From c135fdc390f98013b97c3897521136352ef65c9e Mon Sep 17 00:00:00 2001 From: pranavbrkr Date: Thu, 12 Oct 2023 16:41:06 -0700 Subject: [PATCH 01/10] task9 init --- Phase 2/label_sim-cm_fd-svd-10-semantics.json | 1 + Phase 2/task_5.ipynb | 223 +++++++++--------- Phase 2/task_9.ipynb | 132 +++++++++++ Phase 2/utils.py | 41 +++- 4 files changed, 288 insertions(+), 109 deletions(-) create mode 100644 Phase 2/label_sim-cm_fd-svd-10-semantics.json create mode 100644 Phase 2/task_9.ipynb diff --git a/Phase 2/label_sim-cm_fd-svd-10-semantics.json b/Phase 2/label_sim-cm_fd-svd-10-semantics.json new file mode 100644 index 0000000..3cbef2c --- /dev/null +++ b/Phase 2/label_sim-cm_fd-svd-10-semantics.json @@ -0,0 +1 @@ +{"image-semantic": [[-0.12970349333930165, -0.10134391389040841, 0.1384139791414865, -0.07864531657634273, -0.08204300358429883, 0.1370982739579513, 0.05519373317570632, 0.18579423291522054, 0.15145696367688846, 0.0941041888294147], [-0.15226070794899815, -0.14730381908164875, 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b/Phase 2/task_5.ipynb index 7f025ea..0b1cdb0 100644 --- a/Phase 2/task_5.ipynb +++ b/Phase 2/task_5.ipynb @@ -2,9 +2,18 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], "source": [ "%load_ext autoreload\n", "%autoreload 2" @@ -12,7 +21,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -23,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -32,124 +41,124 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Applying svd on the fc_fd space to get 10 latent semantics (showing only top 10 image-weight pairs for each latent semantic)...\n", + "Applying svd on the given similarity matrix to get 10 latent semantics (showing only top 10 image-weight pairs for each latent semantic)...\n", "Latent semantic no. 0\n", - "Image_ID\t80\t-\tWeight\t0.2614097705550824\n", - "Image_ID\t74\t-\tWeight\t0.255431983850539\n", - "Image_ID\t72\t-\tWeight\t0.24329045773521019\n", - "Image_ID\t76\t-\tWeight\t0.22867416408250565\n", - "Image_ID\t38\t-\tWeight\t0.19933358228759127\n", - "Image_ID\t70\t-\tWeight\t0.18697368408982706\n", - "Image_ID\t78\t-\tWeight\t0.13796715203849405\n", - "Image_ID\t130\t-\tWeight\t0.12802644225327572\n", - "Image_ID\t128\t-\tWeight\t0.12766513481071043\n", - "Image_ID\t116\t-\tWeight\t0.12432195172872901\n", + "Image_ID\t200\t-\tWeight\t0.0\n", + "Image_ID\t198\t-\tWeight\t-0.004684806351746236\n", + "Image_ID\t196\t-\tWeight\t-0.007271577414375871\n", + "Image_ID\t194\t-\tWeight\t-0.011073051177514079\n", + "Image_ID\t192\t-\tWeight\t-0.011680371639188197\n", + "Image_ID\t188\t-\tWeight\t-0.014876024947438421\n", + "Image_ID\t186\t-\tWeight\t-0.017327189984007427\n", + "Image_ID\t190\t-\tWeight\t-0.021143262428570023\n", + "Image_ID\t182\t-\tWeight\t-0.026835375354998945\n", + "Image_ID\t180\t-\tWeight\t-0.030539133156424272\n", "Latent semantic no. 1\n", - "Image_ID\t42\t-\tWeight\t0.24451953308549035\n", - "Image_ID\t104\t-\tWeight\t0.17513827022527176\n", - "Image_ID\t2\t-\tWeight\t0.17502495949250704\n", - "Image_ID\t0\t-\tWeight\t0.17209867451969002\n", - "Image_ID\t170\t-\tWeight\t0.16656363902027468\n", - "Image_ID\t96\t-\tWeight\t0.15318453472976815\n", - "Image_ID\t40\t-\tWeight\t0.1432149719665029\n", - "Image_ID\t44\t-\tWeight\t0.1429496131499582\n", - "Image_ID\t160\t-\tWeight\t0.13479710738132986\n", - "Image_ID\t6\t-\tWeight\t0.1264545662660414\n", + "Image_ID\t130\t-\tWeight\t0.21209688019072415\n", + "Image_ID\t138\t-\tWeight\t0.20392427070510372\n", + "Image_ID\t120\t-\tWeight\t0.1528415927574225\n", + "Image_ID\t132\t-\tWeight\t0.14995762877608315\n", + "Image_ID\t160\t-\tWeight\t0.1488052541453248\n", + "Image_ID\t136\t-\tWeight\t0.14309946283137032\n", + "Image_ID\t164\t-\tWeight\t0.1374261619484733\n", + "Image_ID\t140\t-\tWeight\t0.13528239495542024\n", + "Image_ID\t128\t-\tWeight\t0.12811923299406092\n", + "Image_ID\t152\t-\tWeight\t0.12752116772697258\n", "Latent