growing_datasets/Lab - Growing Datasets.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a6f14f0e",
   "metadata": {},
   "source": [
    "# Lab -- Growing Dataset with Active (Machine) Learning\n",
    "\n",
    "Intended to accompany the lecture on Growing or Compressing Datasets, this notebook contains exercises to build labeled training datasets for binary classification through active learning.\n",
    "\n",
    "You may find it helpful to first install the following dependencies:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b8e1a8c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: faiss-cpu in /Users/colemanc/.pyenv/versions/3.8.16/envs/dcai-iap/lib/python3.8/site-packages (1.7.3)\n",
      "Requirement already satisfied: pyarrow in /Users/colemanc/.pyenv/versions/3.8.16/envs/dcai-iap/lib/python3.8/site-packages (10.0.1)\n",
      "Requirement already satisfied: pandas in /Users/colemanc/.pyenv/versions/3.8.16/envs/dcai-iap/lib/python3.8/site-packages (1.5.3)\n",
      "Requirement already satisfied: matplotlib in /Users/colemanc/.pyenv/versions/3.8.16/envs/dcai-iap/lib/python3.8/site-packages (3.6.3)\n",
      "Requirement already satisfied: scikit-learn in /Users/colemanc/.pyenv/versions/3.8.16/envs/dcai-iap/lib/python3.8/site-packages (1.2.0)\n",
      "Requirement already satisfied: torch in /Users/colemanc/.pyenv/versions/3.8.16/envs/dcai-iap/lib/python3.8/site-packages (1.13.1)\n",
      "Requirement already satisfied: tqdm in /Users/colemanc/.pyenv/versions/3.8.16/envs/dcai-iap/lib/python3.8/site-packages (4.64.1)\n",
      "Requirement already satisfied: numpy>=1.16.6 in /Users/colemanc/.pyenv/versions/3.8.16/envs/dcai-iap/lib/python3.8/site-packages (from pyarrow) (1.24.1)\n",
      "Requirement already satisfied: pytz>=2020.1 in /Users/colemanc/.pyenv/versions/3.8.16/envs/dcai-iap/lib/python3.8/site-packages (from pandas) (2022.7.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /Users/colemanc/.pyenv/versions/3.8.16/envs/dcai-iap/lib/python3.8/site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /Users/colemanc/.pyenv/versions/3.8.16/envs/dcai-iap/lib/python3.8/site-packages (from matplotlib) (9.4.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /Users/colemanc/.pyenv/versions/3.8.16/envs/dcai-iap/lib/python3.8/site-packages (from matplotlib) (4.38.0)\n",
      "Requirement already satisfied: packaging>=20.0 in /Users/colemanc/.pyenv/versions/3.8.16/envs/dcai-iap/lib/python3.8/site-packages (from matplotlib) (23.0)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in /Users/colemanc/.pyenv/versions/3.8.16/envs/dcai-iap/lib/python3.8/site-packages (from matplotlib) (3.0.9)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /Users/colemanc/.pyenv/versions/3.8.16/envs/dcai-iap/lib/python3.8/site-packages (from matplotlib) (1.0.7)\n",
      "Requirement already satisfied: cycler>=0.10 in /Users/colemanc/.pyenv/versions/3.8.16/envs/dcai-iap/lib/python3.8/site-packages (from matplotlib) (0.11.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/colemanc/.pyenv/versions/3.8.16/envs/dcai-iap/lib/python3.8/site-packages (from matplotlib) (1.4.4)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /Users/colemanc/.pyenv/versions/3.8.16/envs/dcai-iap/lib/python3.8/site-packages (from scikit-learn) (3.1.0)\n",
      "Requirement already satisfied: scipy>=1.3.2 in /Users/colemanc/.pyenv/versions/3.8.16/envs/dcai-iap/lib/python3.8/site-packages (from scikit-learn) (1.10.0)\n",
      "Requirement already satisfied: joblib>=1.1.1 in /Users/colemanc/.pyenv/versions/3.8.16/envs/dcai-iap/lib/python3.8/site-packages (from scikit-learn) (1.2.0)\n",
      "Requirement already satisfied: typing-extensions in /Users/colemanc/.pyenv/versions/3.8.16/envs/dcai-iap/lib/python3.8/site-packages (from torch) (4.4.0)\n",
      "Requirement already satisfied: six>=1.5 in /Users/colemanc/.pyenv/versions/3.8.16/envs/dcai-iap/lib/python3.8/site-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install faiss-cpu pyarrow pandas matplotlib scikit-learn torch tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6096ec1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/yz/ytphbcfx10d7xgxshk2hl1jc0000gn/T/ipykernel_84056/1857601726.py:17: DeprecationWarning: Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display\n",
      "  from IPython.core.display import Image, display, clear_output\n",
      "/var/folders/yz/ytphbcfx10d7xgxshk2hl1jc0000gn/T/ipykernel_84056/1857601726.py:17: DeprecationWarning: Importing clear_output from IPython.core.display is deprecated since IPython 7.14, please import from IPython display\n",
