{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from utils import *\n",
"warnings.filterwarnings('ignore')\n",
"%matplotlib inline\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"fd_collection = getCollection(\"team_5_mwdb_phase_2\", \"fd_collection\")\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"class ImageGraph:\n",
" \"\"\"\n",
" Construct image-similarity graph and apply personalized pagerank algorithm to get PPR scores and find relevant images\n",
" \"\"\"\n",
"\n",
" def __init__(self, fd_collection, verbose=False):\n",
" self.fd_collection = fd_collection\n",
" self.similarity_graph = None\n",
" self.verbose = verbose\n",
"\n",
" def create_similarity_graph(\n",
" self, n, feature_model, semantic_data=None, dim_reduction_method=None\n",
" ):\n",
" if semantic_data is None:\n",
" # Similarity graph from feature models\n",
" image_sim_matrix = find_image_image_similarity(fd_collection, feature_model)\n",
" if self.verbose:\n",
" print(\"Image-image similarity matrix constructed from\", feature_model)\n",
" else:\n",
" # Similarity graph from image-semantic latent space\n",
" # LS3, LS4\n",
" if \"sim-matrix\" in semantic_data:\n",
" # for now, don't work with LS3\n",
" # TODO: do similar to task 7 and 10\n",
" image_sim_matrix = np.array(semantic_data[\"sim-matrix\"])\n",
" if image_sim_matrix.shape[0] != NUM_IMAGES:\n",
" raise TypeError(\n",
" \"Functionality to construct similarity graph from LS3 not yet done\"\n",
" )\n",
" if self.verbose:\n",
" print(\"Using image-image similarity matrix from semantic data\")\n",
" # LS1, LS2\n",
" else:\n",
" image_semantic = semantic_data[\"image-semantic\"]\n",
" # SVD, CP\n",
" if \"semantics-core\" in semantic_data:\n",
" semantics_core = np.array(semantic_data[\"semantics-core\"])\n",
" if len(semantics_core.shape) == 1:\n",
" semantics_core = np.diag(semantics_core)\n",
" image_semantic = np.matmul(image_semantic, semantics_core)\n",
"\n",
" image_sim_matrix = np.zeros((NUM_IMAGES, NUM_IMAGES))\n",
" # Calculate half and fill the other\n",
" for i in range(NUM_IMAGES):\n",
" for j in range(i + 1, NUM_IMAGES):\n",
" # Note: lower the value, lower the distance => higher the similarity\n",
" distance_measure = (\n",
" kl_divergence_measure\n",
" if dim_reduction_method == \"lda\"\n",
" else euclidean_distance_measure\n",
" )\n",
" image_sim_matrix[j][i] = distance_measure(\n",
" np.array(image_semantic[i]),\n",
" np.array(image_semantic[j]),\n",
" )\n",
" image_sim_matrix[i][j] = image_sim_matrix[j][i]\n",
" if self.verbose:\n",
" print(\n",
" \"Image-image similarity matrix constructed from given image-semantic\"\n",
" )\n",
"\n",
" # Create an unweighted directed similarity graph, with no self-loops\n",
" self.similarity_graph = []\n",
" for i in range(len(image_sim_matrix)):\n",
" # distances should be small, so sort in ascending order\n",
" similar_image_ids = np.argsort(image_sim_matrix[i])[\n",
" 1 : n + 1\n",
" ] # exclude self\n",
" self.similarity_graph.extend(\n",
" [(i * 2, j * 2) for j in similar_image_ids]\n",
" ) # i*2 cuz even IDs\n",
" if self.verbose:\n",
" print(\"Similarity graph created\")\n",
"\n",
" def personalized_pagerank(\n",
" self, label, m, damping_factor=0.85, max_iter=1000, tol=1e-6\n",
" ):\n",
" import time\n",
" if self.similarity_graph is None:\n",
" raise ValueError(\n",
" \"Similarity graph not created. Call create_similarity_graph() first.\"\n",
" )\n",
"\n",
" label_indices = [\n",
" img[\"image_id\"] for img in self.fd_collection.find({\"true_label\": label})\n",
" ] # IDs of images with the given label\n",
"\n",
" pr_scores = np.ones(NUM_IMAGES) / NUM_IMAGES # Initialize PageRank scores\n",
" if self.verbose:\n",
" print(\"Initialized pagerank scores\")\n",
"\n",
" for _iter in range(max_iter):\n",
" prev_scores = np.copy(pr_scores)\n",
" # for every node,\n",
" for i in range(NUM_IMAGES):\n",
" tic = time.time()\n",
" # add sum of connected nodes' PR scores\n",
" pr_scores[i] = damping_factor * sum(\n",
" pr_scores[j]\n",
" for j in range(NUM_IMAGES)\n",
" if (i * 2, j * 2) in self.similarity_graph\n",