semantic no. 2\n", - "Image_ID\t86\t-\tWeight\t0.21244971577008848\n", - "Image_ID\t96\t-\tWeight\t0.19744514449239337\n", - "Image_ID\t90\t-\tWeight\t0.19463642108355275\n", - "Image_ID\t32\t-\tWeight\t0.18145091969843855\n", - "Image_ID\t42\t-\tWeight\t0.16316970985189788\n", - "Image_ID\t26\t-\tWeight\t0.15711519451212017\n", - "Image_ID\t184\t-\tWeight\t0.14991640994990046\n", - "Image_ID\t134\t-\tWeight\t0.1462330756631442\n", - "Image_ID\t40\t-\tWeight\t0.14437675159652016\n", - "Image_ID\t182\t-\tWeight\t0.1383518461119224\n", + "Image_ID\t4\t-\tWeight\t0.2518749001016952\n", + "Image_ID\t8\t-\tWeight\t0.24177133880298157\n", + "Image_ID\t58\t-\tWeight\t0.1467873881626323\n", + "Image_ID\t0\t-\tWeight\t0.1384139791414865\n", + "Image_ID\t56\t-\tWeight\t0.11818058158618501\n", + "Image_ID\t20\t-\tWeight\t0.1102967668802325\n", + "Image_ID\t84\t-\tWeight\t0.1044376029159064\n", + "Image_ID\t18\t-\tWeight\t0.10262843674760519\n", + "Image_ID\t138\t-\tWeight\t0.10181762652349924\n", + "Image_ID\t70\t-\tWeight\t0.10127861659022899\n", "Latent semantic no. 3\n", - "Image_ID\t90\t-\tWeight\t0.1720078267722524\n", - "Image_ID\t156\t-\tWeight\t0.16000154385617743\n", - "Image_ID\t158\t-\tWeight\t0.1512646317732056\n", - "Image_ID\t160\t-\tWeight\t0.14646801598350143\n", - "Image_ID\t152\t-\tWeight\t0.1464352560589073\n", - "Image_ID\t150\t-\tWeight\t0.14619374900432364\n", - "Image_ID\t30\t-\tWeight\t0.14143498327111978\n", - "Image_ID\t36\t-\tWeight\t0.14028252934190766\n", - "Image_ID\t92\t-\tWeight\t0.14010606099568526\n", - "Image_ID\t96\t-\tWeight\t0.12878454015856147\n", + "Image_ID\t84\t-\tWeight\t0.16299489544466675\n", + "Image_ID\t94\t-\tWeight\t0.155336350677209\n", + "Image_ID\t70\t-\tWeight\t0.14011002627071287\n", + "Image_ID\t102\t-\tWeight\t0.13701247594788535\n", + "Image_ID\t88\t-\tWeight\t0.1320753872066342\n", + "Image_ID\t82\t-\tWeight\t0.1320716816148611\n", + "Image_ID\t86\t-\tWeight\t0.12902969925360877\n", + "Image_ID\t72\t-\tWeight\t0.12610296358207826\n", + "Image_ID\t92\t-\tWeight\t0.12596461453701044\n", + "Image_ID\t66\t-\tWeight\t0.12532841063277217\n", "Latent semantic no. 4\n", - 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"Image_ID\t38\t-\tWeight\t0.18831453133913492\n", - "Image_ID\t44\t-\tWeight\t0.17741038115946053\n", - "Image_ID\t42\t-\tWeight\t0.16444727858214978\n", - "Image_ID\t130\t-\tWeight\t0.15436113645002744\n", - "Image_ID\t40\t-\tWeight\t0.1536450181907607\n", - "Image_ID\t132\t-\tWeight\t0.14964910372393345\n", - "Image_ID\t46\t-\tWeight\t0.147369630386678\n", - "Image_ID\t36\t-\tWeight\t0.14003912645014002\n", - "Image_ID\t128\t-\tWeight\t0.13864439525825356\n", - "Image_ID\t138\t-\tWeight\t0.13770732538821512\n", + "Image_ID\t184\t-\tWeight\t0.25060450796637307\n", + "Image_ID\t96\t-\tWeight\t0.19653319773940384\n", + "Image_ID\t4\t-\tWeight\t0.1927615510140044\n", + "Image_ID\t190\t-\tWeight\t0.1823467475920773\n", + "Image_ID\t104\t-\tWeight\t0.17232402315708764\n", + "Image_ID\t176\t-\tWeight\t0.15944267571419668\n", + "Image_ID\t2\t-\tWeight\t0.15830010074390483\n", + "Image_ID\t180\t-\tWeight\t0.15710086389623582\n", + "Image_ID\t86\t-\tWeight\t0.1531972222034532\n", + "Image_ID\t178\t-\tWeight\t0.14864580852650564\n", "Latent