-active-learning)\n",
    "- [Part IV: Comparison](#nb3-part4-comparison)\n",
    "\n",
    "----\n",
    "\n",
    "<a id=\"nb3-part1-installation\"></a>\n",
    "## I. Installation\n",
    "\n",
    "The small-text wrapper for SetFit is a part of the transformers integration, so the installation procedure is the same as for every other example that shows small-text with transformers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "837556c3",
   "metadata": {
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    "tags": []
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   "outputs": [
    {
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     "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-kz2a9oqj/small-text_8a3d4bc547bc484791d9734897406458\n",
      "  Running command git clone --filter=blob:none --quiet https://github.com/webis-de/small-text.git /tmp/pip-install-kz2a9oqj/small-text_8a3d4bc547bc484791d9734897406458\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",
      "\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",
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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",
      "Requirement already satisfied: datasets in /home/cschroeder/.pyenv/versions/3.9.18/envs/notebooks/lib/python3.9/site-packages (2.19.1)\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",
      "Requirement already satisfied: matplotlib in /home/cschroeder/.pyenv/versions/3.9.18/envs/notebooks/lib/python3.9/site-packages (3.9.0)\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",
      "env: TOKENIZERS_PARALLELISM=false\n"
     ]
    }
   ],
   "source": [
    "%pip install small-text[transformers]==1.4.1\n",
    "\n",
    "# setfit is used as an optional dependency here\n",
    "%pip install setfit==0.7.0  # Currently requires setfit<1.0.0; see installation guide for details.\n",
    "%pip install datasets\n",
    "%pip install matplotlib\n",
    "\n",
    "%env TOKENIZERS_PARALLELISM=false\n",
    "    \n",
    "import logging\n",
    "import warnings\n",
    "\n",
    "import datasets\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rc('figure', titlesize=22)\n",
    "plt.rc('axes', titlesize=22, labelsize=20, linewidth=1.2)\n",
    "plt.rc('xtick', labelsize=14)\n",
    "plt.rc('ytick', labelsize=14)\n",
    "plt.rc('legend', fontsize=16)\n",
    "plt.rc('lines', linewidth=2)\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",
    "for logger_name in ['setfit.modeling', 'setfit.trainer']:\n",
    "    logger = logging.getLogger(logger_name)\n",
    "    logger.setLevel(logging.ERROR)\n",
    "\n",
    "warnings.filterwarnings(action='ignore', category=FutureWarning, message=r'.*`clean_up_tokenization_spaces` was not set..*')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdf5fd19",
   "metadata": {},
   "source": [
    "<a id=\"nb3-part2-data\"></a>\n",
    "## II. Data\n",
    "\n",
    "We use the \"AG's News\" dataset for this demonstration. It consists of news articles which are categorized into four mutually exclusive classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "754e5b56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 10 training samples:\n",
      "\n",
      "2   Wall St. Bears Claw Back Into the Black (Reuters) Reuters - Short-sellers, Wall Street's dwindling\\band of ultra-cynics, are seeing green again.\n",
      "2   Carlyle Looks Toward Commercial Aerospace (Reuters) Reuters - Private investment firm Carlyle Group,\\which has a reputation for making well-timed and occasionally\\controversial plays in the defense industry, has quietly placed\\its bets on another part of the market.\n",
      "2   Oil and Economy Cloud Stocks' Outlook (Reuters) Reuters - Soaring crude prices plus worries\\about the economy and the outlook for earnings are expected to\\hang over the stock market next week during the depth of the\\summer doldrums.\n",
      "2   Iraq Halts Oil Exports from Main Southern Pipeline (Reuters) Reuters - Authorities have halted oil export\\flows from the main pipeline in southern Iraq after\\intelligence showed a rebel militia could strike\\infrastructure, an oil official said on Saturday.\n",
      "2   Oil prices soar to all-time record, posing new menace to US economy (AFP) AFP - Tearaway world oil prices, toppling records and straining wallets, present a new economic menace barely three months before the US presidential elections.\n",
      "2   Stocks End Up, But Near Year Lows (Reuters) Reuters - Stocks ended slightly higher on Friday\\but stayed near lows for the year as oil prices surged past  #36;46\\a barrel, offsetting a positive outlook from computer maker\\Dell Inc. (DELL.O)\n",
