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

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  {
   "cell_type": "markdown",
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   "source": [
    "# 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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     "text": [
      "Collecting git+https://github.com/webis-de/small-text\n",
      "  Cloning https://github.com/webis-de/small-text to /tmp/pip-req-build-t03mrl6z\n",
      "  Running command git clone --filter=blob:none --quiet https://github.com/webis-de/small-text /tmp/pip-req-build-t03mrl6z\n",
      "  Resolved https://github.com/webis-de/small-text to commit 9b3377732c32fadafbce63d98fb9ce6448d818f3\n",
      "  Installing build dependencies ... \u001b[?25ldone\n",
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      "Building wheels for collected packages: small-text\n",
      "  Building wheel for small-text (pyproject.toml) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for small-text: filename=small_text-1.3.0-py3-none-any.whl size=202963 sha256=e6f4adb4206fae1528fe418b0fd383a906e892dbc7ec52d12bac9870741de8f3\n",
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      "      Successfully uninstalled small-text-1.2.0.dev1\n",
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      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install small-text[transformers]==1.3.1   # use \"small-text\" without \"[transformers]\" if you want to work on the CPU only\n",
    "\n",
    "# additional dependencies for this example\n",
    "%pip install datasets matplotlib seaborn"
   ]
  },
  {
   "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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "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": [],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "\n",
    "seed = 2022\n",
    "torch.manual_seed(seed)\n",
    "np.random.seed(seed)"
   ]
  },
  {
   "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": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 406.54it/s]"
     ]
    },
    {
     "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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\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 = AutoTokenizer.from_pretrained(\n",
    "    transformer_model_name\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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/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": [
    "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_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=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: 0.60\n",
      "Test accuracy: 0.49\n",
      "---------------\n",
      "Iteration #0 (40 samples)\n",
      "Train accuracy: 0.78\n",
      "Test accuracy: 0.50\n",
      "---------------\n",
      "Iteration #1 (60 samples)\n",
      "Train accuracy: 0.88\n",
      "Test accuracy: 0.59\n",
      "---------------\n",
      "Iteration #2 (80 samples)\n",
      "Train accuracy: 0.93\n",
      "Test accuracy: 0.64\n",
      "---------------\n",
      "Iteration #3 (100 samples)\n",
      "Train accuracy: 0.94\n",
      "Test accuracy: 0.69\n",
      "---------------\n",
      "Iteration #4 (120 samples)\n",
      "Train accuracy: 0.97\n",
      "Test accuracy: 0.71\n",
      "---------------\n",
      "Iteration #5 (140 samples)\n",
      "Train accuracy: 0.80\n",
      "Test accuracy: 0.62\n",
      "---------------\n",
      "Iteration #6 (160 samples)\n",
      "Train accuracy: 0.91\n",
      "Test accuracy: 0.67\n",
      "---------------\n",
      "Iteration #7 (180 samples)\n",
      "Train accuracy: 0.92\n",
      "Test accuracy: 0.73\n",
      "---------------\n",
      "Iteration #8 (200 samples)\n",
      "Train accuracy: 0.94\n",
      "Test accuracy: 0.75\n",
      "---------------\n",
      "Iteration #9 (220 samples)\n",
      "Train accuracy: 0.86\n",
      "Test accuracy: 0.68\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": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\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.vstack((np.arange(num_queries+1), np.array(results)))\n",
    "sns.lineplot(x=0, y=1, data=data)\n",
    "\n",
    "plt.xlabel('number of queries', labelpad=15)\n",
    "plt.ylabel('test accuracy', labelpad=25)\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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}