      "  from IPython.core.display import Image, display, clear_output\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import time\n",
    "import random\n",
    "from glob import glob\n",
    "from datetime import datetime\n",
    "from typing import List, Tuple, Optional, Callable\n",
    "\n",
    "import faiss  # Must import faiss before everything else\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from torch.utils.data import Dataset\n",
    "from tqdm.notebook import tqdm\n",
    "from sklearn import linear_model\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import average_precision_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from IPython.core.display import Image, display, clear_output"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e6fb5ff",
   "metadata": {},
   "source": [
    "# Section 1: 1D Classifier\n",
    "\n",
    "First, let's go through the toy 1D example from lecture."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e9c0ea0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate random data for two classes\n",
    "np.random.seed(2)\n",
    "x1 = np.random.normal(-2, 1, 100)\n",
    "x2 = np.random.normal(2, 1, 100)\n",
    "boundary = 0\n",
    "seed = np.array([(boundary + 2 + (random.random() * 2)),\n",
    "                 (boundary - 2 - (random.random() * 2))])\n",
    "x = np.concatenate([seed, x1, x2])\n",
    "labeled_0 = np.arange(len(seed))\n",
    "\n",
    "\n",
    "# This is a simple case, so labeling is easy and cheap.\n",
    "# But this isn't the case in practice. A real 'label' function would\n",
    "# request information from humans or an expensive process.\n",
    "def label(example):\n",
    "    return int(example > boundary)\n",
    "\n",
    "\n",
    "# Helper functions for plotting\n",
    "def color(label):\n",
    "    return 'r' if label else 'b'\n",
    "\n",
    "\n",
    "def plot_boundary(ax):\n",
    "    ax.axvline(x=boundary, color='gray', linestyle='--')\n",
    "    \n",
    "    \n",
    "def plot_labeled(ax, x, labeled, labels):\n",
    "    ax.scatter(x, np.zeros(len(x)), c='gray', alpha=0.2)\n",
    "    ax.scatter(x[labeled], np.zeros(len(labeled)), c=[color(l) for l in labels], s=200, alpha=0.5)\n",
    "    ax.axvline(x=boundary, color='gray', linestyle='--')\n",
    "    ax.set_axis_off()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9baa7aec",
   "metadata": {},
   "source": [
    "## Passive Learning (Random Sampling)\n",
    "Passive learning simply samples unlabeled examples at random. It doesn't adapt to the model or the data, so we have to hope that we get examples that help us close in on the decision boundary.\n",
    "\n",
    "In the figures below, gray dots represent unlabeled examples, red & blue dots represent the labeled examples for the two classes, and the dashed gray line is the actual decision boundary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "59f6eef3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def passive_selection(x, labeled, label_func, n):\n",
    "    candidates = set(range(0, len(x))) - set(labeled)\n",
    "    labeled = np.concatenate([labeled, random.sample(list(candidates), n)])\n",
    "    labels = [label_func(example) for example in x[labeled]]\n",
    "    return labeled, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6fb3d635",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Round 0: 2 labeled examples\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 600x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Round 1: 3 labeled examples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Round 2: 4 labeled examples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Round 3: 5 labeled examples\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 600x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Round 4: 6 labeled examples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Round 5: 7 labeled examples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Round 6: 8 labeled examples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Round 7: 9 labeled examples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 8\n",
    "labeled, labels = passive_selection(x, labeled_0, label, n)\n",
    "for i in range(n):\n",
    "    print(f\"Round {i}: {2+i} labeled examples\")\n",
    "    fig, ax = plt.subplots(1, 1, figsize=(6, 1))\n",
    "    plot_labeled(ax, x, labeled[:2+i], labels[:2+i])\n",
    "    plot_boundary(ax)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "685512fc",
   "metadata": {},
   "source": [
    "## Exercise 1: Active Learning (Max Entropy)\n",