      "2   Money Funds Fell in Latest Week (AP) AP - Assets of the nation's retail money market mutual funds fell by  #36;1.17 billion in the latest week to  #36;849.98 trillion, the Investment Company Institute said Thursday.\n",
      "2   Fed minutes show dissent over inflation (USATODAY.com) USATODAY.com - Retail sales bounced back a bit in July, and new claims for jobless benefits fell last week, the government said Thursday, indicating the economy is improving from a midsummer slump.\n",
      "2   Safety Net (Forbes.com) Forbes.com - After earning a PH.D. in Sociology, Danny Bazil Riley started to work as the general manager at a commercial real estate firm at an annual base salary of  #36;70,000. Soon after, a financial planner stopped by his desk to drop off brochures about insurance benefits available through his employer. But, at 32, \"buying insurance was the furthest thing from my mind,\" says Riley.\n",
      "2   Wall St. Bears Claw Back Into the Black  NEW YORK (Reuters) - Short-sellers, Wall Street's dwindling  band of ultra-cynics, are seeing green again.\n"
     ]
    }
   ],
   "source": [
    "import datasets\n",
    "import logging\n",
    "import numpy as np\n",
    "\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",
    "raw_dataset = datasets.load_dataset('ag_news')\n",
    "num_classes = np.unique(raw_dataset['train']['label']).shape[0]\n",
    "\n",
    "print('First 10 training samples:\\n')\n",
    "for i in range(10):\n",
    "    print(raw_dataset['train']['label'][i], ' ', raw_dataset['train']['text'][i])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1742f7d7",
   "metadata": {},
   "source": [
    "### Preparing the Data\n",
    "\n",
    "The SetFit Classifier uses the `TextData` structure as input. Such datasets can be easily created from raw text and labels using `TextDataset.from_arrays()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "be151ce8",
   "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 numpy as np\n",
    "from small_text import TextDataset\n",
    "\n",
    "num_classes = raw_dataset['train'].features['label'].num_classes\n",
    "\n",
    "target_labels = np.arange(num_classes)\n",
    "\n",
    "train = TextDataset.from_arrays(raw_dataset['train']['text'], np.array(raw_dataset['train']['label']), target_labels=target_labels)\n",
    "test = TextDataset.from_arrays(raw_dataset['test']['text'], np.array(raw_dataset['test']['label']), target_labels=target_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3db26811",
   "metadata": {},
   "source": [
    "<a id=\"nb3-part3-active-learning\"></a>\n",
    "## III. Active Learning \n",
    "\n",
    "### Configuring the Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2184e4b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from small_text.integrations.transformers.classifiers.setfit import SetFitModelArguments\n",
    "from small_text.integrations.transformers.classifiers.factories import SetFitClassificationFactory\n",
    "\n",
    "\n",
    "num_classes = 4\n",
    "\n",
    "sentence_transformer_model_name = 'sentence-transformers/paraphrase-mpnet-base-v2'\n",
    "setfit_model_args = SetFitModelArguments(sentence_transformer_model_name)\n",
    "clf_factory = SetFitClassificationFactory(setfit_model_args, num_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "017ebf3d",
   "metadata": {},
   "source": [
    "### Setting up the Active Learner\n",
    "\n",
    "Here, we construct a `PoolBasedActiveLearner` instance which requires a classifier factory, a query strategy, and the train dataset.\n",
    "\n",
    "To obtain a first model, we initialize the active learner by providing the true labels for 20 sentences. This corresponds to an initial labeling the real-world setting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "66774521",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`SentenceTransformer._target_device` has been deprecated, please use `SentenceTransformer.device` instead.\n",
      "Currently using DataParallel (DP) for multi-gpu training, while DistributedDataParallel (DDP) is recommended for faster training. See https://sbert.net/docs/sentence_transformer/training/distributed.html for more information.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'train_runtime': 9.6827, 'train_samples_per_second': 82.622, 'train_steps_per_second': 2.582, 'train_loss': 0.18906953811645508, 'epoch': 1.0}\n"
     ]
    }
   ],
   "source": [
    "from small_text import (\n",
