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https://github.com/20kaushik02/CSE515_MWDB_Project.git
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task 5 init
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pranavbrkr
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Phase 2/task5.ipynb
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198
Phase 2/task5.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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"import json\n",
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"import math\n",
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"from pymongo import MongoClient\n",
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"import scipy\n",
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"import numpy as np\n",
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"from sklearn.decomposition import NMF\n",
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"from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
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"from sklearn.cluster import KMeans"
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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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{
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"ename": "ValueError",
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"evalue": "invalid literal for int() with base 10: ''",
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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;31mValueError\u001b[0m Traceback (most recent call last)",
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"\u001b[1;32me:\\Fall 23\\CSE 515 - Multimedia and web databases\\CSE515_MWDB_Project\\Phase 2\\task5.ipynb Cell 2\u001b[0m line \u001b[0;36m9\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%202/task5.ipynb#W1sZmlsZQ%3D%3D?line=88'>89</a>\u001b[0m extractKLatentSemantics(k, label_sim_matrix, feature_model, dim_reduction)\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%202/task5.ipynb#W1sZmlsZQ%3D%3D?line=92'>93</a>\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39m__name__\u001b[39m \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m__main__\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[1;32m---> <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%202/task5.ipynb#W1sZmlsZQ%3D%3D?line=93'>94</a>\u001b[0m main()\n",
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"\u001b[1;32me:\\Fall 23\\CSE 515 - Multimedia and web databases\\CSE515_MWDB_Project\\Phase 2\\task5.ipynb Cell 2\u001b[0m line \u001b[0;36m6\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%202/task5.ipynb#W1sZmlsZQ%3D%3D?line=66'>67</a>\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mmain\u001b[39m():\n\u001b[1;32m---> <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%202/task5.ipynb#W1sZmlsZQ%3D%3D?line=68'>69</a>\u001b[0m k \u001b[39m=\u001b[39m \u001b[39mint\u001b[39;49m(\u001b[39minput\u001b[39;49m(\u001b[39m\"\u001b[39;49m\u001b[39mEnter k: \u001b[39;49m\u001b[39m\"\u001b[39;49m))\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%202/task5.ipynb#W1sZmlsZQ%3D%3D?line=70'>71</a>\u001b[0m features \u001b[39m=\u001b[39m [\u001b[39m'\u001b[39m\u001b[39mcolor_moments\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mhog\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mlayer3\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mavgpool\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mfc\u001b[39m\u001b[39m'\u001b[39m]\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%202/task5.ipynb#W1sZmlsZQ%3D%3D?line=72'>73</a>\u001b[0m \u001b[39m# User input for feature model to extract\u001b[39;00m\n",
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"\u001b[1;31mValueError\u001b[0m: invalid literal for int() with base 10: ''"
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]
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}
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],
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"source": [
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"client = MongoClient()\n",
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"client = MongoClient(host = \"localhost\", port = 27017)\n",
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"\n",
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"# Select the database\n",
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"db = client.Multimedia_Web_DBs\n",
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"\n",
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"# Fetch all documents from the collection and then sort them by \"_id\"\n",
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"feature_descriptors = list(db.Caltech101_Feature_Descriptors.find({}))\n",
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"feature_descriptors = sorted(list(db.Caltech101_Feature_Descriptors.find({})), key=lambda x: x[\"_id\"], reverse=False)"
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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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"def extractKLatentSemantics(k, image_sim_matrix, feature_model, dim_reduction):\n",
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"\n",
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" feature_ids = [x[\"_id\"] for x in feature_descriptors if x[\"_id\"] % 2 == 0]\n",
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" feature_labels = [x[\"label\"] for x in feature_descriptors if x[\"_id\"] % 2 == 0]\n",
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"\n",
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" filename = 'ls3.json'\n",
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"\n",
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" match dim_reduction:\n",
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"\n",
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" case 1:\n",
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" U, S, Vh = scipy.sparse.linalg.svds(np.array(image_sim_matrix), k=k)\n",
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" k_latent_semantics = sorted(list(zip(feature_ids, U.tolist())), key = lambda x: x[1][0], reverse = True)\n",
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"\n",
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" case 2:\n",
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" model = NMF(n_components = k, init = 'random', solver = 'cd', alpha_H = 0.01, alpha_W = 0.01, max_iter = 10000)\n",
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" min_value = np.min(image_sim_matrix)\n",
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" feature_vectors_shifted = image_sim_matrix - min_value\n",
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" U = model.fit_transform(np.array(feature_vectors_shifted))\n",
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" k_latent_semantics = sorted(list(zip(feature_ids, U.tolist())), key = lambda x: x[1][0], reverse = True)\n",