semantic no. 6\n", - 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"Image_ID\t158\t-\tWeight\t0.15332739573127638\n", - "Image_ID\t152\t-\tWeight\t0.15027095321242787\n", - "Image_ID\t2\t-\tWeight\t0.148228537938103\n", - "Image_ID\t0\t-\tWeight\t0.14693245027728857\n", - "Image_ID\t156\t-\tWeight\t0.1439438847861891\n", - "Image_ID\t8\t-\tWeight\t0.14356918947005834\n", - "Image_ID\t10\t-\tWeight\t0.1431162549061445\n", - "Image_ID\t6\t-\tWeight\t0.14277108702825383\n", - "Image_ID\t150\t-\tWeight\t0.1424099571884803\n", - "Image_ID\t164\t-\tWeight\t0.13731169848767164\n", + "Image_ID\t0\t-\tWeight\t0.18579423291522054\n", + "Image_ID\t160\t-\tWeight\t0.15838043091994455\n", + "Image_ID\t12\t-\tWeight\t0.1569899414230264\n", + "Image_ID\t16\t-\tWeight\t0.15348073631252238\n", + "Image_ID\t20\t-\tWeight\t0.14749435830520785\n", + "Image_ID\t18\t-\tWeight\t0.14710442040625207\n", + "Image_ID\t14\t-\tWeight\t0.14572307182896904\n", + "Image_ID\t2\t-\tWeight\t0.135886756644037\n", + "Image_ID\t158\t-\tWeight\t0.12716375063129493\n", + "Image_ID\t154\t-\tWeight\t0.11653475862758583\n", "Latent semantic no. 8\n", - "Image_ID\t136\t-\tWeight\t0.14826723874051348\n", - "Image_ID\t142\t-\tWeight\t0.1444905135922577\n", - "Image_ID\t116\t-\tWeight\t0.14310970423245634\n", - "Image_ID\t132\t-\tWeight\t0.13967210710664973\n", - "Image_ID\t152\t-\tWeight\t0.13699976834141417\n", - "Image_ID\t114\t-\tWeight\t0.13649814331495427\n", - "Image_ID\t138\t-\tWeight\t0.13624706512987708\n", - "Image_ID\t106\t-\tWeight\t0.13620952950667425\n", - "Image_ID\t110\t-\tWeight\t0.1346054901033104\n", - "Image_ID\t144\t-\tWeight\t0.13436573258693213\n", + "Image_ID\t128\t-\tWeight\t0.20162255290912043\n", + "Image_ID\t64\t-\tWeight\t0.2013551710742827\n", + "Image_ID\t76\t-\tWeight\t0.19200691322367733\n", + "Image_ID\t68\t-\tWeight\t0.183262211696717\n", + "Image_ID\t2\t-\tWeight\t0.17626949463475755\n", + "Image_ID\t126\t-\tWeight\t0.17260073717551033\n", + "Image_ID\t130\t-\tWeight\t0.16679745247386799\n", + "Image_ID\t0\t-\tWeight\t0.15145696367688846\n", + "Image_ID\t80\t-\tWeight\t0.13382645234168947\n", + "Image_ID\t132\t-\tWeight\t0.12607547198838437\n", "Latent semantic no. 9\n", - "Image_ID\t38\t-\tWeight\t0.15911686596038474\n", - "Image_ID\t2\t-\tWeight\t0.15207108925634513\n", - "Image_ID\t0\t-\tWeight\t0.15116756158498235\n", - "Image_ID\t6\t-\tWeight\t0.15009399187071035\n", - "Image_ID\t10\t-\tWeight\t0.14437025978168486\n", - "Image_ID\t4\t-\tWeight\t0.14315858315130434\n", - "Image_ID\t34\t-\tWeight\t0.14296451776950192\n", - "Image_ID\t22\t-\tWeight\t0.14272703151065388\n", - "Image_ID\t24\t-\tWeight\t0.14254462871698045\n", - "Image_ID\t20\t-\tWeight\t0.14096073579756538\n" + "Image_ID\t110\t-\tWeight\t0.2380313932091839\n", + "Image_ID\t126\t-\tWeight\t0.22284705922022288\n", + "Image_ID\t170\t-\tWeight\t0.20294066349000953\n", + "Image_ID\t58\t-\tWeight\t0.19271846291888434\n", + "Image_ID\t166\t-\tWeight\t0.16710379029940944\n", + "Image_ID\t118\t-\tWeight\t0.16159034411481996\n", + "Image_ID\t42\t-\tWeight\t0.1585043891315177\n", + "Image_ID\t120\t-\tWeight\t0.15529190621970054\n", + "Image_ID\t56\t-\tWeight\t0.1484578124120866\n", + "Image_ID\t160\t-\tWeight\t0.13578707023661948\n" ] } ], @@ -206,7 +215,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.5" + "version": "3.11.4" } }, "nbformat": 4, diff --git a/Phase 2/task_9.ipynb b/Phase 2/task_9.ipynb new