    "Active learning trains a model $A_r$ after every example $\\mathbf{x}$ (or batch of examples) is labeled and leverages that model to select the following example. For instance, we can quantify the model's uncertainty by calculating the entropy for each of the pool of unlabeled examples $\\mathcal{P}_r$ based on the model's predicted probabilities:\n",
    "\n",
    "$$\\phi_{\\text{Ent}}(\\mathbf{x}, A_r) = - \\sum_{\\hat{y}} P(\\hat{y}|\\mathbf{x}; A_r) \\log P(\\hat{y}|\\mathbf{x}; A_r)$$\n",
    "\n",
    "and select the examples with the highest entropy:\n",
    "\n",
    "$$argmax_{\\mathbf{x} \\in \\mathcal{P}_r} \\phi_{\\text{Ent}}(\\mathbf{x}, A_r, \\mathcal{P}_r)$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bbef1f1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def select_max_entropy(x, model, indices: List[int], budget: int):\n",
    "    # Get prediction probability for elegible indices\n",
    "    proba = model.predict_proba(x[indices].reshape(-1, 1))\n",
    "\n",
    "    # Calculate information entropy from probabilities\n",
    "    entropy = -1.0 * (np.log(proba) * proba).sum(axis=1)\n",
    "\n",
    "    # Select indices with highest entropy (i.e. MaxEnt)\n",
    "    selected = entropy.argsort(axis=0)[::-1][:budget]\n",
    "\n",
    "    # Convert to original index\n",
    "    selected = np.array(indices)[selected]\n",
    "\n",
    "    return selected"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a294044",
   "metadata": {},
   "source": [
    "In this exercise, you'll leverage this max entropy heuristic to actively select examples to label! You should implement the active learning loop from lecture with a `linear_model.LogisticRegression()` as the classifier. This should effectively perform binary search to find the decision boundary. You just need to fill in the section below marked `YOUR CODE HERE`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a022fe72",
   "metadata": {},
   "outputs": [],
   "source": [
    "def active_selection(x, labeled, label_func, n):\n",
    "    labels = [label_func(example) for example in x[labeled]]\n",
    "    candidates = set(np.arange(len(x))) - set(labeled)\n",
    "    # YOUR CODE HERE\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bad1afa0",
   "metadata": {},
   "source": [
    "Let's put your active selection process into action! In the figures below, gray dots represent unlabeled examples, red & blue dots represent the labeled examples for the two classes, and the dashed gray line is the actual decision boundary. Rounds 1+ will show the results of your active learning implementation. How does it compare to the passive approach?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c69e16eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Round 0: 2 labeled examples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Round 1: 3 labeled examples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Round 2: 4 labeled examples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Round 3: 5 labeled examples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Round 4: 6 labeled examples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Round 5: 7 labeled examples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Round 6: 8 labeled examples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Round 7: 9 labeled examples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 8\n",
    "labeled, labels = active_selection(x, labeled_0, label, n)\n",
    "for i in range(n):\n",
    "    print(f\"Round {i}: {2+i} labeled examples\")\n",
    "    fig, ax = plt.subplots(1, 1, figsize=(6, 1))\n",
    "    plot_labeled(ax, x, labeled[:2+i], labels[:2+i])\n",
    "    plot_boundary(ax)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "145c5e27",
   "metadata": {},
   "source": [
    "# Section 2: OpenImages"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a36cd68e",
   "metadata": {},
   "source": [
    "Let's scale up active learning to a real dataset, [OpenImages](https://storage.googleapis.com/openimages/web/index.html)! The OpenImages dataset has over 8 million images from Flickr and captures tens of thousands of classes. To make this manageable for our lab assignment, we will train a `LogisticRegression()` classifier on top of pre-computed embeddings for the available images. Please download and unpack the embeddings.\n",
    "\n",
    "1. [Download embeddings](https://drive.google.com/file/d/1HLEI7OWDGfP-__w6vlnZjgOCo5fshP-7/view?usp=sharing) (~18 GB)\n",
    "2. Unpack embeddings: `tar -xvf data.tar.gz`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ce4bc30a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assuming you unpacked the data in the same directory\n",
