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

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "94536c6d",
   "metadata": {},
   "source": [
    "# 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 notebCriterionook 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": [
      "Collecting git+https://github.com/webis-de/small-text\n",
      "  Cloning https://github.com/webis-de/small-text to /tmp/pip-req-build-gc5f8wl7\n",
      "  Running command git clone --filter=blob:none --quiet https://github.com/webis-de/small-text /tmp/pip-req-build-gc5f8wl7\n",
      "  Resolved https://github.com/webis-de/small-text to commit 9b3377732c32fadafbce63d98fb9ce6448d818f3\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",
      "\u001B[?25hRequirement already satisfied: scipy in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from small-text==1.3.0) (1.10.0)\n",
      "Requirement already satisfied: scikit-learn>=0.24.1 in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from small-text==1.3.0) (1.2.1)\n",
      "Requirement already satisfied: dill in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from small-text==1.3.0) (0.3.6)\n",
      "Requirement already satisfied: numpy>=1.20.0 in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from small-text==1.3.0) (1.24.1)\n",
      "Requirement already satisfied: packaging in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from small-text==1.3.0) (23.0)\n",
      "Requirement already satisfied: tqdm in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from small-text==1.3.0) (4.64.1)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from scikit-learn>=0.24.1->small-text==1.3.0) (3.1.0)\n",
      "Requirement already satisfied: joblib>=1.1.1 in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from scikit-learn>=0.24.1->small-text==1.3.0) (1.2.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
      "Requirement already satisfied: datasets in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (2.7.1)\n",
      "Requirement already satisfied: matplotlib in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (3.4.3)\n",
      "Requirement already satisfied: seaborn in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (0.11.2)\n",
      "Requirement already satisfied: fsspec[http]>=2021.11.1 in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from datasets) (2022.11.0)\n",
      "Requirement already satisfied: packaging in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from datasets) (23.0)\n",
      "Requirement already satisfied: responses<0.19 in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from datasets) (0.18.0)\n",
      "Requirement already satisfied: requests>=2.19.0 in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from datasets) (2.28.2)\n",
      "Requirement already satisfied: pyarrow>=6.0.0 in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from datasets) (10.0.1)\n",
      "Requirement already satisfied: numpy>=1.17 in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from datasets) (1.24.1)\n",
      "Requirement already satisfied: aiohttp in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from datasets) (3.8.3)\n",
      "Requirement already satisfied: xxhash in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from datasets) (3.1.0)\n",
      "Requirement already satisfied: multiprocess in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from datasets) (0.70.14)\n",
      "Requirement already satisfied: pandas in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from datasets) (1.5.2)\n",
      "Requirement already satisfied: huggingface-hub<1.0.0,>=0.2.0 in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from datasets) (0.12.0)\n",
      "Requirement already satisfied: pyyaml>=5.1 in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from datasets) (6.0)\n",
      "Requirement already satisfied: tqdm>=4.62.1 in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from datasets) (4.64.1)\n",
      "Requirement already satisfied: dill<0.3.7 in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from datasets) (0.3.6)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from matplotlib) (9.4.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from matplotlib) (0.11.0)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from matplotlib) (3.0.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from matplotlib) (2.8.2)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from matplotlib) (1.3.2)\n",
      "Requirement already satisfied: scipy>=1.0 in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from seaborn) (1.10.0)\n",
      "Requirement already satisfied: frozenlist>=1.1.1 in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from aiohttp->datasets) (1.3.3)\n",
      "Requirement already satisfied: yarl<2.0,>=1.0 in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from aiohttp->datasets) (1.8.2)\n",
      "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from aiohttp->datasets) (4.0.2)\n",
      "Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from aiohttp->datasets) (2.1.1)\n",
      "Requirement already satisfied: multidict<7.0,>=4.5 in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from aiohttp->datasets) (6.0.3)\n",
      "Requirement already satisfied: aiosignal>=1.1.2 in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from aiohttp->datasets) (1.3.1)\n",
      "Requirement already satisfied: attrs>=17.3.0 in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from aiohttp->datasets) (22.1.0)\n",
      "Requirement already satisfied: filelock in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from huggingface-hub<1.0.0,>=0.2.0->datasets) (3.9.0)\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from huggingface-hub<1.0.0,>=0.2.0->datasets) (4.4.0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from pandas->datasets) (2022.6)\n",
      "Requirement already satisfied: six>=1.5 in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from requests>=2.19.0->datasets) (2022.12.7)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from requests>=2.19.0->datasets) (3.4)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages (from requests>=2.19.0->datasets) (1.26.14)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install small-text[transformers]==1.3.2   # 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": "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": [
    {
     "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",
    "import torch\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)"
   ]
  },
  {
   "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": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1222.24it/s]\n",
      "/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": [
    "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 setup a tranformer-based active learning process."
