CSE515_MWDB_Project/Phase 3/task_5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils import *\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "fd_collection = getCollection(\"team_5_mwdb_phase_2\", \"fd_collection\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_feature_model = \"fc_fd\"\n",
    "\n",
    "t = int(input(\"Enter value of t: \"))\n",
    "if t < 1:\n",
    "    raise ValueError(\"t should be a positive integer\")\n",
    "\n",
    "num_layers = int(input(\"Enter the number of layers: \"))\n",
    "if num_layers < 1:\n",
    "    raise ValueError(\"num_layers should be a positive integer\")\n",
    "\n",
    "num_hashes_per_layer = int(input(\"Enter the number of hashes per layer: \"))\n",
    "if num_hashes_per_layer < 1:\n",
    "    raise ValueError(\"num_hashes_per_layer should be a positive integer\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_images = list(fd_collection.find())\n",
    "all_images = sorted(all_images, key = lambda x: x[\"image_id\"])\n",
    "\n",
    "odd_image_ids = [img[\"image_id\"] for img in all_images if img[\"image_id\"] % 2 == 0]\n",
    "\n",
    "even_image_labels = [img[\"true_label\"] for img in all_images if img[\"image_id\"] % 2 == 0]\n",
    "odd_image_labels = [img[\"true_label\"] for img in all_images if img[\"image_id\"] % 2 != 0]\n",
    "\n",
    "feature_vectors = [np.array(img[selected_feature_model]).flatten() for img in all_images]\n",
    "\n",
    "total_len = len(feature_vectors)\n",
    "even_feature_vectors = []\n",
    "odd_feature_vectors = []\n",
    "\n",
    "for i in range(total_len):\n",
    "  if i % 2 == 0:\n",
    "    even_feature_vectors.append(feature_vectors[i])\n",
    "  else:\n",
    "    odd_feature_vectors.append(feature_vectors[i])\n",
    "\n",
    "even_feature_vectors = np.array(even_feature_vectors)\n",
    "odd_feature_vectors = np.array(odd_feature_vectors)\n",
    "\n",
    "odd_len = odd_feature_vectors.shape[0]\n",
    "even_len = even_feature_vectors.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique vectors considered: 210\n",
      "Overall number of vectors considered: 265\n",
      "15 Most Similar Vectors:\n",
      "Label of the input image: 5\n",
      "Label: 3, (1078, [-0.09339900314807892, -0.003246128559112549, 0.008638394996523857, -0.19130085408687592, -0.09068502485752106, 0.0852460116147995, 0.1705757975578308, -0.19062790274620056, 0.11604724824428558, -0.1439134180545807, -0.03672311082482338, -0.012472901493310928, -0.20803089439868927, -0.19453124701976776, -0.08260566741228104, -0.10372554510831833, 0.2793111205101013, 0.036157600581645966, 0.01458636112511158, -0.29630789160728455, 0.4764160215854645, -0.3228815793991089, -0.370932012796402, -0.031474385410547256, -0.2118302881717682, -0.19923610985279083, -0.196367546916008, 0.07233747839927673, 0.06497518718242645, -0.15095970034599304, -0.1327892243862152, -0.07300391793251038, 0.020353320986032486, -0.09443800151348114, 0.058752235025167465, 0.24090096354484558, -0.4528697729110718, -0.17242345213890076, 0.3063212037086487, -0.005360260605812073, 0.223260760307312, -0.13058391213417053, 0.11432977020740509, -0.013286445289850235, 0.11295782774686813, 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     ]
    }
   ],
   "source": [
    "query_image_id = int(input(\"Enter value of query_image_id: \"))\n",
    "if query_image_id < 1:\n",
    "    raise ValueError(\"query_image_id should be a positive integer\")\n",
    "\n",
    "num_vectors = even_feature_vectors.shape[0]\n",
    "vector_dimension = even_feature_vectors.shape[1]\n",
    "\n",
    "lsh = LSHIndex(num_layers, num_hashes_per_layer, vector_dimension)\n",
    "\n",
    "for index, vector in enumerate(even_feature_vectors):\n",
    "    lsh.add_vector(vector.tolist(), index * 2)\n",
    "\n",
    "query_vector = np.array(odd_feature_vectors[(query_image_id // 2)])\n",
    "\n",
    "result_t_unique, unique_count, overall_count = lsh.query_t_unique(query_vector, t)\n",
    "print(f\"Number of unique vectors considered: {unique_count}\")\n",
    "print(f\"Overall number of vectors considered: {overall_count}\")\n",
    "print(f\"{t} Most Similar Vectors:\")\n",
    "print(f\"Label of the input image: {odd_image_labels[(query_image_id // 2)]}\")\n",
    "\n",
    "training_vectors = []\n",
    "training_labels = []\n",
    "for result in result_t_unique:\n",
    "    training_labels.append(even_image_labels[(result[0] // 2)])\n",
    "    training_vectors.append(result[1])\n",
    "    print(f\"Label: {even_image_labels[(result[0] // 2)]}, {result}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "print(training_vectors)\n",
    "print(training_labels)\n",
    "\n",
    "user_feedback = []\n",
    "\n",
    "length = len(training_labels)\n",
    "\n",
    "for i in range(length):\n",
    "  print(f\"Label: {training_labels[i]}, vector: {training_vectors[i]}\")\n",
    "  feedback = input(\"What is the feedback for result 1? (R+, R, I, I-)\")\n",
    "\n",
    "  match feedback:\n",
    "\n",
    "    case \"R+\":\n",
    "      user_feedback.append(2)\n",
    "\n",
    "    case \"R\":\n",
    "      user_feedback.append(1)\n",
    "\n",
    "    case \"I\":\n",
    "      user_feedback.append(-1)\n",
    "\n",
    "    case \"I-\":\n",
    "      user_feedback.append(-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-2, -1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]\n"
     ]
    }
   ],
   "source": [
    "print(user_feedback)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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