    "# as this notebook, you shouldn't need to change this.\n",
    "metadata_dir='data/saved_metadata/'\n",
    "train_dir='data/saved_embeddings/train/'\n",
    "test_dir='data/saved_embeddings/test/'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17d5e305",
   "metadata": {},
   "source": [
    "## Loading Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80e847a2",
   "metadata": {},
   "source": [
    "Don't worry about understanding all of this. You'll only use a small part of this class in the exercise below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f8ddbcb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "class OpenImagesDataset(Dataset):\n",
    "    def __init__(self,\n",
    "                 metadata_dir: str,\n",
    "                 embeddings_dir: str,\n",
    "                 embeddings_include: str = '*.parquet.gzip',\n",
    "                 target_class: str = None,\n",
    "                 split: str ='train'):\n",
    "\n",
    "        # Save attributes\n",
    "        self._metadata_dir = metadata_dir\n",
    "        self._embeddings_dir = embeddings_dir\n",
    "        self._embeddings_include = embeddings_include\n",
    "        self._target_class = None\n",
    "\n",
    "        # Define paths of metadata files\n",
    "        assert split in ['train', 'test'], 'Invalid value for split'\n",
    "        label_path = os.path.join(metadata_dir, 'oidv6-class-descriptions.csv')\n",
    "        data_path = None\n",
    "        url_path = None\n",
    "\n",
    "        if split == 'train':\n",
    "            data_path = os.path.join(\n",
    "                metadata_dir,\n",
    "                'oidv6-train-annotations-human-imagelabels.csv')\n",
    "            url_path = os.path.join(\n",
    "                metadata_dir,\n",
    "                'oidv6-train-images-with-labels-with-rotation.csv')\n",
    "\n",
    "        elif split == 'test':\n",
    "            data_path = os.path.join(\n",
    "                metadata_dir,\n",
    "                'test-annotations-human-imagelabels.csv')\n",
    "            url_path = os.path.join(\n",
    "                metadata_dir,\n",
    "                'test-images-with-rotation.csv')\n",
    "\n",
    "        # Load metadata\n",
    "        print('Loading metadata...', end=' ')\n",
    "        self._label_df = pd.read_csv(label_path)\n",
    "\n",
    "        self._data_df = pd.read_csv(data_path)\n",
    "        self._data_df.drop('Source', axis=1, inplace=True)\n",
    "        self._data_df.set_index('ImageID', inplace=True)\n",
    "\n",
    "        self._url_df = pd.read_csv(url_path)\n",
    "        self._url_df = self._url_df[['ImageID', 'OriginalURL']]\n",
    "        self._url_df.set_index('ImageID', inplace=True)\n",
    "        print('Done.')\n",
    "\n",
    "        # Load embeddings\n",
    "        self._embeddings_df = self._load_embeddings_df()\n",
    "\n",
    "        # (optional) Load labels\n",
    "        if target_class is not None:\n",
    "            self.target_class = target_class\n",
    "\n",
    "    def to_numpy(self):\n",
    "        \"\"\"\n",
    "        Returns embeddings and labels\n",
    "        Important: does not make a copy of the data in memory\n",
    "        \"\"\"\n",
    "        return self.embeddings, self.labels\n",
    "\n",
    "    @property\n",
    "    def target_class(self):\n",
    "        return self._target_class\n",
    "\n",
    "    @property\n",
    "    def urls(self):\n",
    "        return self._embeddings_df.index.values\n",
    "\n",
    "    @property\n",
    "    def labels(self):\n",
    "        return self._labels_df.Confidence.values\n",
    "\n",
    "    @property\n",
    "    def index(self):\n",
    "        return self._embeddings_df.index.values\n",
    "\n",
    "    @property\n",
    "    def embeddings(self):\n",
    "        return self._embeddings_df.values\n",
    "\n",
    "    @property\n",
    "    def embeddings_dimension(self):\n",
    "        return self._embeddings_df.shape[1]\n",
    "\n",
    "    @target_class.setter\n",
    "    def target_class(self, target_class):\n",
    "        self._labels_df = self._load_labels_df(target_class)\n",
    "        self._target_class = target_class\n",
    "\n",
    "    def _load_labels_df(self, label: str):\n",
    "        \"\"\"\n",
    "        Loads metadata in metadata_dir for target_class\n",
    "        \"\"\"\n",
    "        # Get label name\n",