    "    PoolBasedActiveLearner, \n",
    "    random_initialization_balanced,\n",
    "    BreakingTies,\n",
    "    SubsamplingQueryStrategy\n",
    ")\n",
    "\n",
    "# define a query strategy and initialize a pool-based active learner\n",
    "query_strategy = SubsamplingQueryStrategy(BreakingTies())\n",
    "# suppress progress bars in jupyter notebook\n",
    "setfit_train_kwargs = {'show_progress_bar': False}\n",
    "active_learner = PoolBasedActiveLearner(clf_factory, query_strategy, train, fit_kwargs={'setfit_train_kwargs': setfit_train_kwargs})\n",
    "\n",
    "\n",
    "# simulate a warm start\n",
    "def initialize_active_learner(active_learner, y_train):\n",
    "\n",
    "    x_indices_initial = random_initialization_balanced(y_train, n_samples=20)\n",
    "    y_initial = y_train[x_indices_initial]\n",
    "\n",
    "    active_learner.initialize_data(x_indices_initial, y_initial)\n",
    "\n",
    "    return x_indices_initial\n",
    "\n",
    "\n",
    "initial_indices = initialize_active_learner(active_learner, train.y)\n",
    "labeled_indices = initial_indices"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8888591",
   "metadata": {},
   "source": [
    "### Active Learning Loop\n",
    "\n",
    "The main active learning loop queries the unlabeled pool and thereby decides which documents are labeled next.\n",
    "We then provide the labels for those documents and the active learner retrains the model.\n",
    "After each query, we evaluate the current model against the test set and save the result.\n",
    "\n",
    "\n",
    "Note: This is active learning as it is done in a scientific simulation. In reality, the label feedback would have been given by human annotators."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aacd866c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy: 1.00\n",
      "Test accuracy: 0.74\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`SentenceTransformer._target_device` has been deprecated, please use `SentenceTransformer.device` instead.\n",
      "Currently using DataParallel (DP) for multi-gpu training, while DistributedDataParallel (DDP) is recommended for faster training. See https://sbert.net/docs/sentence_transformer/training/distributed.html for more information.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'train_runtime': 17.8561, 'train_samples_per_second': 89.605, 'train_steps_per_second': 2.8, 'train_loss': 0.18869617462158203, 'epoch': 1.0}\n",
      "---------------\n",
      "Iteration #0 (40 samples)\n",
      "Train accuracy: 1.00\n",
      "Test accuracy: 0.78\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`SentenceTransformer._target_device` has been deprecated, please use `SentenceTransformer.device` instead.\n",
      "Currently using DataParallel (DP) for multi-gpu training, while DistributedDataParallel (DDP) is recommended for faster training. See https://sbert.net/docs/sentence_transformer/training/distributed.html for more information.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'train_runtime': 26.868, 'train_samples_per_second': 89.326, 'train_steps_per_second': 2.791, 'train_loss': 0.17581239064534504, 'epoch': 1.0}\n",
      "---------------\n",
      "Iteration #1 (60 samples)\n",
      "Train accuracy: 0.98\n",
      "Test accuracy: 0.83\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`SentenceTransformer._target_device` has been deprecated, please use `SentenceTransformer.device` instead.\n",
      "Currently using DataParallel (DP) for multi-gpu training, while DistributedDataParallel (DDP) is recommended for faster training. See https://sbert.net/docs/sentence_transformer/training/distributed.html for more information.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'train_runtime': 36.4054, 'train_samples_per_second': 87.899, 'train_steps_per_second': 2.747, 'train_loss': 0.1361895751953125, 'epoch': 1.0}\n",
      "---------------\n",
      "Iteration #2 (80 samples)\n",
      "Train accuracy: 1.00\n",
      "Test accuracy: 0.84\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`SentenceTransformer._target_device` has been deprecated, please use `SentenceTransformer.device` instead.\n",
      "Currently using DataParallel (DP) for multi-gpu training, while DistributedDataParallel (DDP) is recommended for faster training. See https://sbert.net/docs/sentence_transformer/training/distributed.html for more information.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'train_runtime': 44.9959, 'train_samples_per_second': 88.897, 'train_steps_per_second': 2.778, 'train_loss': 0.11862326812744141, 'epoch': 1.0}\n",
