examples/notebooks/01-active-learning-for-text-classification-with-small-text-intro.ipynb

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    "# Active Learning for Text Classification using Small-Text\n",
    "*Notebook 1*  \n",
    "This is an introductory example that shows you how to use [small-text](https://github.com/webis-de/small-text) to perform active learning for text classification using state-of-the-art transformer models.\n",
    "\n",
    "----\n",
    "\n",
    "## Overview\n",
    "\n",
    "- [Part I: Installation](#nb1-part1-installation)\n",
    "- [Part II: Data](#nb1-part2-data)\n",
    "- [Part III: Setting up the Active Learner](#nb1-part3-active-learning)\n",
    "- [Part IV: Plotting the Results](#nb1-part4-plotting)\n",
    "\n",
    "----\n",
    "\n",
    "<a id=\"nb1-part1-installation\"></a>\n",
    "## I. Installation\n",
    "\n",
    "First, we install small-text for its active learning functionality, [datasets](https://github.com/huggingface/datasets) to load an example dataset, and [matptlotlib](https://matplotlib.org/) to plot the learning curves at the end."
   ]
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     "output_type": "stream",
     "text": [
      "\u001b[33mDEPRECATION: git+https://github.com/webis-de/small-text#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 to /tmp/pip-install-vgffqfmm/small-text_9aa5a1dae1e64a4faff332b08ec8e7b1\n",
      "  Running command git clone --filter=blob:none --quiet https://github.com/webis-de/small-text /tmp/pip-install-vgffqfmm/small-text_9aa5a1dae1e64a4faff332b08ec8e7b1\n",
      "  Resolved https://github.com/webis-de/small-text to commit 70885e6a5e8b15c7a7ebaff1ef4cc487ae016826\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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      "\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;49m24.3.1\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;49m24.3.1\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]==2.0.0dev1\n",
    "\n",
    "# additional dependencies for this example\n",
    "%pip install datasets matplotlib \"seaborn>=0.13.0\" pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "658cddec",
   "metadata": {},
   "source": [
    "### Preparation\n",
    "\n",
    "You can skip this part when reading for the first time if you are only interested in active learning. Here, we configure the loggging behavior and display progress bars display of the `datasets` library to improve its appearance in the notebook. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "13170d96",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datasets\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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c35a787",
   "metadata": {},
   "source": [
    "Moreover, we update the default matplotlib settings to receive a more visually appealing plot at the end of this tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "269ce876",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import rcParams\n",
    "rcParams.update({'xtick.labelsize': 14, 'ytick.labelsize': 14, 'axes.labelsize': 16})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "741d6d3f",
   "metadata": {},
   "source": [
    "Finally, we will fix the random seeds so that readers do not get confused when the results change upon repeated execution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e7be9231",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: TOKENIZERS_PARALLELISM=false\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "\n",
    "seed = 2022\n",
    "torch.manual_seed(seed)\n",
    "np.random.seed(seed)\n",
    "\n",
    "%env TOKENIZERS_PARALLELISM=false"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdf5fd19",
   "metadata": {},
   "source": [
    "<a id=\"nb1-part2-data\"></a>\n",
    "## II. Data\n",
    "\n",
    "First, we load the rotten tomatoes dataset. This dataset contains movie reviews sentences, which are labeled by their sentiment as either positive or negative."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "754e5b56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 10 training samples:\n",
      "\n",
      "1   the rock is destined to be the 21st century's new \" conan \" and that he's going to make a splash even greater than arnold schwarzenegger , jean-claud van damme or steven segal .\n",
      "1   the gorgeously elaborate continuation of \" the lord of the rings \" trilogy is so huge that a column of words cannot adequately describe co-writer/director peter jackson's expanded vision of j . r . r . tolkien's middle-earth .\n",
      "1   effective but too-tepid biopic\n",
      "1   if you sometimes like to go to the movies to have fun , wasabi is a good place to start .\n",
      "1   emerges as something rare , an issue movie that's so honest and keenly observed that it doesn't feel like one .\n",
      "1   the film provides some great insight into the neurotic mindset of all comics -- even those who have reached the absolute top of the game .\n",
      "1   offers that rare combination of entertainment and education .\n",
      "1   perhaps no picture ever made has more literally showed that the road to hell is paved with good intentions .\n",
      "1   steers turns in a snappy screenplay that curls at the edges ; it's so clever you want to hate it . but he somehow pulls it off .\n",
      "1   take care of my cat offers a refreshingly different slice of asian cinema .\n"
     ]
    }
   ],
   "source": [
    "import logging\n",
    "\n",
    "raw_dataset = datasets.load_dataset('rotten_tomatoes')\n",
    "num_classes = raw_dataset['train'].features['label'].num_classes\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",
    "Next, we have to convert this raw text data into a format usable by small-text. Since the transformer-based classification in small-text uses huggingface transformers this step is pretty similar to the preprocessing you may know from transformers, with the addition that the end result must be a `TransformersDataset`. In this example, we use `bert-base-uncased` as transformer model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2c52f326",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import AutoTokenizer\n",
    "\n",
    "transformer_model_name = 'bert-base-uncased'\n",
    "\n",
    "tokenizer_kwargs = {'clean_up_tokenization_spaces': True}\n",