    "        label_name = self._label_df.query(f\"DisplayName == '{label}'\")[\n",
    "            'LabelName'].to_list()[0]\n",
    "\n",
    "        # Get labeled data given label\n",
    "        df = self._data_df.query(f\"LabelName == '{label_name}'\")\n",
    "\n",
    "        # Join with URL data\n",
    "        df = df.join(self._url_df, on='ImageID', how='left')\n",
    "\n",
    "        # Rename columns and reset index\n",
    "        df.reset_index(inplace=True)\n",
    "        df.rename(columns={'OriginalURL': 'img_path'}, inplace=True)\n",
    "        df.set_index('img_path', inplace=True)\n",
    "\n",
    "        # Align label index with embeddings index\n",
    "        df = df.reindex(self._embeddings_df.index, fill_value=0)\n",
    "\n",
    "        return df\n",
    "\n",
    "    def _load_embeddings_df(self):\n",
    "        \"\"\"\n",
    "        Loads embeddings from partitions in embeddings_dir with specified\n",
    "        pattern. Each embedding value is loaded into a separate column\n",
    "        such that embeddings.values returns the array necessary for\n",
    "        inference, which avoids creating a copy in memory of the data\n",
    "        \"\"\"\n",
    "        # Get paths of dfs in embeddings_dir with pattern\n",
    "        pattern = os.path.join(self._embeddings_dir,\n",
    "                               self._embeddings_include)\n",
    "        filepaths_ls = glob(pattern, recursive=True)\n",
    "        filepaths_ls.sort()\n",
    "\n",
    "        # Store all embedding values and indices\n",
    "        index_ls = []\n",
    "        values_ls = []\n",
    "        wrapped_loader = tqdm(filepaths_ls,\n",
    "                              desc=\"Loading embeddings\",\n",
    "                              unit=\"partition\")\n",
    "        for filepath in wrapped_loader:\n",
    "\n",
    "            df = pd.read_parquet(filepath)\n",
    "            index_ls.append(df.index)\n",
    "            values_ls.append(df['embedding'].tolist())\n",
    "\n",
    "        # Join into single df and return\n",
    "        return pd.DataFrame(\n",
    "            np.concatenate(values_ls, axis=0, dtype=np.float32),\n",
    "            index=np.concatenate(index_ls, axis=0))\n",
    "\n",
    "    \"\"\"\n",
    "    Necessary for pytorch\n",
    "    \"\"\"\n",
    "    def __len__(self):\n",
    "        return len(self._embeddings_df)\n",
    "\n",
    "    def __getitem__(self, idx: int):\n",
    "        return (self._embeddings_df.iloc[idx].values,\n",
    "                self._labels_df.Confidence.iloc[idx])\n",
    "\n",
    "    \n",
    "def create_dataset(metadata_dir: str, embeddings_dir: str, split: str):\n",
    "    return OpenImagesDataset(metadata_dir, embeddings_dir, split=split)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ea311556",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading metadata... Done.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a964b192f7cf46cb989066ea74a0212b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading embeddings:   0%|          | 0/16 [00:00<?, ?partition/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of embeddings for test images: (113508, 256)\n"
     ]
    }
   ],
   "source": [
    "# Create test dataset object\n",
    "if 'd_test' not in locals():\n",
    "    d_test = create_dataset(metadata_dir, test_dir, 'test')\n",
    "print(\"Shape of embeddings for test images: {}\".format(d_test.embeddings.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ebe209cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading metadata... Done.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "067d8d8c660644c9b56b03e0c0f96853",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading embeddings:   0%|          | 0/94 [00:00<?, ?partition/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of embeddings for training images: (8121242, 256)\n"
     ]
    }
   ],
   "source": [
    "# Create training dataset object\n",
    "if 'd_train' not in locals():\n",
    "    d_train = create_dataset(metadata_dir, train_dir, 'train')\n",
    "print(\"Shape of embeddings for training images: {}\".format(d_train.embeddings.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c87dbcd7",
   "metadata": {},
   "source": [
    "## Helper functions for active learning\n",
    "Don't worry about understanding this to complete the exercise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a110320d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_random_seed(seed: Optional[int] = None):\n",
    "    seed = np.random.randint(10000) if seed is None else seed\n",
