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task 11 code, to be tested
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Phase 2/task_11.ipynb
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Phase 2/task_11.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"from utils import *\n",
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"warnings.filterwarnings('ignore')\n",
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"%matplotlib inline\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"fd_collection = getCollection(\"team_5_mwdb_phase_2\", \"fd_collection\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"class ImageGraph:\n",
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" \"\"\"\n",
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" Construct image-similarity graph and apply personalized pagerank algorithm to get PPR scores and find relevant images\n",
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" \"\"\"\n",
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"\n",
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" def __init__(self, fd_collection, verbose=False):\n",
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" self.fd_collection = fd_collection\n",
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" self.similarity_graph = None\n",
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" self.verbose = verbose\n",
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"\n",
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" def create_similarity_graph(\n",
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" self, n, feature_model, semantic_data=None, dim_reduction_method=None\n",
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" ):\n",
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" if semantic_data is None:\n",
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" # Similarity graph from feature models\n",
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" image_sim_matrix = find_image_image_similarity(fd_collection, feature_model)\n",
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" if self.verbose:\n",
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" print(\"Image-image similarity matrix constructed from\", feature_model)\n",
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" else:\n",
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" # Similarity graph from image-semantic latent space\n",
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" # LS3, LS4\n",
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" if \"sim-matrix\" in semantic_data:\n",
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" # for now, don't work with LS3\n",
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" # TODO: do similar to task 7 and 10\n",
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" image_sim_matrix = np.array(semantic_data[\"sim-matrix\"])\n",
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" if image_sim_matrix.shape[0] != NUM_IMAGES:\n",
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" raise TypeError(\n",
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" \"Functionality to construct similarity graph from LS3 not yet done\"\n",
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" )\n",
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" if self.verbose:\n",
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" print(\"Using image-image similarity matrix from semantic data\")\n",
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" # LS1, LS2\n",
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" else:\n",
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" image_semantic = semantic_data[\"image-semantic\"]\n",
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" # SVD, CP\n",
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" if \"semantics-core\" in semantic_data:\n",
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" semantics_core = np.array(semantic_data[\"semantics-core\"])\n",
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" if len(semantics_core.shape) == 1:\n",
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" semantics_core = np.diag(semantics_core)\n",
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" image_semantic = np.matmul(image_semantic, semantics_core)\n",
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"\n",
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" image_sim_matrix = np.zeros((NUM_IMAGES, NUM_IMAGES))\n",
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" # Calculate half and fill the other\n",
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" for i in range(NUM_IMAGES):\n",
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" for j in range(i + 1, NUM_IMAGES):\n",
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" # Note: lower the value, lower the distance => higher the similarity\n",
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" distance_measure = (\n",
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" kl_divergence_measure\n",
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" if dim_reduction_method == \"lda\"\n",
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" else euclidean_distance_measure\n",
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" )\n",
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" image_sim_matrix[j][i] = distance_measure(\n",
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" np.array(image_semantic[i]),\n",
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" np.array(image_semantic[j]),\n",
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" )\n",
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" image_sim_matrix[i][j] = image_sim_matrix[j][i]\n",
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" if self.verbose:\n",
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" print(\n",
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" \"Image-image similarity matrix constructed from given image-semantic\"\n",
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" )\n",
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"\n",
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" # Create an unweighted directed similarity graph, with no self-loops\n",
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" self.similarity_graph = []\n",
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" for i in range(len(image_sim_matrix)):\n",
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" # distances should be small, so sort in ascending order\n",