      "---------------\n",
      "Iteration #3 (100 samples)\n",
      "Train accuracy: 1.00\n",
      "Test accuracy: 0.86\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`SentenceTransformer._target_device` has been deprecated, please use `SentenceTransformer.device` instead.\n",
      "Currently using DataParallel (DP) for multi-gpu training, while DistributedDataParallel (DDP) is recommended for faster training. See https://sbert.net/docs/sentence_transformer/training/distributed.html for more information.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'train_runtime': 54.5882, 'train_samples_per_second': 87.931, 'train_steps_per_second': 2.748, 'train_loss': 0.1072121556599935, 'epoch': 1.0}\n",
      "---------------\n",
      "Iteration #4 (120 samples)\n",
      "Train accuracy: 0.99\n",
      "Test accuracy: 0.87\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`SentenceTransformer._target_device` has been deprecated, please use `SentenceTransformer.device` instead.\n",
      "Currently using DataParallel (DP) for multi-gpu training, while DistributedDataParallel (DDP) is recommended for faster training. See https://sbert.net/docs/sentence_transformer/training/distributed.html for more information.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'train_runtime': 63.6636, 'train_samples_per_second': 87.962, 'train_steps_per_second': 2.749, 'train_loss': 0.1004845210484096, 'epoch': 1.0}\n",
      "---------------\n",
      "Iteration #5 (140 samples)\n",
      "Train accuracy: 0.99\n",
      "Test accuracy: 0.89\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`SentenceTransformer._target_device` has been deprecated, please use `SentenceTransformer.device` instead.\n",
      "Currently using DataParallel (DP) for multi-gpu training, while DistributedDataParallel (DDP) is recommended for faster training. See https://sbert.net/docs/sentence_transformer/training/distributed.html for more information.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'train_runtime': 72.7188, 'train_samples_per_second': 88.01, 'train_steps_per_second': 2.75, 'train_loss': 0.09031893730163575, 'epoch': 1.0}\n",
      "---------------\n",
      "Iteration #6 (160 samples)\n",
      "Train accuracy: 1.00\n",
      "Test accuracy: 0.89\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`SentenceTransformer._target_device` has been deprecated, please use `SentenceTransformer.device` instead.\n",
      "Currently using DataParallel (DP) for multi-gpu training, while DistributedDataParallel (DDP) is recommended for faster training. See https://sbert.net/docs/sentence_transformer/training/distributed.html for more information.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'train_runtime': 81.2231, 'train_samples_per_second': 88.645, 'train_steps_per_second': 2.77, 'train_loss': 0.09120938618977864, 'epoch': 1.0}\n",
      "---------------\n",
      "Iteration #7 (180 samples)\n",
      "Train accuracy: 1.00\n",
      "Test accuracy: 0.90\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`SentenceTransformer._target_device` has been deprecated, please use `SentenceTransformer.device` instead.\n",
      "Currently using DataParallel (DP) for multi-gpu training, while DistributedDataParallel (DDP) is recommended for faster training. See https://sbert.net/docs/sentence_transformer/training/distributed.html for more information.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'train_runtime': 90.3594, 'train_samples_per_second': 88.535, 'train_steps_per_second': 2.767, 'train_loss': 0.09263275909423828, 'epoch': 1.0}\n",
      "---------------\n",
      "Iteration #8 (200 samples)\n",
      "Train accuracy: 1.00\n",
      "Test accuracy: 0.89\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`SentenceTransformer._target_device` has been deprecated, please use `SentenceTransformer.device` instead.\n",
      "Currently using DataParallel (DP) for multi-gpu training, while DistributedDataParallel (DDP) is recommended for faster training. See https://sbert.net/docs/sentence_transformer/training/distributed.html for more information.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'train_runtime': 99.6519, 'train_samples_per_second': 88.307, 'train_steps_per_second': 2.76, 'train_loss': 0.09249711470170455, 'epoch': 1.0}\n",
      "---------------\n",
      "Iteration #9 (220 samples)\n",
      "Train accuracy: 1.00\n",
      "Test accuracy: 0.89\n"
     ]
    }
   ],
   "source": [
    "import gc\n",
    "import torch\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "\n",
