mirror of
https://github.com/20kaushik02/CSE515_MWDB_Project.git
synced 2025-12-06 07:54:07 +00:00
311 lines
10 KiB
Plaintext
311 lines
10 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 197,
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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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"The autoreload extension is already loaded. To reload it, use:\n",
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" %reload_ext autoreload\n"
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]
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}
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],
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"source": [
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"%load_ext autoreload\n",
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"%autoreload 2"
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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": 198,
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"import os\n",
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"import numpy as np\n",
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"from utils import *\n",
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"import math\n",
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"import heapq"
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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": 199,
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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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"all_images = fd_collection.find()\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": 200,
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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-kmeans-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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"selected_latent_space = valid_latent_spaces[\n",
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" str(input(\"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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"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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"k = int(input(\"Enter value of k: \"))\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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"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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"label = int(input(\"Enter label: \"))\n",
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"if label < 0 and label > 100:\n",
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" raise ValueError(\"k should be between 0 and 100\")\n",
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"\n",
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"knum = int(input(\"Enter value of knum: \"))\n",
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"if knum < 1:\n",
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" raise ValueError(\"knum should be a positive integer\")\n",
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"\n",
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"label_rep = calculate_label_representatives(fd_collection, label, selected_feature_model)\n",
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"\n",
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"match selected_latent_space:\n",
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" case \"\":\n",
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" if os.path.exists(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"):\n",
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" data = json.load(open(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"))\n",
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" print(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json loaded\")\n",
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" else:\n",
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" print(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json does not exist\")\n",
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" case \"cp\":\n",
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" if os.path.exists(f\"{selected_feature_model}-cp-{k}-semantics.json\"):\n",
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" data = json.load(open(f\"{selected_feature_model}-cp-{k}-semantics.json\"))\n",
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" print(f\"{selected_feature_model}-cp-{k}-semantics.json loaded\")\n",
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" else: \n",
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" print(f\"{selected_feature_model}-cp-{k}-semantics.json does not exist\")\n",
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" case _:\n",
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" if os.path.exists(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"):\n",
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" data = json.load(open(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"))\n",
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" print(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json loaded\")\n",
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" else:\n",
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" print(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json 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": 201,
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"metadata": {},
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"outputs": [],
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"source": [
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"def extract_similarities_ls1_ls4(latent_space, dim_reduction, data, label, label_rep):\n",
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"\n",
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" match dim_reduction:\n",
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"\n",
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" case 'svd':\n",
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" U = np.array(data[\"image-semantic\"])\n",
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" S = np.array(data[\"semantics-core\"])\n",
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" if len(S.shape) == 1:\n",
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" S = np.diag(S)\n",
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" V = np.transpose(np.array(data[\"semantic-feature\"]))\n",
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"\n",
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" if latent_space == \"image_sim\":\n",
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" label_vectors = []\n",
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" length = len(U)\n",
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" for i in range(length):\n",
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" if all_images[i][\"true_label\"] == label:\n",
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" label_vectors.append(U[i])\n",
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" label_rep = [sum(col) / len(col) for col in zip(*label_vectors)]\n",
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" \n",
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" comparison_feature_space = np.matmul(U, S)\n",
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"\n",
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" if latent_space == \"image_sim\":\n",
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" comparison_vector = np.matmul(label_rep, S)\n",
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" else:\n",
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" comparison_vector = np.matmul(np.matmul(label_rep, V), S)\n",
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" \n",
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" case \"nmf\":\n",
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" H = np.array(data['semantic-feature'])\n",
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" comparison_feature_space = W = np.array(data['image-semantic'])\n",
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" if latent_space == \"image_sim\":\n",
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" label_vectors = []\n",
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" length = len(W)\n",
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" for i in range(length):\n",
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" if all_images[i][\"true_label\"] == label:\n",
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" label_vectors.append(W[i])\n",
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" label_rep = [sum(col) / len(col) for col in zip(*label_vectors)]\n",
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"\n",
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" if latent_space == \"image_sim\":\n",
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" comparison_vector = label_rep\n",
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" else:\n",