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" similar_image_ids = np.argsort(image_sim_matrix[i])[\n",
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" 1 : n + 1\n",
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" ] # exclude self\n",
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" self.similarity_graph.extend(\n",
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" [(i * 2, j * 2) for j in similar_image_ids]\n",
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" ) # i*2 cuz even IDs\n",
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" if self.verbose:\n",
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" print(\"Similarity graph created\")\n",
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"\n",
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" def personalized_pagerank(\n",
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" self, label, m, damping_factor=0.85, max_iter=1000, tol=1e-6\n",
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" ):\n",
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" import time\n",
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" if self.similarity_graph is None:\n",
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" raise ValueError(\n",
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" \"Similarity graph not created. Call create_similarity_graph() first.\"\n",
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" )\n",
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"\n",
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" label_indices = [\n",
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" img[\"image_id\"] for img in self.fd_collection.find({\"true_label\": label})\n",
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" ] # IDs of images with the given label\n",
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"\n",
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" pr_scores = np.ones(NUM_IMAGES) / NUM_IMAGES # Initialize PageRank scores\n",
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" if self.verbose:\n",
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" print(\"Initialized pagerank scores\")\n",
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"\n",
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" for _iter in range(max_iter):\n",
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" prev_scores = np.copy(pr_scores)\n",
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" # for every node,\n",
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" for i in range(NUM_IMAGES):\n",
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" tic = time.time()\n",
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" # add sum of connected nodes' PR scores\n",
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" pr_scores[i] = damping_factor * sum(\n",
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" pr_scores[j]\n",
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" for j in range(NUM_IMAGES)\n",
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" if (i * 2, j * 2) in self.similarity_graph\n",
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" # and add the prob for random teleport *if node in given label*\n",
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" ) + (1 - damping_factor) * (1 if i * 2 in label_indices else 0) / len(\n",
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" label_indices\n",
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" )\n",
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" toc = time.time()\n",
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" print(toc-tic)\n",
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"\n",
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" pr_scores /= sum(pr_scores) # Normalize\n",
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"\n",
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" # check for convergence\n",
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" conv_tol = np.sum(np.abs(prev_scores - pr_scores))\n",
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" if self.verbose:\n",
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" print(f\"Iter {_iter}, conv_tol={conv_tol}\")\n",
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" if conv_tol < tol:\n",
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" if self.verbose:\n",
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" print(f\"Converged\")\n",
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" break\n",
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"\n",
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" # Select top m images based on PageRank scores\n",
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" top_m_indices = np.argsort(pr_scores)[::-1][\n",
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" :m\n",
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" ] # sort indices, reverse and take top m\n",
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" return top_m_indices * 2 # again, even IDs\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"image_sim-cm_fd-svd-10-semantics.json loaded\n"
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]
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}
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],
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"source": [
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"n = int(input(\"Enter value of n (no. of edges for each image in similarity graph): \"))\n",
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"if n < 1:\n",
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" raise ValueError(\"n should be a positive integer\")\n",
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"\n",
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"l = int(input(\"Enter target label l:\"))\n",
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"if l < 0 or l > 100:\n",
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" raise ValueError(\"l should be in range[0,100]\")\n",
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"\n",
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"m = int(input(\"Enter value of m (no. of significant images relative to given label): \"))\n",
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"if m < 1:\n",
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" raise ValueError(\"m should be a positive integer\")\n",
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"\n",
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"given_ls = int(\n",