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"\n",
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" case 3:\n",
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" U = LinearDiscriminantAnalysis(n_components = k).fit_transform(image_sim_matrix, feature_labels)\n",
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" k_latent_semantics = sorted(list(zip(feature_ids, U.tolist())), key = lambda x: x[1][0], reverse = True)\n",
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"\n",
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" case 4:\n",
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" kmeans = KMeans(n_clusters = k)\n",
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" kmeans.fit(image_sim_matrix)\n",
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" U = kmeans.transform(image_sim_matrix)\n",
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" k_latent_semantics = sorted(list(zip(feature_ids, U.tolist())), key = lambda x: x[1][0], reverse = True)\n",
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" \n",
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" k_latent_semantics = [{\"_id\": item[0], \"semantics\": item[1]} for item in k_latent_semantics]\n",
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" with open(filename, 'w', encoding='utf-8') as f:\n",
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" json.dump(k_latent_semantics, f, ensure_ascii = False)"
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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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"def findLabelLabelSimMatrix(feature_model):\n",
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"\n",
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" label_sim_matrix = []\n",
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" label_mean_vectors = []\n",
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"\n",
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" for label in range(101):\n",
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" label_vectors = [x[feature_model] for x in feature_descriptors if x[\"label\"] == label and x[\"_id\"] % 2 == 0]\n",
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" mean_vector = [sum(col)/len(col) for col in zip(*label_vectors)]\n",
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" label_mean_vectors.append(mean_vector)\n",
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" \n",
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"\n",
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" n = len(label_mean_vectors)\n",
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"\n",
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" label_sim_matrix = np.zeros((n, n))\n",
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"\n",
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" for i in range(n):\n",
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" for j in range(i + 1, n):\n",
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"\n",
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" match feature_model:\n",
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"\n",
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" case \"color_moments\":\n",
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" label_sim_matrix[i][j] = label_sim_matrix[j][i] = math.dist(label_mean_vectors[i], label_mean_vectors[j])\n",
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" \n",
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" case \"hog\":\n",
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" label_sim_matrix[i][j] = label_sim_matrix[j][i] = (np.dot(label_mean_vectors[i], label_mean_vectors[j]) / (np.linalg.norm(label_mean_vectors[i]) * np.linalg.norm(label_mean_vectors[j])))\n",
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"\n",
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" case \"avgpool\" | \"layer3\" | \"fc\":\n",
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" label_sim_matrix[i][j] = label_sim_matrix[j][i] = scipy.stats.pearsonr(label_mean_vectors[i], label_mean_vectors[j]).statistic\n",
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" \n",
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" return label_sim_matrix"
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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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"def main():\n",
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"\n",
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" k = int(input(\"Enter k: \"))\n",
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"\n",
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" features = ['color_moments', 'hog', 'layer3', 'avgpool', 'fc']\n",
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"\n",
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" # User input for feature model to extract\n",
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" print(\"\\n1: Color moments\")\n",
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" print(\"2: HOG\")\n",
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" print(\"3: Resnet50 Avgpool layer\")\n",
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" print(\"4: Resnet50 Layer 3\")\n",
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" print(\"5: Resnet50 FC layer\")\n",
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" feature_model = features[int(input(\"Select the feature model: \")) - 1]\n",
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"\n",
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" print(\"\\n1. SVD\")\n",
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" print(\"2. NNMF\")\n",
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" print(\"3. LDA\")\n",
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" print(\"4. k-means\")\n",
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" dim_reduction = int(input(\"Select the dimensionality reduction technique: \"))\n",
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"\n",
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" label_sim_matrix = findLabelLabelSimMatrix(feature_model)\n",
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"\n",
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" extractKLatentSemantics(k, label_sim_matrix, feature_model, dim_reduction)"
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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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"if __name__ == \"__main__\":\n",
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" main()"
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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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"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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