    "tokenizer = AutoTokenizer.from_pretrained(\n",
    "    transformer_model_name,\n",
    "    **tokenizer_kwargs\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3252a546",
   "metadata": {},
   "source": [
    "We use the `TransformersDataset.from_arrays()` helper function which constructs a `TransformersDataset` instance using the tokenizer, text, and labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "be151ce8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from small_text import TransformersDataset\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": "017ebf3d",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<a name=\"nb1-part3-active-learning\"></a>\n",
    "## III. Setting up the Active Learner\n",
    "\n",
    "Now we constrauct 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 10 sentences. This corresponds to an initial labeling the real-world setting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "66774521",
   "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(indices_initial)\n",
    "\n",
    "    return indices_initial\n",
    "\n",
    "\n",
    "transformer_model = TransformerModelArguments(transformer_model_name, tokenizer_kwargs=tokenizer_kwargs)\n",
    "clf_factory = TransformerBasedClassificationFactory(transformer_model, \n",
    "                                                    num_classes, \n",
    "                                                    classification_kwargs=dict({'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": "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, and moreover, we would not be able to measure the test accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "aacd866c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy: 0.95\n",
      "Test accuracy: 0.62\n",
      "---------------\n",
      "Iteration #0 (40 samples)\n",
      "Train accuracy: 0.90\n",
      "Test accuracy: 0.66\n",
      "---------------\n",
      "Iteration #1 (60 samples)\n",
      "Train accuracy: 0.97\n",
      "Test accuracy: 0.65\n",
      "---------------\n",
      "Iteration #2 (80 samples)\n",
      "Train accuracy: 0.96\n",
      "Test accuracy: 0.70\n",
      "---------------\n",
      "Iteration #3 (100 samples)\n",
      "Train accuracy: 0.93\n",
      "Test accuracy: 0.65\n",
      "---------------\n",
      "Iteration #4 (120 samples)\n",
      "Train accuracy: 0.97\n",
      "Test accuracy: 0.69\n",
      "---------------\n",
      "Iteration #5 (140 samples)\n",
      "Train accuracy: 0.96\n",
      "Test accuracy: 0.72\n",
      "---------------\n",
      "Iteration #6 (160 samples)\n",
      "Train accuracy: 0.96\n",
      "Test accuracy: 0.71\n",
      "---------------\n",
      "Iteration #7 (180 samples)\n",
      "Train accuracy: 0.96\n",
      "Test accuracy: 0.69\n",
      "---------------\n",
      "Iteration #8 (200 samples)\n",
      "Train accuracy: 0.95\n",
      "Test accuracy: 0.78\n",
      "---------------\n",
      "Iteration #9 (220 samples)\n",
      "Train accuracy: 0.96\n",
      "Test accuracy: 0.81\n"
     ]
    }
   ],
   "source": [
    "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 = []\n",
    "results.append(evaluate(active_learner, train[indices_labeled], test))\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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5eeb9a63",
   "metadata": {},
   "source": [
    "----\n",
    "\n",
    "<a id=\"nb1-part4-plotting\"></a>\n",
    "## IV. Plotting the Results\n",
    "\n",
    "Using the previously saved results we can plot a [learning curve](https://en.wikipedia.org/wiki/Learning_curve_(machine_learning)) to visualize the resulting accuracy on the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "278c1d13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib==3.9.0 in /home/cschroeder/.pyenv/versions/3.9.18/envs/notebooks/lib/python3.9/site-packages (3.9.0)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /home/cschroeder/.pyenv/versions/3.9.18/envs/notebooks/lib/python3.9/site-packages (from matplotlib==3.9.0) (1.4.5)\n",
      "Requirement already satisfied: packaging>=20.0 in /home/cschroeder/.pyenv/versions/3.9.18/envs/notebooks/lib/python3.9/site-packages (from matplotlib==3.9.0) (24.1)\n",
      "Requirement already satisfied: importlib-resources>=3.2.0 in /home/cschroeder/.pyenv/versions/3.9.18/envs/notebooks/lib/python3.9/site-packages (from matplotlib==3.9.0) (6.4.2)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /home/cschroeder/.pyenv/versions/3.9.18/envs/notebooks/lib/python3.9/site-packages (from matplotlib==3.9.0) (1.2.1)\n",
      "Requirement already satisfied: numpy>=1.23 in /home/cschroeder/.pyenv/versions/3.9.18/envs/notebooks/lib/python3.9/site-packages (from matplotlib==3.9.0) (1.26.4)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /home/cschroeder/.pyenv/versions/3.9.18/envs/notebooks/lib/python3.9/site-packages (from matplotlib==3.9.0) (3.1.2)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /home/cschroeder/.pyenv/versions/3.9.18/envs/notebooks/lib/python3.9/site-packages (from matplotlib==3.9.0) (4.53.1)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/cschroeder/.pyenv/versions/3.9.18/envs/notebooks/lib/python3.9/site-packages (from matplotlib==3.9.0) (0.12.1)\n",
      "Requirement already satisfied: pillow>=8 in /home/cschroeder/.pyenv/versions/3.9.18/envs/notebooks/lib/python3.9/site-packages (from matplotlib==3.9.0) (10.4.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /home/cschroeder/.pyenv/versions/3.9.18/envs/notebooks/lib/python3.9/site-packages (from matplotlib==3.9.0) (2.9.0.post0)\n",
      "Requirement already satisfied: zipp>=3.1.0 in /home/cschroeder/.pyenv/versions/3.9.18/envs/notebooks/lib/python3.9/site-packages (from importlib-resources>=3.2.0->matplotlib==3.9.0) (3.20.0)\n",
      "Requirement already satisfied: six>=1.5 in /home/cschroeder/.pyenv/versions/3.9.18/envs/notebooks/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib==3.9.0) (1.16.0)\n",
      "\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;49m24.3.1\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"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%pip install \"matplotlib==3.9.0\"\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('test accuracy', labelpad=25)\n",
    "\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f22253e-2afe-43af-9ad3-f13165827a40",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}