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" (input(\"Enter 0 to select a feature model, 1 to select a latent space: \"))\n",
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")\n",
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"\n",
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"selected_feature_model = valid_feature_models[\n",
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" str(input(\"Enter feature model - one of \" + str(list(valid_feature_models.keys()))))\n",
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"]\n",
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"\n",
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"if given_ls:\n",
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" selected_latent_space = valid_latent_spaces[\n",
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" str(\n",
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" input(\n",
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" \"Enter latent space - one of \" + str(list(valid_latent_spaces.keys()))\n",
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" )\n",
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" )\n",
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" ]\n",
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"\n",
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" k = int(input(\"Enter value of k (no. of latent semantics): \"))\n",
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" if k < 1:\n",
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" raise ValueError(\"k should be a positive integer\")\n",
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"\n",
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" if selected_latent_space != \"cp\":\n",
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" selected_dim_reduction_method = str(\n",
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" input(\n",
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" \"Enter dimensionality reduction method - one of \"\n",
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" + str(list(valid_dim_reduction_methods.keys()))\n",
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" )\n",
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" )\n",
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"\n",
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" # Loading latent semantics\n",
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" match selected_latent_space:\n",
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" # LS1\n",
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" case \"\":\n",
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" file_prefix = (\n",
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" f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}\"\n",
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" )\n",
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" file_name = file_prefix + \"-semantics.json\"\n",
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" model_name = file_prefix + \"-model.joblib\"\n",
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" if os.path.exists(file_name):\n",
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" data = json.load(open(file_name))\n",
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" print(file_name + \" loaded\")\n",
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" else:\n",
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" raise Exception(file_name + \" does not exist\")\n",
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" # LDA model\n",
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" if selected_dim_reduction_method == \"lda\":\n",
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" if os.path.exists(model_name):\n",
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" data_model = load(model_name)\n",
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" print(model_name + \" loaded\")\n",
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" else:\n",
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" raise Exception(model_name + \" does not exist\")\n",
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" # LS2\n",
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" case \"cp\":\n",
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" file_name = f\"{selected_feature_model}-cp-{k}-semantics.json\"\n",
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" if os.path.exists(file_name):\n",
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" data = json.load(open(file_name))\n",
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" print(file_name + \" loaded\")\n",
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" else:\n",
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" raise Exception(file_name + \" does not exist\")\n",
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" # LS3, LS4\n",
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" case _:\n",
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" file_name = f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"\n",
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" if os.path.exists(file_name):\n",
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" data = json.load(open(file_name))\n",
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" print(file_name + \" loaded\")\n",
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" else:\n",
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" raise Exception(file_name + \" does not exist\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Using image-image similarity matrix from semantic data\n",
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"Similarity graph created\n",
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"Initialized pagerank scores\n"
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]
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},
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{
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"ename": "KeyboardInterrupt",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