    "num_queries = 10\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",
    "    test_acc = accuracy_score(y_pred_test, test.y)\n",
    "\n",
    "    print('Train accuracy: {:.2f}'.format(accuracy_score(y_pred, train.y)))\n",
    "    print('Test accuracy: {:.2f}'.format(test_acc))\n",
    "    \n",
    "    return test_acc\n",
    "\n",
    "\n",
    "results_setfit = []\n",
    "results_setfit.append(evaluate(active_learner, train[labeled_indices], test))\n",
    "\n",
    "    \n",
    "for i in range(num_queries):\n",
    "    # ...where each iteration consists of labelling 20 samples\n",
    "    q_indices = active_learner.query(num_samples=20)\n",
    "\n",
    "    # Simulate user interaction here. Replace this for real-world usage.\n",
    "    y = train.y[q_indices]\n",
    "\n",
    "    # Return the labels for the current query to the active learner.\n",
    "    active_learner.update(y)\n",
    "    \n",
    "    # memory fix: https://github.com/UKPLab/sentence-transformers/issues/1793\n",
    "    gc.collect()\n",
    "    torch.cuda.empty_cache()\n",
    "\n",
    "    labeled_indices = np.concatenate([q_indices, labeled_indices])\n",
    "\n",
    "    print('---------------')\n",
    "    print('Iteration #{:d} ({} samples)'.format(i, len(labeled_indices)))\n",
    "    results_setfit.append(evaluate(active_learner, train[labeled_indices], test))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5eeb9a63",
   "metadata": {},
   "source": [
    "## Plotting the Results\n",
    "\n",
    "With the previous results we can plot a [learning curve](https://en.wikipedia.org/wiki/Learning_curve_(machine_learning)) to visualize the performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "278c1d13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/home/cschroeder/.pyenv/versions/3.9.18/envs/notebooks/lib/python3.9/site-packages/matplotlib/pyplot.py'>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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 matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize=(12, 8))\n",
    "ax = plt.axes()\n",
    "\n",
    "ax.plot(np.arange(num_queries+1), results_setfit, label='SetFit')\n",
    "ax.legend()\n",
    "\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('num queries')\n",
    "\n",
    "display(plt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b6f8f75",
   "metadata": {},
   "source": [
    "<a id=\"nb3-part4-comparison\"></a>\n",
    "## IV. Comparing against BERT\n",
    "\n",
    "We perform the same steps again for BERT."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0c64d809",
   "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 numpy as np\n",
    "\n",
    "from small_text import TransformersDataset\n",
    "from transformers import AutoTokenizer\n",
    "\n",
    "\n",
    "transformer_model_name = 'bert-base-uncased'\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(\n",
    "    transformer_model_name\n",
    ")\n",
    "\n",
    "num_classes = raw_dataset['train'].features['label'].num_classes\n",
    "\n",
    "target_labels = np.arange(num_classes)\n",
    "max_length = 128\n",
    "\n",
    "\n",
    "train = TransformersDataset.from_arrays(raw_dataset['train']['text'], \n",
    "                                        np.array(raw_dataset['train']['label']), \n",
    "                                        tokenizer,\n",
    "                                        target_labels=target_labels,\n",
    "                                        max_length=max_length)\n",
    "test = TransformersDataset.from_arrays(raw_dataset['test']['text'], \n",
    "                                       np.array(raw_dataset['test']['label']), \n",
    "                                       tokenizer,\n",
    "                                       target_labels=target_labels,\n",
    "                                       max_length=max_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b8b78fc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from small_text import TransformerBasedClassificationFactory, TransformerModelArguments\n",
    "\n",
    "setfit_model_args = TransformerModelArguments(transformer_model_name)\n",
    "clf_factory = TransformerBasedClassificationFactory(setfit_model_args, \n",
    "                                                    num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a2d93583",
   "metadata": {},
   "outputs": [],
   "source": [
    "from small_text import (\n",
    "    PoolBasedActiveLearner, \n",
    "    random_initialization_balanced,\n",