file mode 100644 index 0000000..d39bac1 --- /dev/null +++ b/Phase 2/task_9.ipynb @@ -0,0 +1,132 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import os\n", + "from utils import *" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "selected_latent_space = valid_latent_spaces[\n", + " str(input(\"Enter latent space - one of \" + str(list(valid_latent_spaces.keys()))))\n", + "]\n", + "\n", + "selected_feature_model = valid_feature_models[\n", + " str(input(\"Enter feature model - one of \" + str(list(valid_feature_models.keys()))))\n", + "]\n", + "\n", + "k = int(input(\"Enter value of k: \"))\n", + "if k < 1:\n", + " raise ValueError(\"k should be a positive integer\")\n", + "\n", + "selected_dim_reduction_method = str(\n", + " input(\n", + " \"Enter dimensionality reduction method - one of \"\n", + " + str(list(valid_dim_reduction_methods.keys()))\n", + " )\n", + ")\n", + "\n", + "label = int(input(\"Enter label: \"))\n", + "if label < 0 and label > 100:\n", + " raise ValueError(\"k should be between 0 and 100\")\n", + "\n", + "\n", + "match selected_latent_space:\n", + " case \"\":\n", + " if os.path.exists(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"):\n", + " data = json.load(open(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"))\n", + " else:\n", + " print(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json does not exist\" )\n", + " case \"cp\":\n", + " if os.path.exists(f\"{selected_feature_model}-cp-{k}-semantics.json\"):\n", + " data = json.load(open(f\"{selected_feature_model}-cp-{k}-semantics.json\"))\n", + " else:\n", + " \n", + " print(f\"{selected_feature_model}-cp-{k}-semantics.json does not exist\" )\n", + " case _:\n", + " if os.path.exists(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"):\n", + " data = json.load(open(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"))\n", + " else:\n", + " print(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json does not exist\" )\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(101, 10)\n", + "(10, 10)\n", + "(10, 101)\n" + ] + } + ], + "source": [ + "match selected_latent_space:\n", + "\n", + " case \"label_sim\":\n", + "\n", + " extract_simila\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": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Phase 2/utils.py b/Phase 2/utils.py index 6afbba7..fa749ff 100644 --- a/Phase 2/utils.py +++ b/Phase 2/utils.py @@ -358,6 +358,12 @@ valid_feature_models = { "fc": "fc_fd", "resnet": "resnet_fd", } +valid_latent_spaces = { + "ls1": "", + "ls2": "cp", + "ls3": "label_sim", + "ls4": "image_sim", +} valid_distance_measures = { "euclidean": euclidean_distance_measure, "cosine": cosine_distance_measure, @@ -517,7 +523,7 @@ def calculate_label_representatives(fd_collection, label, feature_model): """Calculate representative feature vector of a label as the mean of all feature vectors under a feature model""" label_fds = [ - img_fds[feature_model] # get the specific feature model's feature vector + np.array(img_fds[feature_model]).flatten() # get the specific feature model's feature vector for img_fds in fd_collection.find( {"true_label": label} ) # repeat for all images @@ -804,6 +810,37 @@ class KMeans: return Y +def svd(matrix, k): + # Step 1: Compute the covariance matrix + cov_matrix = np.dot(matrix.T, matrix) + + # Step 2: Compute the eigenvalues and eigenvectors of the covariance matrix + eigenvalues, eigenvectors = np.linalg.eig(cov_matrix) + + # Step 3: Sort the eigenvalues and corresponding eigenvectors + sort_indices = eigenvalues.argsort()[::-1] + eigenvalues = eigenvalues[sort_indices] + eigenvectors = eigenvectors[:, sort_indices] + + # Step 4: Compute the singular values and the left and right singular vectors + singular_values = np.sqrt(eigenvalues) + left_singular_vectors = np.dot(matrix, eigenvectors) + right_singular_vectors = eigenvectors + + # Step 5: Normalize the singular vectors + for i in range(left_singular_vectors.shape[1]): + left_singular_vectors[:, i] /= singular_values[i] + + for i in range(right_singular_vectors.shape[1]): + right_singular_vectors[:, i] /= singular_values[i] + + # Keep only the top k singular values and their corresponding vectors + singular_values = singular_values[:k] + left_singular_vectors = left_singular_vectors[:, :k] + right_singular_vectors = right_singular_vectors[:, :k] + + return left_singular_vectors, np.diag(singular_values), right_singular_vectors.T + def extract_latent_semantics( fd_collection, k, @@ -861,7 +898,7 @@ def extract_latent_semantics( # singular value decomposition # sparse version of SVD to get only k singular values case 1: - U, S, V_T = svds(feature_vectors, k=k) + U, S, V_T = svd(feature_vectors, k=k) all_latent_semantics = { "image-semantic": U.tolist(), From 6e012173f0506b348d6ad7c02109b52150e37caf Mon Sep 17 00:00:00 2001 From: pranavbrkr Date: Fri, 13 Oct 2023 10:43:00 -0700 Subject: [PATCH 02/10] ls1 and ls3 svd and nmf --- Phase 2/task_9.ipynb | 186 ++++++++++++++++++++++++++++++++++++++++--- Phase 2/utils.py | 22 ++++- 2 files changed, 194 insertions(+), 14 deletions(-) diff --git a/Phase 2/task_9.ipynb b/Phase 2/task_9.ipynb index d39bac1..dda192e 100644 --- a/Phase 2/task_9.ipynb +++ b/Phase 2/task_9.ipynb @@ -2,9 +2,18 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 62, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], "source": [ "%load_ext autoreload\n", "%autoreload 2" @@ -12,18 +21,31 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 63, "metadata": {}, "outputs": [], "source": [ "import json\n", "import os\n", - "from utils import *" + "import numpy as np\n", + "from utils import *\n", + "import math\n", + "import heapq" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "fd_collection = getCollection(\"team_5_mwdb_phase_2\", \"fd_collection\")\n", + "all_images = fd_collection.find()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 65, "metadata": {}, "outputs": [], "source": [ @@ -50,6 +72,11 @@ "if label < 0 and label > 100:\n", " raise ValueError(\"k should be between 0 and 100\")\n", "\n", + "knum = int(input(\"Enter value of knum: \"))\n", + "if knum < 1:\n", + " raise ValueError(\"knum should be a positive integer\")\n", + "\n", + "label_rep = calculate_label_representatives(fd_collection, label, selected_feature_model)\n", "\n", "match selected_latent_space:\n", " case \"\":\n", @@ -72,34 +99,169 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 66, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "def extract_similarities_ls1(dim_reduction, data, label, label_rep):\n", + "\n", + " match dim_reduction:\n", + "\n", + " case 'svd':\n", + " U = np.array(data[\"image-semantic\"])\n", + " S = np.array(data[\"semantics-core\"])\n", + " V = np.transpose(np.array(data[\"semantic-feature\"]))\n", + "\n", + " comparison_feature_space = np.matmul(U, S)\n", + " comparison_vector = np.matmul(np.matmul(label_rep, V), S)\n", + " \n", + " case \"nmf\":\n", + " H = np.array(data['semantic-feature'])\n", + " comparison_feature_space = np.array(data['image-semantic'])\n", + " comparison_vector = np.matmul(label_rep, np.transpose(H))\n", + "\n", + " print(comparison_feature_space.shape)\n", + " n = len(comparison_feature_space)\n", + " \n", + " distances = []\n", + " for i in range(n):\n", + " if i != label:\n", + " distances.append({\"image_id\": i, \"label\": all_images[i][\"true_label\"],\"distance\": math.dist(comparison_vector, comparison_feature_space[i])})\n", + "\n", + " distances = sorted(distances, key=lambda x: x[\"distance\"], reverse=False)\n", + "\n", + " similar_labels = []\n", + " unique_labels = set()\n", + "\n", + " for img in distances:\n", + " if img['label'] not in unique_labels:\n", + " similar_labels.append(img)\n", + " unique_labels.add(img[\"label\"])\n", + "\n", + " if len(similar_labels) == knum:\n", + " break\n", + "\n", + "\n", + " for x in similar_labels:\n", + " print(x)" + ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "def extract_similarities_ls3(dim_reduction, data, label):\n", + "\n", + " match dim_reduction:\n", + "\n", + " case 'svd':\n", + " U = np.array(data[\"image-semantic\"])\n", + " S = np.array(data[\"semantics-core\"])\n", + " V = np.transpose(np.array(data[\"semantic-feature\"]))\n", + "\n", + " comparison_feature_space = np.matmul(U, S)\n", + " comparison_vector = comparison_feature_space[label]\n", + " \n", + " case \"nmf\":\n", + " comparison_feature_space = np.array(data['image-semantic'])\n", + " comparison_vector = comparison_feature_space[label]\n", + "\n", + " n = len(comparison_feature_space)\n", + " distances = []\n", + " for i in range(n):\n", + " if i != label:\n", + " distances.append({\"label\": i, \"distance\": math.dist(comparison_vector, comparison_feature_space[i])})\n", + "\n", + " distances = sorted(distances, key=lambda x: x[\"distance\"], reverse=False)[:knum]\n", + "\n", + " for x in distances:\n", + " print(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 68, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(101, 10)\n", - "(10, 10)\n", - "(10, 101)\n" + "{'label': 4, 'distance': 0.9931105104385977}\n", + "{'label': 92, 'distance': 1.1209182190288185}\n", + "{'label': 65, 'distance': 1.2107732156271573}\n", + "{'label': 21, 'distance': 1.5053484881391492}\n", + "{'label': 2, 'distance': 1.698430977110922}\n", + "{'label': 100, 'distance': 1.8636096001573115}\n", + "{'label': 95, 'distance': 2.003755992104511}\n", + "{'label': 11, 'distance': 2.069066281581252}\n", + "{'label': 60, 'distance': 2.070894540798742}\n", + "{'label': 88, 'distance': 2.0925931256031}\n", + "{'label': 43, 'distance': 2.1056747598887218}\n", + "{'label': 33, 'distance': 2.165431005806523}\n", + "{'label': 90, 'distance': 2.174626607979455}\n", + "{'label': 83, 'distance': 2.188609736988739}\n", + "{'label': 68, 'distance': 2.209562202827548}\n", + "{'label': 59, 'distance': 2.27130902508622}\n", + "{'label': 35, 'distance': 2.276916489521396}\n", + "{'label': 70, 'distance': 2.283111150497479}\n", + "{'label': 53, 'distance': 2.2871296343421075}\n", + "{'label': 42, 'distance': 2.2943393449254192}\n", + "{'label': 1, 'distance': 2.299515307388396}\n", + "{'label': 89, 'distance': 2.300444335700286}\n", + "{'label': 64, 'distance': 2.3105619552648906}\n", + "{'label': 47, 'distance': 2.3258018764464126}\n", + "{'label': 28, 'distance': 2.33793138436563}\n", + "{'label': 91, 'distance': 2.348432279582375}\n", + "{'label': 66, 'distance': 2.378823252101462}\n", + "{'label': 52, 'distance': 2.3845656934663344}\n", + "{'label': 17, 'distance': 2.3851103284430946}\n", + "{'label': 