    "    np.random.seed(seed)\n",
    "    return seed\n",
    "\n",
    "\n",
    "def create_seed(labels: np.array,\n",
    "                emb_dimension: int,\n",
    "                npositive: int = 5,\n",
    "                nnegative: int = 95,\n",
    "                random_seed: int = 400):\n",
    "    # Make sure there are enough negative examples\n",
    "    nnegative = max(nnegative, emb_dimension - npositive)\n",
    "\n",
    "    # Select indices for random samples\n",
    "    pos_samples = np.random.choice(\n",
    "        np.where(labels == 1)[0], npositive, replace=False)\n",
    "    neg_samples = np.random.choice(\n",
    "        np.where(labels == 0)[0], nnegative, replace=False)\n",
    "\n",
    "    # Combine and sort\n",
    "    seed = np.sort(np.concatenate((pos_samples, neg_samples)))\n",
    "\n",
    "    return seed\n",
    "\n",
    "\n",
    "def visualize_urls(urls: List[str], message: str = ''):\n",
    "    print(message)\n",
    "    [display(Image(url=url, width=400)) for url in urls]\n",
    "    input('Press any key to continue')\n",
    "    clear_output()\n",
    "\n",
    "    \n",
    "def train_model(embeddings: np.array, labels: np.array):\n",
    "    return LogisticRegression().fit(embeddings, labels)\n",
    "\n",
    "\n",
    "def eval_model(model, dataset):\n",
    "    proba = model.predict_proba(dataset.embeddings)[:, 1]\n",
    "    return average_precision_score(dataset.labels, proba)\n",
    "\n",
    "\n",
    "def collect_labels(dataset, indices: np.array, target_class: str):\n",
    "    labels = []\n",
    "    for url in dataset.urls[indices]:\n",
    "\n",
    "        # Show link and image\n",
    "        clear_output()\n",
    "        print(f'Link: {url}')\n",
    "        display(Image(url=url, width=400))\n",
    "        time.sleep(0.25)\n",
    "\n",
    "        # Get user label\n",
    "        need_input = True\n",
    "        while need_input:\n",
    "            label = input(f'Is this an example of {target_class}? [Y/n] ')\n",
    "            label = 'Y' if label is None or label == '' else label[0].upper()\n",
    "\n",
    "            if label not in ['N', 'Y']:\n",
    "                print('Invalid input')\n",
    "                continue\n",
    "\n",
    "            labels.append(1 if label == 'Y' else 0)\n",
    "            need_input = False\n",
    "\n",
    "    clear_output()\n",
    "    print(f'Finished labeling {len(labels)} images')\n",
    "    return labels\n",
    "\n",
    "\n",
    "def visualize_scores(scores: List[float], ylabel: str = 'Average precision'):\n",
    "    plt.title('Model score for test data')\n",
    "    plt.xlabel('Active learning round (#)')\n",
    "    plt.ylabel(ylabel)\n",
    "\n",
    "    plt.plot(range(len(scores)), scores, 'o-', color='b')\n",
    "    plt.xticks(range(len(scores)))\n",
    "    plt.legend(loc=\"best\")\n",
    "    plt.show()\n",
    "    \n",
    "def select_maxent(model, dataset, indices: List[int], budget: int):\n",
    "    # Get prediction probability for elegible indices\n",
    "    proba = model.predict_proba(dataset.embeddings[indices])\n",
    "\n",
    "    # Calculate information entropy from probabilities\n",
    "    entropy = -1.0 * (np.log(proba) * proba).sum(axis=1)\n",
    "\n",
    "    # Select indices with highest entropy (i.e. MaxEnt)\n",
    "    selected = entropy.argsort(axis=0)[::-1][:budget]\n",
    "\n",
    "    # Convert to original index\n",
    "    selected = np.array(indices)[selected]\n",
    "\n",
    "    return selected"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17c30ba8",
   "metadata": {},
   "source": [
    "## Baseline approach for reference\n",
    "You might want to skip this if you're in a rush ;)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0a067eb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def baseline_selection(\n",
    "    train, test, concept: str, rounds: Tuple[int],\n",
    "    npos: int = 5, nneg: int = 95, k: int = 100,\n",
    "    select: Callable = select_maxent):\n",
    "\n",
    "    # Set initial values\n",
    "    labeled = create_seed(train.labels, train.embeddings_dimension, npos, nneg)\n",
    "    new_labeled = labeled\n",
    "    labels = train.labels[labeled]\n",
    "    scores = []\n",
    "    model = None\n",
    "    candidates = set(np.arange(len(train))) - set(labeled)\n",
    "\n",
    "    # Visualize seed\n",
    "    visualize_urls(train.urls[labeled[np.where(labels == 1)]],\n",
    "                   message=f'Initial positive samples for {concept}')\n",
    "\n",