" # and add the prob for random teleport *if node in given label*\n",
" ) + (1 - damping_factor) * (1 if i * 2 in label_indices else 0) / len(\n",
" label_indices\n",
" )\n",
" toc = time.time()\n",
" print(toc-tic)\n",
"\n",
" pr_scores /= sum(pr_scores) # Normalize\n",
"\n",
" # check for convergence\n",
" conv_tol = np.sum(np.abs(prev_scores - pr_scores))\n",
" if self.verbose:\n",
" print(f\"Iter {_iter}, conv_tol={conv_tol}\")\n",
" if conv_tol < tol:\n",
" if self.verbose:\n",
" print(f\"Converged\")\n",
" break\n",
"\n",
" # Select top m images based on PageRank scores\n",
" top_m_indices = np.argsort(pr_scores)[::-1][\n",
" :m\n",
" ] # sort indices, reverse and take top m\n",
" return top_m_indices * 2 # again, even IDs\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"image_sim-cm_fd-svd-10-semantics.json loaded\n"
]
}
],
"source": [
"n = int(input(\"Enter value of n (no. of edges for each image in similarity graph): \"))\n",
"if n < 1:\n",
" raise ValueError(\"n should be a positive integer\")\n",
"\n",
"l = int(input(\"Enter target label l:\"))\n",
"if l < 0 or l > 100:\n",
" raise ValueError(\"l should be in range[0,100]\")\n",
"\n",
"m = int(input(\"Enter value of m (no. of significant images relative to given label): \"))\n",
"if m < 1:\n",
" raise ValueError(\"m should be a positive integer\")\n",
"\n",
"given_ls = int(\n",
" (input(\"Enter 0 to select a feature model, 1 to select a latent space: \"))\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",
"if given_ls:\n",
" selected_latent_space = valid_latent_spaces[\n",
" str(\n",
" input(\n",
" \"Enter latent space - one of \" + str(list(valid_latent_spaces.keys()))\n",
" )\n",
" )\n",
" ]\n",
"\n",
" k = int(input(\"Enter value of k (no. of latent semantics): \"))\n",
" if k < 1:\n",
" raise ValueError(\"k should be a positive integer\")\n",
"\n",
" if selected_latent_space != \"cp\":\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",
" # Loading latent semantics\n",
" match selected_latent_space:\n",
" # LS1\n",
" case \"\":\n",
" file_prefix = (\n",
" f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}\"\n",
" )\n",
" file_name = file_prefix + \"-semantics.json\"\n",
" model_name = file_prefix + \"-model.joblib\"\n",
" if os.path.exists(file_name):\n",
" data = json.load(open(file_name))\n",
" print(file_name + \" loaded\")\n",
" else:\n",
" raise Exception(file_name + \" does not exist\")\n",
" # LDA model\n",
" if selected_dim_reduction_method == \"lda\":\n",
" if os.path.exists(model_name):\n",
" data_model = load(model_name)\n",
" print(model_name + \" loaded\")\n",
" else:\n",
" raise Exception(model_name + \" does not exist\")\n",
" # LS2\n",
" case \"cp\":\n",
" file_name = f\"{selected_feature_model}-cp-{k}-semantics.json\"\n",
" if os.path.exists(file_name):\n",
" data = json.load(open(file_name))\n",
" print(file_name + \" loaded\")\n",
" else:\n",
" raise Exception(file_name + \" does not exist\")\n",
" # LS3, LS4\n",
" case _:\n",
" file_name = f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"\n",
" if os.path.exists(file_name):\n",
" data = json.load(open(file_name))\n",
" print(file_name + \" loaded\")\n",
" else:\n",
" raise Exception(file_name + \" does not exist\")\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using image-image similarity matrix from semantic data\n",
"Similarity graph created\n",
"Initialized pagerank scores\n"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[1;32mc:\\Kaushik\\ASU\\CSE 515 - Multimedia and Web Databases\\Project\\Phase 2\\task_11.ipynb Cell 5\u001b[0m line \u001b[0;36m1\n\u001b[0;32m 6\u001b[0m img_graph \u001b[39m=\u001b[39m ImageGraph(fd_collection, \u001b[39mTrue\u001b[39;00m)\n\u001b[0;32m 7\u001b[0m img_graph\u001b[39m.\u001b[39mcreate_similarity_graph(\n\u001b[0;32m 8\u001b[0m n,\n\u001b[0;32m 9\u001b[0m selected_feature_model,\n\u001b[0;32m 10\u001b[0m data,\n\u001b[0;32m 11\u001b[0m selected_dim_reduction_method\n\u001b[0;32m 12\u001b[0m )\n\u001b[1;32m---> 13\u001b[0m imgs \u001b[39m=\u001b[39m img_graph\u001b[39m.\u001b[39;49mpersonalized_pagerank(l, m)\n",