    "    BreakingTies,\n",
    "    SubsamplingQueryStrategy\n",
    ")\n",
    "\n",
    "# define a query strategy and initialize a pool-based active learner\n",
    "query_strategy = SubsamplingQueryStrategy(BreakingTies())\n",
    "active_learner = PoolBasedActiveLearner(clf_factory, query_strategy, train)\n",
    "\n",
    "\n",
    "# use the same initial set as above\n",
    "active_learner.initialize_data(initial_indices, train.y[initial_indices])\n",
    "labeled_indices = initial_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e4252f63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy: 0.85\n",
      "Test accuracy: 0.42\n",
      "---------------\n",
      "Iteration #0 (40 samples)\n",
      "Train accuracy: 0.72\n",
      "Test accuracy: 0.43\n",
      "---------------\n",
      "Iteration #1 (60 samples)\n",
      "Train accuracy: 0.90\n",
      "Test accuracy: 0.63\n",
      "---------------\n",
      "Iteration #2 (80 samples)\n",
      "Train accuracy: 0.97\n",
      "Test accuracy: 0.78\n",
      "---------------\n",
      "Iteration #3 (100 samples)\n",
      "Train accuracy: 0.99\n",
      "Test accuracy: 0.84\n",
      "---------------\n",
      "Iteration #4 (120 samples)\n",
      "Train accuracy: 0.94\n",
      "Test accuracy: 0.78\n",
      "---------------\n",
      "Iteration #5 (140 samples)\n",
      "Train accuracy: 0.97\n",
      "Test accuracy: 0.85\n",
      "---------------\n",
      "Iteration #6 (160 samples)\n",
      "Train accuracy: 0.96\n",
      "Test accuracy: 0.86\n",
      "---------------\n",
      "Iteration #7 (180 samples)\n",
      "Train accuracy: 0.97\n",
      "Test accuracy: 0.83\n",
      "---------------\n",
      "Iteration #8 (200 samples)\n",
      "Train accuracy: 0.97\n",
      "Test accuracy: 0.88\n",
      "---------------\n",
      "Iteration #9 (220 samples)\n",
      "Train accuracy: 0.97\n",
      "Test accuracy: 0.88\n"
     ]
    }
   ],
   "source": [
    "results_bert = []\n",
    "results_bert.append(evaluate(active_learner, train[labeled_indices], test))\n",
    "\n",
    "    \n",
    "for i in range(num_queries):\n",
    "    # ...where each iteration consists of labelling 20 samples\n",
    "    q_indices = active_learner.query(num_samples=20)\n",
    "\n",
    "    # Simulate user interaction here. Replace this for real-world usage.\n",
    "    y = train.y[q_indices]\n",
    "\n",
    "    # Return the labels for the current query to the active learner.\n",
    "    active_learner.update(y)\n",
    "\n",
    "    labeled_indices = np.concatenate([q_indices, labeled_indices])\n",
    "    \n",
    "    print('---------------')\n",
    "    print('Iteration #{:d} ({} samples)'.format(i, len(labeled_indices)))\n",
    "    results_bert.append(evaluate(active_learner, train[labeled_indices], test))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b282d8bb",
   "metadata": {},
   "source": [
    "### Comparison\n",
    "\n",
    "This time we plot the learning curves of SetFit and BERT. Not only does SetFit run quite fast (albeit we did not measure the runtime here) but also its learning curve is quite steep.\n",
    "\n",
    "Do not draw any conclusions from this besides the impressive performance and runtime of SetFit. The comparison itself is not really fair and should be in favor of `bert-base-uncased` which has 12 layers and outputs embeddings of size 768 compared to `paraphrase-MiniLM-L3-v2` which has only three layers and outputs embeddings of size 384."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c2c39b7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/home/cschroeder/.pyenv/versions/3.9.18/envs/notebooks/lib/python3.9/site-packages/matplotlib/pyplot.py'>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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 matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize=(12, 8))\n",
    "ax = plt.axes()\n",
    "\n",
    "ax.plot(np.arange(20, 240, 20), results_setfit, label=f'SetFit ({sentence_transformer_model_name})')\n",
    "ax.plot(np.arange(20, 240, 20), results_bert, label=f'BERT ({transformer_model_name})')\n",
    "ax.legend(loc='lower right')\n",
    "\n",
    "plt.xticks(np.arange(20, 240, 20))\n",
    "plt.ylim((0.25, 1.0))\n",
    "\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('number of instances')\n",
    "\n",
    "plt.title('AG\\'s News / Breaking Ties')\n",
    "\n",
    "\n",
    "display(plt)"
   ]
  }
 ],
 "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": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}