    "    # Main active learning loop\n",
    "    for budget in rounds:\n",
    "        # Train model using labeled samples and score\n",
    "        model = train_model(train.embeddings[labeled], labels)\n",
    "        scores.append(eval_model(model, test))\n",
    "\n",
    "        # Select points to label (MaxEnt for now)\n",
    "        new_labeled = select(model, train, list(candidates), budget)\n",
    "\n",
    "        # User labels selected points\n",
    "        new_labels = collect_labels(train, new_labeled, concept)\n",
    "\n",
    "        # Update arrays\n",
    "        labeled = np.concatenate((labeled, new_labeled))\n",
    "        labels = np.concatenate((labels, new_labels))\n",
    "        candidates -= set(new_labeled)\n",
    "\n",
    "    # Train and score final iteration of model\n",
    "    model = train_model(train.embeddings[labeled], labels)\n",
    "    scores.append(eval_model(model, test))\n",
    "    return model, scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "78bde7f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set rare concept (e.g. Bowling, Sushi, Rat, Beach, Hawk, Cupcake, Crowd, etc)\n",
    "concept = 'Bowling'\n",
    "\n",
    "# Set concept\n",
    "d_train.target_class = concept\n",
    "d_test.target_class = concept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "676a8935",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished labeling 5 images\n",
      "Total time: 0:09:12.237339\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(400)  # for reproducibility\n",
    "start = datetime.now()\n",
    "model, scores = baseline_selection(\n",
    "    train=d_train, test=d_test, concept=concept, npos=5, nneg=300,\n",
    "    rounds=[5, 5, 5], select=select_maxent)\n",
    "print(\"Total time: {}\".format(datetime.now() - start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "b9beab7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_scores(scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d75a1506",
   "metadata": {},
   "source": [
    "## Similarity Search for Efficient Active Learning and Search (SEALS)\n",
    "That was really slow. Let's speed up the process by implementing the [SEALS](https://aaai-2022.virtualchair.net/poster_aaai12967) approach from lecture.\n",
    "\n",
    "First, we need to create a similarity search index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4b7bb423",
   "metadata": {},
   "outputs": [],
   "source": [
    "class IndexWrapper():\n",
    "\n",
    "    def __init__(self, dataset, method='LSH'):\n",
    "        assert method in ['LSH', 'flatL2'], 'Invalid index method'\n",
    "        self._dataset = dataset\n",
    "        self._method = method\n",
    "        self._index = self._create_index()\n",
    "\n",
    "    def __len__(self):\n",
    "        return self._index.ntotal\n",
    "\n",
    "    def __getitem__(self, idx: int):\n",
    "        # Return url and corresponding embedding\n",
    "        return self._dataset.index[idx], self._dataset.embeddings[idx]\n",
    "\n",
    "    @property\n",
    "    def d(self):\n",
    "        return self._index.d\n",
    "\n",
    "    @property\n",
    "    def ntotal(self):\n",
    "        return self._index.ntotal\n",
    "\n",
    "    def _create_index(self):\n",
    "        \"\"\"\n",
    "        Returns FAISS index created from dataset.embeddings using the\n",
    "        specified index method\n",
    "        \"\"\"\n",
    "        # Initialize index\n",
    "        index = None\n",
    "        d = self._dataset.embeddings_dimension\n",
    "\n",
    "        if self._method == 'flatL2':\n",
    "            index = faiss.IndexFlatL2(d)\n",
    "\n",
    "        elif self._method == 'LSH':\n",
    "            n_bits = 2 * d\n",
    "            index = faiss.IndexLSH(d, n_bits)\n",
    "            index.train(self._dataset.embeddings)\n",
    "\n",
    "        # Add embeddings\n",
    "        index.add(self._dataset.embeddings)\n",
    "\n",
    "        return index\n",
    "\n",
    "    def search(self, q: np.array, k: int, attrs=None):\n",
    "        \"\"\"\n",
    "        Returns tuple, where each element is an attribute array specified\n",
    "        in attrs. Valid attributes include 'distances', 'urls', 'indices', and\n",
    "        'embeddings'\n",
    "        \"\"\"\n",
    "        # If no attributes selected, use default\n",
    "        default_attrs = ('indices', 'distances')\n",
    "        attrs = default_attrs if attrs is None else attrs\n",
    "\n",
    "        # Initialize results dictionary\n",
