{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "import numpy as np\n",
    "from utils import *\n",
    "import math\n",
    "import heapq\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "fd_collection = getCollection(\"team_5_mwdb_phase_2\", \"fd_collection\")\n",
    "all_images = fd_collection.find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label_sim-cm_fd-lda-10-model.joblib loaded\n"
     ]
    }
   ],
   "source": [
    "selected_latent_space = valid_latent_spaces[\n",
    "    str(input(\"Enter latent space - one of \" + str(list(valid_latent_spaces.keys()))))\n",
    "]\n",
    "\n",
    "selected_feature_model = valid_feature_models[\n",
    "    str(input(\"Enter feature model - one of \" + str(list(valid_feature_models.keys()))))\n",
    "]\n",
    "\n",
    "k = int(input(\"Enter value of k: \"))\n",
    "if k < 1:\n",
    "    raise ValueError(\"k should be a positive integer\")\n",
    "\n",
    "selected_dim_reduction_method = str(\n",
    "    input(\n",
    "        \"Enter dimensionality reduction method - one of \"\n",
    "        + str(list(valid_dim_reduction_methods.keys()))\n",
    "    )\n",
    ")\n",
    "\n",
    "image_id = int(input(\"Enter image ID: \"))\n",
    "if image_id < 0 and image_id > 8676 and image_id % 2 != 0:\n",
    "    raise ValueError(\"image id should be even number between 0 and 8676\")\n",
    "\n",
    "img_label = all_images[int(image_id / 2)][\"true_label\"]\n",
    "\n",
    "knum = int(input(\"Enter value of knum: \"))\n",
    "if knum < 1:\n",
    "    raise ValueError(\"knum should be a positive integer\")\n",
    "\n",
    "match selected_latent_space:\n",
    "    case \"\":\n",
    "        if selected_dim_reduction_method == \"lda\":\n",
    "            if os.path.exists(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-model.joblib\") and os.path.exists(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"):\n",
    "                if os.path.exists(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-model.joblib\"):\n",
    "                    model = load(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-model.joblib\")\n",
    "                    data = json.load(open(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"))\n",
    "                    print(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-model.joblib and json loaded\")\n",
    "                else:\n",
    "                    print(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-model.joblib does not exist\")\n",
    "            else:\n",
    "                if os.path.exists(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"):\n",
    "                    data = json.load(open(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"))\n",
    "                    print(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json loaded\")\n",
    "                else:\n",
    "                    print(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json does not exist\")\n",
    "    case \"cp\":\n",
    "        if os.path.exists(f\"{selected_feature_model}-cp-{k}-semantics.json\"):\n",
    "            data = json.load(open(f\"{selected_feature_model}-cp-{k}-semantics.json\"))\n",
    "            print(f\"{selected_feature_model}-cp-{k}-semantics.json loaded\")\n",
    "        else:\n",
    "          print(f\"{selected_feature_model}-cp-{k}-semantics.json does not exist\")\n",
    "    case _:\n",
    "        if selected_dim_reduction_method == \"lda\":\n",
    "            if os.path.exists(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-model.joblib\") and os.path.exists(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"):\n",
    "                model = load(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-model.joblib\")\n",
    "                data = json.load(open(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"))\n",
    "                print(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-model.joblib loaded\")\n",
    "            else:\n",