29, 'distance': 2.392106657184808}\n", + "{'label': 46, 'distance': 2.4059349825734024}\n", + "{'label': 98, 'distance': 2.425981349727766}\n", + "{'label': 12, 'distance': 2.4320238781945878}\n", + "{'label': 5, 'distance': 2.433658250868235}\n", + "{'label': 72, 'distance': 2.4438014606638965}\n", + "{'label': 96, 'distance': 2.446857205149324}\n", + "{'label': 18, 'distance': 2.4473786634019508}\n", + "{'label': 0, 'distance': 2.4482053195868017}\n", + "{'label': 49, 'distance': 2.451590137889849}\n", + "{'label': 14, 'distance': 2.4717097207497414}\n", + "{'label': 85, 'distance': 2.473715190942228}\n", + "{'label': 19, 'distance': 2.4754273396104534}\n", + "{'label': 51, 'distance': 2.4810475345400316}\n", + "{'label': 75, 'distance': 2.4850838216864224}\n", + "{'label': 93, 'distance': 2.4867224184341175}\n", + "{'label': 44, 'distance': 2.498509815319209}\n", + "{'label': 82, 'distance': 2.501339416798757}\n", + "{'label': 54, 'distance': 2.506342353975533}\n", + "{'label': 9, 'distance': 2.5065630929096394}\n", + "{'label': 41, 'distance': 2.51345667730748}\n" ] } ], "source": [ "match selected_latent_space:\n", "\n", + " case \"\":\n", + " \n", + " extract_similarities_ls1(selected_dim_reduction_method, data, label, label_rep)\n", + "\n", " case \"label_sim\":\n", "\n", - " extract_simila\n" + " extract_similarities_ls3(selected_dim_reduction_method, data, label)\n", + " " ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, diff --git a/Phase 2/utils.py b/Phase 2/utils.py index 6dbb8ed..2eb58b7 100644 --- a/Phase 2/utils.py +++ b/Phase 2/utils.py @@ -841,6 +841,25 @@ def svd(matrix, k): return left_singular_vectors, np.diag(singular_values), right_singular_vectors.T +def nmf(matrix, k, num_iterations=100): + d1, d2 = matrix.shape + # Initialize W and H matrices with random non-negative values + W = np.random.rand(d1, k) + H = np.random.rand(k, d2) + + for iteration in range(num_iterations): + # Update H matrix + numerator_h = np.dot(W.T, matrix) + denominator_h = np.dot(np.dot(W.T, W), H) + H *= numerator_h / denominator_h + + # Update W matrix + numerator_w = np.dot(matrix, H.T) + denominator_w = np.dot(W, np.dot(H, H.T)) + W *= numerator_w / denominator_w + + return W, H + def extract_latent_semantics_from_feature_model( fd_collection, k, @@ -1087,8 +1106,7 @@ def extract_latent_semantics_from_sim_matrix( ) model.fit(feature_vectors_shifted) - W = model.transform(feature_vectors_shifted) - H = model.components_ + W, H = nmf(feature_vectors_shifted, k = k) all_latent_semantics = { "image-semantic": W.tolist(), From d8490c7c4b5c72b8351b618f4d50cd471473f4d9 Mon Sep 17 00:00:00 2001 From: pranavbrkr Date: Fri, 13 Oct 2023 11:27:59 -0700 Subject: [PATCH 03/10] start kmeans task 9 --- Phase 2/task_3.ipynb | 205 ++++++++++++++++++----------------- Phase 2/task_5.ipynb | 251 +++++++++++++++++++++++-------------------- Phase 2/task_6.ipynb | 128 +++++++++++++++++++++- Phase 2/task_9.ipynb | 2 + 4 files changed, 364 insertions(+), 222 deletions(-) diff --git a/Phase 2/task_3.ipynb b/Phase 2/task_3.ipynb index b8378ce..f56d892 100644 --- a/Phase 2/task_3.ipynb +++ b/Phase 2/task_3.ipynb @@ -29,120 +29,119 @@ "name": "stdout", "output_type": "stream", "text": [ - "Applying kmeans on the resnet_fd space to get 10 latent semantics (showing only top 10 image-weight pairs for each latent semantic)...\n", + "Applying kmeans on the cm_fd space to get 10 latent semantics (showing only top 10 image-weight pairs for each latent semantic)...