CSE515_MWDB_Project/Phase 2/task6.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import math\n",
    "from pymongo import MongoClient\n",
    "import scipy\n",
    "import numpy as np\n",
    "from sklearn.decomposition import NMF\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "client = MongoClient()\n",
    "client = MongoClient(host = \"localhost\", port = 27017)\n",
    "\n",
    "# Select the database\n",
    "db = client.Multimedia_Web_DBs\n",
    "\n",
    "# Fetch all documents from the collection and then sort them by \"_id\"\n",
    "feature_descriptors = list(db.Caltech101_Feature_Descriptors.find({}))\n",
    "feature_descriptors = sorted(list(db.Caltech101_Feature_Descriptors.find({})), key=lambda x: x[\"_id\"], reverse=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extractKLatentSemantics(k, image_sim_matrix, feature_model, dim_reduction):\n",
    "\n",
    "  feature_ids = [x[\"_id\"] for x in feature_descriptors if x[\"_id\"] % 2 == 0]\n",
    "  feature_labels = [x[\"label\"] for x in feature_descriptors if x[\"_id\"] % 2 == 0]\n",
    "\n",
    "  filename = 'ls4.json'\n",
    "\n",
    "  match dim_reduction:\n",
    "\n",
    "    case 1:\n",
    "      U, S, Vh = scipy.sparse.linalg.svds(np.array(image_sim_matrix), k=k)\n",
    "      k_latent_semantics = sorted(list(zip(feature_ids, U.tolist())), key = lambda x: x[1][0], reverse = True)\n",
    "\n",
    "    case 2:\n",
    "      model = NMF(n_components = k, init = 'random', solver = 'cd', alpha_H = 0.01, alpha_W = 0.01, max_iter = 10000)\n",
    "      min_value = np.min(image_sim_matrix)\n",
    "      feature_vectors_shifted = image_sim_matrix - min_value\n",
    "      U = model.fit_transform(np.array(feature_vectors_shifted))\n",
    "      k_latent_semantics = sorted(list(zip(feature_ids, U.tolist())), key = lambda x: x[1][0], reverse = True)\n",
    "\n",
    "    case 3:\n",
    "      U = LinearDiscriminantAnalysis(n_components = k).fit_transform(image_sim_matrix, feature_labels)\n",
    "      k_latent_semantics = sorted(list(zip(feature_ids, U.tolist())), key = lambda x: x[1][0], reverse = True)\n",
    "\n",
    "    case 4:\n",
    "      kmeans = KMeans(n_clusters = k)\n",
    "      kmeans.fit(image_sim_matrix)\n",
    "      U = kmeans.transform(image_sim_matrix)\n",
    "      k_latent_semantics = sorted(list(zip(feature_ids, U.tolist())), key = lambda x: x[1][0], reverse = True)\n",
    "  \n",
    "  k_latent_semantics = [{\"_id\": item[0], \"semantics\": item[1]} for item in k_latent_semantics]\n",
    "  with open(filename, 'w', encoding='utf-8') as f:\n",
    "    json.dump(k_latent_semantics, f, ensure_ascii = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def findImageImageSimMatrix(feature_model):\n",
    "   \n",
    "  feature_vectors = [x[feature_model] for x in feature_descriptors if x[\"_id\"] % 2 == 0]\n",
    "\n",
    "  n = len(feature_vectors)\n",
    "\n",
    "  image_sim_matrix = np.zeros((n, n))\n",
    "\n",
    "  for i in range(n):\n",
    "    for j in range(i + 1, n):\n",
    "\n",
    "      match feature_model:\n",
    "\n",
    "        case \"color_moments\":\n",
    "          image_sim_matrix[i][j] = image_sim_matrix[j][i] = math.dist(feature_vectors[i], feature_vectors[j])\n",
    "        \n",
    "        case \"hog\":\n",
    "          image_sim_matrix[i][j] = image_sim_matrix[j][i] = (np.dot(feature_vectors[i], feature_vectors[j]) / (np.linalg.norm(feature_vectors[i]) * np.linalg.norm(feature_vectors[j])))\n",
    "\n",
    "        case \"avgpool\" | \"layer3\" | \"fc\":\n",
    "          image_sim_matrix[i][j] = image_sim_matrix[j][i] = scipy.stats.pearsonr(feature_vectors[i], feature_vectors[j]).statistic\n",
    "          \n",
    "  return image_sim_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def main():\n",
    "\n",
    "  k = int(input(\"Enter k: \"))\n",
    "\n",
    "  features = ['color_moments', 'hog', 'layer3', 'avgpool', 'fc']\n",
    "\n",
    "  # User input for feature model to extract\n",
    "  print(\"\\n1: Color moments\")\n",
    "  print(\"2: HOG\")\n",
    "  print(\"3: Resnet50 Avgpool layer\")\n",
    "  print(\"4: Resnet50 Layer 3\")\n",
    "  print(\"5: Resnet50 FC layer\")\n",
    "  feature_model = features[int(input(\"Select the feature model: \")) - 1]\n",
    "\n",
    "  print(\"\\n1. SVD\")\n",
    "  print(\"2. NNMF\")\n",
    "  print(\"3. LDA\")\n",
    "  print(\"4. k-means\")\n",
    "  dim_reduction = int(input(\"Select the dimensionality reduction technique: \"))\n",
    "\n",
    "  image_sim_matrix = findImageImageSimMatrix(feature_model)\n",
    "  print(image_sim_matrix)\n",
    "\n",
    "  extractKLatentSemantics(k, image_sim_matrix, feature_model, dim_reduction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "if __name__ == \"__main__\":\n",
    "   main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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