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https://github.com/20kaushik02/CSE515_MWDB_Project.git
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task10
This commit is contained in:
Niraj Sonje
parent
3ca1614746
commit
c652a6606e
290
Phase 2/task_10.ipynb
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290
Phase 2/task_10.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": 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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"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": 16,
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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": 17,
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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()"
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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": 18,
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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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"label_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": 19,
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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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" print(np.array(label_rep).shape)\n",
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" print(np.array(S).shape)\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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" 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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" 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_images = []\n",
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"\n",
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" for img in distances:\n",
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" similar_images.append(img)\n",
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" if len(similar_images) == 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_images:\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": 20,
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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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" label_distance = sorted(distances, key=lambda x: x[\"distance\"], reverse=False)[:1]\n",
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"\n",
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" dataset = loadDataset(Caltech101)\n",
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" similar_images = []\n",
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" for i in range(len(dataset)):\n",
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" _, l = dataset[i]\n",
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" if l == label:\n",
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" similar_images.append(i)\n",
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"\n",
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" similar_images = random.sample(similar_images, knum)\n",
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" images_distances = []\n",
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" for i in similar_images:\n",
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" images_distances.append({\"image_id\": i,\"distance\": label_distance[0][\"distance\"]})\n",
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" \n",
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" for x in images_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": 21,
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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': 2309, 'distance': 4.117664288663269}\n",
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"{'image_id': 1930, 'distance': 4.117664288663269}\n",
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"{'image_id': 1940, 'distance': 4.117664288663269}\n",
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"{'image_id': 1929, 'distance': 4.117664288663269}\n",
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"{'image_id': 2250, 'distance': 4.117664288663269}\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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"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": "3.11.6"
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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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