   ]
  },
  {
   "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",
    "\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": "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: 0.60\n",
      "Test accuracy: 0.49\n",
      "---------------\n",
      "Iteration #0 (40 samples)\n",
      "Train accuracy: 0.93\n",
      "Test accuracy: 0.63\n",
      "Stop: False\n",
      "---------------\n",
      "Iteration #1 (60 samples)\n",
      "Train accuracy: 0.98\n",
      "Test accuracy: 0.64\n",
      "Stop: False\n",
      "---------------\n",
      "Iteration #2 (80 samples)\n",
      "Train accuracy: 0.97\n",
      "Test accuracy: 0.68\n",
      "Stop: False\n",
      "---------------\n",
      "Iteration #3 (100 samples)\n",
      "Train accuracy: 0.66\n",
      "Test accuracy: 0.52\n",
      "Stop: False\n",
      "---------------\n",
      "Iteration #4 (120 samples)\n",
      "Train accuracy: 0.94\n",
      "Test accuracy: 0.70\n",
      "Stop: False\n",
      "---------------\n",
      "Iteration #5 (140 samples)\n",
      "Train accuracy: 0.96\n",
      "Test accuracy: 0.71\n",
      "Stop: False\n",
      "---------------\n",
      "Iteration #6 (160 samples)\n",
      "Train accuracy: 0.98\n",
      "Test accuracy: 0.74\n",
      "Stop: False\n",
      "---------------\n",
      "Iteration #7 (180 samples)\n",
      "Train accuracy: 0.94\n",
      "Test accuracy: 0.77\n",
      "Stop: False\n",
      "---------------\n",
      "Iteration #8 (200 samples)\n",
      "Train accuracy: 0.97\n",
      "Test accuracy: 0.76\n",
      "Stop: True\n",
      "---------------\n",
      "Iteration #9 (220 samples)\n",
      "Train accuracy: 0.95\n",
      "Test accuracy: 0.77\n",
      "Stop: True\n",
      "---------------\n",
      "Iteration #10 (240 samples)\n",
      "Train accuracy: 0.96\n",
      "Test accuracy: 0.77\n",
      "Stop: True\n",
      "---------------\n",
      "Iteration #11 (260 samples)\n",
      "Train accuracy: 0.98\n",
      "Test accuracy: 0.80\n",
      "Stop: True\n",
      "---------------\n",
      "Iteration #12 (280 samples)\n",
      "Train accuracy: 0.97\n",
      "Test accuracy: 0.81\n",
      "Stop: True\n",
      "---------------\n",
      "Iteration #13 (300 samples)\n",
      "Train accuracy: 0.97\n",
      "Test accuracy: 0.80\n",
      "Stop: True\n",
      "---------------\n",
      "Iteration #14 (320 samples)\n",
      "Train accuracy: 0.98\n",
      "Test accuracy: 0.78\n",
      "Stop: True\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from small_text import KappaAverage\n",
    "\n",
    "\n",
    "num_queries = 15\n",
    "stopping_criterion = KappaAverage(num_classes, kappa=0.9)\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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\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('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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}