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" comparison_vector = np.matmul(label_rep, np.transpose(H))\n",
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"\n",
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" case \"kmeans\":\n",
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" comparison_vector = []\n",
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" comparison_feature_space = np.array(data[\"image-semantic\"])\n",
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" S = np.array(data[\"semantic-feature\"])\n",
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"\n",
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" if latent_space == \"image_sim\":\n",
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" sim_matrix = np.array(data[\"sim-matrix\"])\n",
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" label_vectors = []\n",
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" length = len(sim_matrix)\n",
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" for i in range(length):\n",
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" if all_images[i][\"true_label\"] == label:\n",
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" label_vectors.append(sim_matrix[i])\n",
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" label_rep = [sum(col) / len(col) for col in zip(*label_vectors)]\n",
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"\n",
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"\n",
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" for centroid in S:\n",
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" comparison_vector.append(math.dist(label_rep, centroid))\n",
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"\n",
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" n = len(comparison_feature_space)\n",
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"\n",
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" distances = []\n",
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" for i in range(n):\n",
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" if all_images[i][\"true_label\"] != label:\n",
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" distances.append({\"image_id\": i, \"label\": all_images[i][\"true_label\"], \"distance\": math.dist(comparison_vector, comparison_feature_space[i])})\n",
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"\n",
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" distances = sorted(distances, key=lambda x: x[\"distance\"], reverse=False)\n",
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"\n",
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" similar_labels = []\n",
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" unique_labels = set()\n",
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"\n",
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" for img in distances:\n",
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" if img['label'] not in unique_labels:\n",
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" similar_labels.append(img)\n",
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" unique_labels.add(img[\"label\"])\n",
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"\n",
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" if len(similar_labels) == knum:\n",
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" break\n",
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"\n",
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"\n",
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" for x in similar_labels:\n",
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" print(x)"
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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": 202,
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"metadata": {},
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"outputs": [],
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"source": [
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"def extract_similarities_ls3(dim_reduction, data, label):\n",
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"\n",
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" match dim_reduction:\n",
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"\n",
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" case 'svd':\n",
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" U = np.array(data[\"image-semantic\"])\n",
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" S = np.array(data[\"semantics-core\"])\n",
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" V = np.transpose(np.array(data[\"semantic-feature\"]))\n",
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"\n",
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" comparison_feature_space = np.matmul(U, S)\n",
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" comparison_vector = comparison_feature_space[label]\n",
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" \n",
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" case \"nmf\":\n",
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" comparison_feature_space = np.array(data['image-semantic'])\n",
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" comparison_vector = comparison_feature_space[label]\n",
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"\n",
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" case \"kmeans\":\n",
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" comparison_feature_space = np.array(data[\"image-semantic\"])\n",
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" comparison_vector = comparison_feature_space[label]\n",
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"\n",
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" n = len(comparison_feature_space)\n",
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" distances = []\n",
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" for i in range(n):\n",
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" if i != label:\n",
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" distances.append({\"label\": i, \"distance\": math.dist(comparison_vector, comparison_feature_space[i])})\n",
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"\n",
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" distances = sorted(distances, key=lambda x: x[\"distance\"], reverse=False)[:knum]\n",
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"\n",
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" for x in distances:\n",
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" print(x)"
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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": 203,
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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_id': 1596, 'label': 13, 'distance': 10.699607616770502}\n",
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"{'image_id': 81, 'label': 0, 'distance': 11.42726536242745}\n",
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"{'image_id': 3045, 'label': 57, 'distance': 12.5398964971548}\n",
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"{'image_id': 311, 'label': 1, 'distance': 13.106117374912184}\n",
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"{'image_id': 2671, 'label': 47, 'distance': 14.239608716065096}\n",
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"{'image_id': 1923, 'label': 23, 'distance': 15.409297843450119}\n",
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"{'image_id': 3471, 'label': 74, 'distance': 15.417780769047727}\n",
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"{'image_id': 4108, 'label': 94, 'distance': 17.628035952336866}\n",
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"{'image_id': 1547, 'label': 12, 'distance': 19.28128511589925}\n",
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"{'image_id': 3115, 'label': 59, 'distance': 19.762521112658867}\n"
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]
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}
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],
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"source": [
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"match selected_latent_space:\n",
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"\n",
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" case \"\" | \"image_sim\":\n",
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" \n",
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" extract_similarities_ls1_ls4(selected_latent_space, selected_dim_reduction_method, data, label, label_rep)\n",
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"\n",
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" case \"label_sim\":\n",
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"\n",
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" extract_similarities_ls3(selected_dim_reduction_method, data, label)\n",
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" "
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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