    "                print(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-model.joblib does not exist\")\n",
    "        else:\n",
    "            if os.path.exists(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}img-{k}-semantics.json\"):\n",
    "                data = json.load(open(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"))\n",
    "                print(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json loaded\")\n",
    "            else:\n",
    "                print(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json does not exist\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_similarities_ls1_ls4(latent_space, dim_reduction, selected_feature_model, data, image_id):\n",
    "\n",
    "  image_fd = np.array(all_images[int(image_id / 2)][selected_feature_model]).flatten()\n",
    "\n",
    "  match dim_reduction:\n",
    "\n",
    "    case 'svd':\n",
    "      U = np.array(data[\"image-semantic\"])\n",
    "      S = np.array(data[\"semantics-core\"])\n",
    "      if len(S.shape) == 1:\n",
    "        S = np.diag(S)\n",
    "      V = np.transpose(np.array(data[\"semantic-feature\"]))\n",
    "      \n",
    "      comparison_feature_space = np.matmul(U, S)\n",
    "\n",
    "      if latent_space == \"image_sim\":\n",
    "        comparison_vector = comparison_feature_space[int(image_id / 2)]\n",
    "      else:\n",
    "        comparison_vector = np.matmul(np.matmul(image_fd, V), S)\n",
    "    \n",
    "    case \"nmf\":\n",
    "      H = np.array(data['semantic-feature'])\n",
    "      comparison_feature_space = np.array(data['image-semantic'])\n",
    "\n",
    "      if latent_space == \"image_sim\":\n",
    "        comparison_vector = comparison_feature_space[int(image_id / 2)]\n",
    "      else:\n",
    "        comparison_vector = np.matmul(image_fd, np.transpose(H))\n",
    "\n",
    "    case \"kmeans\":\n",
    "      comparison_vector = []\n",
    "      comparison_feature_space = np.array(data[\"image-semantic\"])\n",
    "      S = np.array(data[\"semantic-feature\"])\n",
    "\n",
    "      for centroid in S:\n",
    "        if latent_space == \"image_sim\":\n",
    "          sim_matrix = np.array(data[\"sim-matrix\"])\n",
    "          comparison_vector.append(math.dist(sim_matrix[int(image_id / 2)], centroid))\n",
    "        else:\n",
    "          comparison_vector.append(math.dist(image_fd, centroid))\n",
    "    \n",
    "    case \"lda\":\n",
    "      comparison_feature_space = np.array(data[\"image-semantic\"])\n",
    "      if latent_space == \"image_sim\":\n",
    "        comparison_vector = comparison_feature_space[int(image_id / 2)]\n",
    "      else:\n",
    "        fd = np.array(all_images[int(image_id / 2)][selected_feature_model])\n",
    "        min_value = np.min(fd)\n",
    "        feature_vectors_shifted = fd - min_value\n",
    "        comparison_vector = model.transform(feature_vectors_shifted.flatten().reshape(1, -1)).flatten()\n",
    "        print(comparison_feature_space.shape)\n",
    "        print(comparison_vector.shape)\n",
    "\n",
    "  n = len(comparison_feature_space)\n",
    "\n",
    "  distances = []\n",
    "  for i in range(n):\n",
    "    if (i * 2) != image_id:\n",
    "      distances.append({\"image_id\": i, \"label\": all_images[i][\"true_label\"], \"distance\": math.dist(comparison_vector, comparison_feature_space[i])})\n",
    "\n",
    "  distances = sorted(distances, key=lambda x: x[\"distance\"], reverse=False)\n",
    "\n",
    "  similar_labels = []\n",
    "  unique_labels = set()\n",
    "\n",
    "  for img in distances:\n",
    "    if img['label'] not in unique_labels:\n",
    "      similar_labels.append(img)\n",
    "      unique_labels.add(img[\"label\"])\n",
    "\n",
    "      if len(similar_labels) == knum:\n",
    "        break\n",
    "\n",
    "\n",
    "  for x in similar_labels:\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_similarities_ls2(data, image_id):\n",
    "\n",