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"\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 <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_11.ipynb#W5sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m img_graph \u001b[39m=\u001b[39m ImageGraph(fd_collection, \u001b[39mTrue\u001b[39;00m)\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_11.ipynb#W5sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m img_graph\u001b[39m.\u001b[39mcreate_similarity_graph(\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_11.ipynb#W5sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m n,\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_11.ipynb#W5sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m selected_feature_model,\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_11.ipynb#W5sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m data,\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_11.ipynb#W5sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m selected_dim_reduction_method\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_11.ipynb#W5sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m )\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_11.ipynb#W5sZmlsZQ%3D%3D?line=12'>13</a>\u001b[0m imgs \u001b[39m=\u001b[39m img_graph\u001b[39m.\u001b[39;49mpersonalized_pagerank(l, m)\n",
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"\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 <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_11.ipynb#W5sZmlsZQ%3D%3D?line=92'>93</a>\u001b[0m \u001b[39m# for every node,\u001b[39;00m\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_11.ipynb#W5sZmlsZQ%3D%3D?line=93'>94</a>\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(NUM_IMAGES):\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_11.ipynb#W5sZmlsZQ%3D%3D?line=94'>95</a>\u001b[0m \u001b[39m# add sum of connected nodes' PR scores\u001b[39;00m\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_11.ipynb#W5sZmlsZQ%3D%3D?line=95'>96</a>\u001b[0m pr_scores[i] \u001b[39m=\u001b[39m damping_factor \u001b[39m*\u001b[39m \u001b[39msum\u001b[39;49m(\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_11.ipynb#W5sZmlsZQ%3D%3D?line=96'>97</a>\u001b[0m pr_scores[j]\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_11.ipynb#W5sZmlsZQ%3D%3D?line=97'>98</a>\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 <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_11.ipynb#W5sZmlsZQ%3D%3D?line=98'>99</a>\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 <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_11.ipynb#W5sZmlsZQ%3D%3D?line=99'>100</a>\u001b[0m \u001b[39m# and add the prob for random teleport *if node in given label*\u001b[39;49;00m\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_11.ipynb#W5sZmlsZQ%3D%3D?line=100'>101</a>\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 <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_11.ipynb#W5sZmlsZQ%3D%3D?line=101'>102</a>\u001b[0m label_indices\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_11.ipynb#W5sZmlsZQ%3D%3D?line=102'>103</a>\u001b[0m )\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_11.ipynb#W5sZmlsZQ%3D%3D?line=103'>104</a>\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 <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_11.ipynb#W5sZmlsZQ%3D%3D?line=105'>106</a>\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 <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_11.ipynb#W5sZmlsZQ%3D%3D?line=92'>93</a>\u001b[0m \u001b[39m# for every node,\u001b[39;00m\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_11.ipynb#W5sZmlsZQ%3D%3D?line=93'>94</a>\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(NUM_IMAGES):\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_11.ipynb#W5sZmlsZQ%3D%3D?line=94'>95</a>\u001b[0m \u001b[39m# add sum of connected nodes' PR scores\u001b[39;00m\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_11.ipynb#W5sZmlsZQ%3D%3D?line=95'>96</a>\u001b[0m pr_scores[i] \u001b[39m=\u001b[39m damping_factor \u001b[39m*\u001b[39m \u001b[39msum\u001b[39m(\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_11.ipynb#W5sZmlsZQ%3D%3D?line=96'>97</a>\u001b[0m pr_scores[j]\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_11.ipynb#W5sZmlsZQ%3D%3D?line=97'>98</a>\u001b[0m \u001b[39mfor\u001b[39;00m j \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(NUM_IMAGES)\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_11.ipynb#W5sZmlsZQ%3D%3D?line=98'>99</a>\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 <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_11.ipynb#W5sZmlsZQ%3D%3D?line=99'>100</a>\u001b[0m \u001b[39m# and add the prob for random teleport *if node in given label*\u001b[39;00m\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_11.ipynb#W5sZmlsZQ%3D%3D?line=100'>101</a>\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 <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_11.ipynb#W5sZmlsZQ%3D%3D?line=101'>102</a>\u001b[0m label_indices\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_11.ipynb#W5sZmlsZQ%3D%3D?line=102'>103</a>\u001b[0m )\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_11.ipynb#W5sZmlsZQ%3D%3D?line=103'>104</a>\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 <a href='vscode-notebook-cell:/c%3A/Kaushik/ASU/CSE%20515%20-%20Multimedia%20and%20Web%20Databases/Project/Phase%202/task_11.ipynb#W5sZmlsZQ%3D%3D?line=105'>106</a>\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": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"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": "3.10.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@ -310,7 +310,7 @@
|
||||
"\n",
|
||||
" distances = []\n",
|
||||
" for i in range(NUM_LABELS):\n",
|
||||
" if i != label:\n",
|
||||
" if i != img_label:\n",
|
||||
" distances.append(\n",
|
||||
" {\n",
|
||||
" \"label\": i,\n",
|
||||
@ -326,7 +326,7 @@
|
||||
" similar_images = []\n",
|
||||
" for i in range(len(dataset)):\n",
|
||||
" _, l = dataset[i]\n",
|
||||
" if l == label:\n",
|
||||
" if l == img_label:\n",
|
||||
" similar_images.append(i)\n",
|
||||
"\n",
|
||||
" similar_images = random.sample(similar_images, k_2)\n",
|
||||
|
||||
@ -5,6 +5,7 @@ import random
|
||||
import cv2
|
||||
import numpy as np
|
||||
from scipy.stats import pearsonr
|
||||
|
||||
# from scipy.sparse.linalg import svds
|
||||
# from sklearn.decomposition import NMF
|
||||
from sklearn.decomposition import LatentDirichletAllocation
|
||||
@ -355,11 +356,14 @@ def pearson_distance_measure(img_1_fd, img_2_fd):
|
||||
# such that lower distance implies more similarity
|
||||
return 0.5 * (1 - pearsonr(img_1_fd_reshaped, img_2_fd_reshaped).statistic)
|
||||
|
||||
|
||||
def kl_divergence_measure(p, q):
|
||||
p_f = p.flatten()
|
||||
q_f = q.flatten()
|
||||
# Avoid division by zero
|
||||
epsilon = 1e-10
|
||||
|
||||
return np.sum(p * np.log((p + epsilon) / (q + epsilon)))
|
||||
return np.sum(p_f * np.log((p_f + epsilon) / (q_f + epsilon)))
|
||||
|
||||
|
||||
valid_feature_models = {
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user