    "        results = {}\n",
    "\n",
    "        # Convert query to 2D np array for FAISS index\n",
    "        q = q if len(q.shape) == 2 else np.array([q])\n",
    "\n",
    "        # Search k-NN of q using the index\n",
    "        nn_distances, nn_indices = self._index.search(q, k)\n",
    "        results['distances'], results['indices'] = nn_distances, nn_indices\n",
    "\n",
    "        # Get other attributes\n",
    "        other_attrs = [attr for attr in attrs if attr not in default_attrs]\n",
    "        for attr in other_attrs:\n",
    "            assert hasattr(self._dataset, attr), 'Invalid attribute'\n",
    "            results[attr] = [\n",
    "                getattr(self._dataset, attr)[idx] for idx in results['indices']]\n",
    "\n",
    "        # Return results in order given by attrs\n",
    "        return tuple([results[attr] for attr in attrs])\n",
    "    \n",
    "    \n",
    "def create_index(dataset, method: str = 'LSH'):\n",
    "    return IndexWrapper(dataset, method=method)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6534d729",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create IndexWrapper for training data\n",
    "if 'index_train' not in locals():\n",
    "    index_train = create_index(d_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9634a77",
   "metadata": {},
   "source": [
    "## Exercise 2: Manage Candidate Pool\n",
    "Now that we have a similarity search index let's use it to limit the candidate pool we select unlabeled examples from. We don't need to look at all the unlabeled examples from the beginning. Instead, we could start with the nearest neighbors to our currently labeled examples and expand the pool. Using the baseline approach as a starting point, fill in the sections of the code labeled `YOUR CODE HERE`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0c47d19c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def SEALS(\n",
    "    train, test, concept: str, rounds: Tuple[int],\n",
    "    npos: int = 5, nneg: int = 95, k: int = 100,\n",
    "    select: Callable = select_maxent, knn = None):\n",
    "\n",
    "    # Set initial values\n",
    "    labeled = create_seed(train.labels, train.embeddings_dimension, npos, nneg)\n",
    "    new_labeled = labeled\n",
    "    labels = train.labels[labeled]\n",
    "    scores = []\n",
    "    model = None\n",
    "    candidates = set(np.arange(len(train))) - set(labeled)\n",
    "    \n",
    "    if knn is not None:\n",
    "        # YOUR CODE HERE\n",
    "        pass\n",
    "\n",
    "    # Visualize seed\n",
    "    visualize_urls(train.urls[labeled[np.where(labels == 1)]],\n",
    "                   message=f'Initial positive samples for {concept}')\n",
    "\n",
    "    # Main active learning loop\n",
    "    for budget in rounds:\n",
    "        if knn is not None:\n",
    "            # YOUR CODE HERE\n",
    "            pass\n",
    "            \n",
    "        # Train model using labeled samples and score\n",
    "        model = train_model(train.embeddings[labeled], labels)\n",
    "        scores.append(eval_model(model, test))\n",
    "\n",
    "        # Select points to label (MaxEnt for now)\n",
    "        new_labeled = select(model, train, list(candidates), budget)\n",
    "\n",
    "        # User labels selected points\n",
    "        new_labels = collect_labels(train, new_labeled, concept)\n",
    "\n",
    "        # Update arrays\n",
    "        labeled = np.concatenate((labeled, new_labeled))\n",
    "        labels = np.concatenate((labels, new_labels))\n",
    "        candidates -= set(new_labeled)\n",
    "\n",
    "    # Train and score final iteration of model\n",
    "    model = train_model(train.embeddings[labeled], labels)\n",
    "    scores.append(eval_model(model, test))\n",
    "    return model, scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b4ce9817",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished labeling 5 images\n",
      "Total time: 0:00:46.942903\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(400)  # for reproducibility\n",
    "start = datetime.now()\n",
    "model, scores = SEALS(\n",
    "    train=d_train, test=d_test, concept=concept, npos=5, nneg=300,\n",
    "    rounds=[5, 5, 5], select=select_maxent, knn=index_train)\n",
    "print(\"Total time: {}\".format(datetime.now() - start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1dcc53df",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_scores(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "283a65a0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}