    "  IS = np.array(data[\"image-semantic\"])\n",
    "  S = np.array(data[\"semantics-core\"])\n",
    "\n",
    "  if len(S.shape) == 1:\n",
    "    S = np.diag(S)\n",
    "\n",
    "  comparison_feature_space = np.matmul(IS, S)\n",
    "  comparison_vector = comparison_feature_space[int(image_id / 2)]\n",
    "\n",
    "  distances = []\n",
    "\n",
    "  n = len(comparison_feature_space)\n",
    "  for i in range(n):\n",
    "    if i != (image_id / 2):\n",
    "      distances.append({\"image_id\": i * 2, \"label\":  all_images[i][\"true_label\"], \"distance\": math.dist(comparison_vector, comparison_feature_space[i])})\n",
    "  \n",
    "  distances = sorted(distances, key=lambda x: x[\"distance\"], reverse=False)\n",
    "\n",
    "  similar_labels = []\n",
    "  unique_labels = set()\n",
    "\n",
    "  for img in distances:\n",
    "    if img[\"label\"] not in unique_labels and img[\"label\"] != img_label:\n",
    "      similar_labels.append(img)\n",
    "      unique_labels.add(img[\"label\"])\n",
    "\n",
    "      if len(similar_labels) == knum:\n",
    "        break\n",
    "\n",
    "\n",
    "  for x in similar_labels:\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_similarities_ls3(dim_reduction, data, image_id):\n",
    "\n",
    "  img_label = all_images[int(image_id / 2)][\"true_label\"]\n",
    "\n",
    "  match dim_reduction:\n",
    "\n",
    "    case 'svd':\n",
    "      U = np.array(data[\"image-semantic\"])\n",
    "      S = np.array(data[\"semantics-core\"])\n",
    "      V = np.transpose(np.array(data[\"semantic-feature\"]))\n",
    "\n",
    "      comparison_feature_space = np.matmul(U, S)\n",
    "      comparison_vector = comparison_feature_space[img_label]\n",
    "    \n",
    "    case \"nmf\":\n",
    "      comparison_feature_space = np.array(data['image-semantic'])\n",
    "      comparison_vector = comparison_feature_space[img_label]\n",
    "\n",
    "    case \"kmeans\":\n",
    "      comparison_feature_space = np.array(data[\"image-semantic\"])\n",
    "      comparison_vector = comparison_feature_space[img_label]\n",
    "\n",
    "    case \"lda\":\n",
    "      comparison_feature_space = np.array(data[\"image-semantic\"])\n",
    "      comparison_vector = comparison_feature_space[img_label]\n",
    "\n",
    "  n = len(comparison_feature_space)\n",
    "  distance = float('inf')\n",
    "  most_similar_label = img_label\n",
    "  distances = []\n",
    "  for i in range(n):\n",
    "    if i != img_label:\n",
    "      distances.append({\"label\": i, \"distance\": math.dist(comparison_vector, comparison_feature_space[i])})\n",
    "\n",
    "  distances = sorted(distances, key=lambda x: x[\"distance\"], reverse=False)[:knum]\n",
    "\n",
    "  for img in distances:\n",
    "    print(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'label': 19, 'distance': 6.355424425784916e-06}\n",
      "{'label': 33, 'distance': 1.2740918489579356e-05}\n",
      "{'label': 54, 'distance': 6.85116641512525e-05}\n",
      "{'label': 49, 'distance': 8.314856743319156e-05}\n",
      "{'label': 28, 'distance': 0.00016883047586624644}\n",
      "{'label': 36, 'distance': 0.0002122873813686887}\n",
      "{'label': 35, 'distance': 0.00023350326417372972}\n",
      "{'label': 44, 'distance': 0.0002406295461878455}\n",
      "{'label': 9, 'distance': 0.00024234313765943418}\n",
      "{'label': 10, 'distance': 0.0002640071831902495}\n"
     ]
    }
   ],
   "source": [
    "match selected_latent_space:\n",
    "\n",
    "  case \"\" | \"image_sim\":\n",
    "    \n",
    "    extract_similarities_ls1_ls4(selected_latent_space, selected_dim_reduction_method, selected_feature_model, data, image_id)\n",
    "\n",
    "  case \"label_sim\":\n",
    "\n",
    "    extract_similarities_ls3(selected_dim_reduction_method, data, image_id)\n",
    "\n",
    "  case \"cp\":\n",
    "\n",
    "    extract_similarities_ls2(data, image_id)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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