"\u001b[1;32mc:\\Kaushik\\ASU\\CSE 515 - Multimedia and Web Databases\\Project\\Phase 2\\task_11.ipynb Cell 5\u001b[0m line \u001b[0;36m9\n\u001b[0;32m 93\u001b[0m \u001b[39m# for every node,\u001b[39;00m\n\u001b[0;32m 94\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(NUM_IMAGES):\n\u001b[0;32m 95\u001b[0m \u001b[39m# add sum of connected nodes' PR scores\u001b[39;00m\n\u001b[1;32m---> 96\u001b[0m pr_scores[i] \u001b[39m=\u001b[39m damping_factor \u001b[39m*\u001b[39m \u001b[39msum\u001b[39;49m(\n\u001b[0;32m 97\u001b[0m pr_scores[j]\n\u001b[0;32m 98\u001b[0m \u001b[39mfor\u001b[39;49;00m j \u001b[39min\u001b[39;49;00m \u001b[39mrange\u001b[39;49m(NUM_IMAGES)\n\u001b[0;32m 99\u001b[0m \u001b[39mif\u001b[39;49;00m (i \u001b[39m*\u001b[39;49m \u001b[39m2\u001b[39;49m, j \u001b[39m*\u001b[39;49m \u001b[39m2\u001b[39;49m) \u001b[39min\u001b[39;49;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msimilarity_graph\n\u001b[0;32m 100\u001b[0m \u001b[39m# and add the prob for random teleport *if node in given label*\u001b[39;49;00m\n\u001b[0;32m 101\u001b[0m ) \u001b[39m+\u001b[39m (\u001b[39m1\u001b[39m \u001b[39m-\u001b[39m damping_factor) \u001b[39m*\u001b[39m (\u001b[39m1\u001b[39m \u001b[39mif\u001b[39;00m i \u001b[39m*\u001b[39m \u001b[39m2\u001b[39m \u001b[39min\u001b[39;00m label_indices \u001b[39melse\u001b[39;00m \u001b[39m0\u001b[39m) \u001b[39m/\u001b[39m \u001b[39mlen\u001b[39m(\n\u001b[0;32m 102\u001b[0m label_indices\n\u001b[0;32m 103\u001b[0m )\n\u001b[0;32m 104\u001b[0m pr_scores \u001b[39m/\u001b[39m\u001b[39m=\u001b[39m \u001b[39msum\u001b[39m(pr_scores) \u001b[39m# Normalize\u001b[39;00m\n\u001b[0;32m 106\u001b[0m \u001b[39m# check for convergence\u001b[39;00m\n",
"\u001b[1;32mc:\\Kaushik\\ASU\\CSE 515 - Multimedia and Web Databases\\Project\\Phase 2\\task_11.ipynb Cell 5\u001b[0m line \u001b[0;36m9\n\u001b[0;32m 93\u001b[0m \u001b[39m# for every node,\u001b[39;00m\n\u001b[0;32m 94\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(NUM_IMAGES):\n\u001b[0;32m 95\u001b[0m \u001b[39m# add sum of connected nodes' PR scores\u001b[39;00m\n\u001b[0;32m 96\u001b[0m pr_scores[i] \u001b[39m=\u001b[39m damping_factor \u001b[39m*\u001b[39m \u001b[39msum\u001b[39m(\n\u001b[0;32m 97\u001b[0m pr_scores[j]\n\u001b[0;32m 98\u001b[0m \u001b[39mfor\u001b[39;00m j \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(NUM_IMAGES)\n\u001b[1;32m---> 99\u001b[0m \u001b[39mif\u001b[39;00m (i \u001b[39m*\u001b[39;49m \u001b[39m2\u001b[39;49m, j \u001b[39m*\u001b[39;49m \u001b[39m2\u001b[39;49m) \u001b[39min\u001b[39;49;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msimilarity_graph\n\u001b[0;32m 100\u001b[0m \u001b[39m# and add the prob for random teleport *if node in given label*\u001b[39;00m\n\u001b[0;32m 101\u001b[0m ) \u001b[39m+\u001b[39m (\u001b[39m1\u001b[39m \u001b[39m-\u001b[39m damping_factor) \u001b[39m*\u001b[39m (\u001b[39m1\u001b[39m \u001b[39mif\u001b[39;00m i \u001b[39m*\u001b[39m \u001b[39m2\u001b[39m \u001b[39min\u001b[39;00m label_indices \u001b[39melse\u001b[39;00m \u001b[39m0\u001b[39m) \u001b[39m/\u001b[39m \u001b[39mlen\u001b[39m(\n\u001b[0;32m 102\u001b[0m label_indices\n\u001b[0;32m 103\u001b[0m )\n\u001b[0;32m 104\u001b[0m pr_scores \u001b[39m/\u001b[39m\u001b[39m=\u001b[39m \u001b[39msum\u001b[39m(pr_scores) \u001b[39m# Normalize\u001b[39;00m\n\u001b[0;32m 106\u001b[0m \u001b[39m# check for convergence\u001b[39;00m\n",
"\u001b[1;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"if not given_ls:\n",
" img_graph = ImageGraph(fd_collection, True)\n",
" img_graph.create_similarity_graph(n, selected_feature_model)\n",
" imgs = img_graph.personalized_pagerank(l, m)\n",
"else:\n",
" img_graph = ImageGraph(fd_collection, True)\n",
" img_graph.create_similarity_graph(\n",
" n,\n",
" selected_feature_model,\n",
" data,\n",
" selected_dim_reduction_method\n",
" )\n",
" imgs = img_graph.personalized_pagerank(l, m)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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