\n", "Initialized centroids\n", - "Iteration 56 - Converged\n", "Note: for K-Means we display distances, in ascending order\n", "Latent semantic no. 0\n", - "Image_ID\t440\t-\tDistance\t10.640763416796371\n", - "Image_ID\t700\t-\tDistance\t11.159224514655602\n", - "Image_ID\t654\t-\tDistance\t11.395135539610168\n", - "Image_ID\t486\t-\tDistance\t11.550858382118225\n", - "Image_ID\t462\t-\tDistance\t11.61044182679253\n", - "Image_ID\t652\t-\tDistance\t11.818427599783789\n", - "Image_ID\t676\t-\tDistance\t11.925768133017636\n", - "Image_ID\t584\t-\tDistance\t11.93319861884516\n", - "Image_ID\t692\t-\tDistance\t11.979693069110743\n", - "Image_ID\t6\t-\tDistance\t12.137562566975056\n", + "Image_ID\t2406\t-\tDistance\t2.4329297906521914\n", + "Image_ID\t2624\t-\tDistance\t2.4610601036735282\n", + "Image_ID\t7112\t-\tDistance\t2.5837781069798633\n", + "Image_ID\t5390\t-\tDistance\t2.60890832624663\n", + "Image_ID\t4782\t-\tDistance\t2.6300363909906017\n", + "Image_ID\t4218\t-\tDistance\t2.6526211985836103\n", + "Image_ID\t4210\t-\tDistance\t2.6581936664893533\n", + "Image_ID\t944\t-\tDistance\t2.7472085431102213\n", + "Image_ID\t6600\t-\tDistance\t2.788716977448917\n", + "Image_ID\t2398\t-\tDistance\t2.797045487845613\n", "Latent semantic no. 1\n", - "Image_ID\t3602\t-\tDistance\t13.563162479981145\n", - "Image_ID\t2414\t-\tDistance\t14.192224338224467\n", - "Image_ID\t3560\t-\tDistance\t14.205420291205272\n", - "Image_ID\t3600\t-\tDistance\t14.389262503144405\n", - "Image_ID\t2228\t-\tDistance\t14.4828087393621\n", - "Image_ID\t3636\t-\tDistance\t14.497503774497243\n", - "Image_ID\t3614\t-\tDistance\t14.591251785931954\n", - "Image_ID\t2090\t-\tDistance\t14.620114150279178\n", - "Image_ID\t2328\t-\tDistance\t14.69159730598465\n", - "Image_ID\t2448\t-\tDistance\t14.774950728597261\n", + "Image_ID\t5826\t-\tDistance\t1.7730956906058473\n", + "Image_ID\t3944\t-\tDistance\t1.8750448829509372\n", + "Image_ID\t968\t-\tDistance\t1.9655862567434115\n", + "Image_ID\t1068\t-\tDistance\t1.9677696006956515\n", + "Image_ID\t5664\t-\tDistance\t2.0908245587325114\n", + "Image_ID\t7392\t-\tDistance\t2.1187697478953686\n", + "Image_ID\t3304\t-\tDistance\t2.154265483459674\n", + "Image_ID\t1008\t-\tDistance\t2.2197924178276014\n", + "Image_ID\t908\t-\tDistance\t2.237300492325052\n", + "Image_ID\t2940\t-\tDistance\t2.2377555386247865\n", "Latent semantic no. 2\n", - "Image_ID\t4838\t-\tDistance\t12.261260721990451\n", - "Image_ID\t7302\t-\tDistance\t12.880136852617754\n", - "Image_ID\t7978\t-\tDistance\t13.077993711608961\n", - "Image_ID\t8600\t-\tDistance\t13.305290839761437\n", - "Image_ID\t7292\t-\tDistance\t13.334716062864114\n", - "Image_ID\t7720\t-\tDistance\t13.37155798887382\n", - "Image_ID\t7958\t-\tDistance\t13.430323190148206\n", - "Image_ID\t4600\t-\tDistance\t13.45781162474979\n", - "Image_ID\t4270\t-\tDistance\t13.491427681265899\n", - "Image_ID\t4828\t-\tDistance\t13.539053205319615\n", + "Image_ID\t2406\t-\tDistance\t2.1258319537256445\n", + "Image_ID\t6922\t-\tDistance\t2.2011613151345975\n", + "Image_ID\t2624\t-\tDistance\t2.2289354011778006\n", + "Image_ID\t6484\t-\tDistance\t2.2515469285749545\n", + "Image_ID\t5390\t-\tDistance\t2.451999872498352\n", + "Image_ID\t4222\t-\tDistance\t2.4690306175362067\n", + "Image_ID\t5038\t-\tDistance\t2.4722970669139785\n", + "Image_ID\t3196\t-\tDistance\t2.475614158419068\n", + "Image_ID\t462\t-\tDistance\t2.49778761746267\n", + "Image_ID\t7380\t-\tDistance\t2.5265238831399635\n", "Latent semantic no. 3\n", - "Image_ID\t1758\t-\tDistance\t5.030040634300718\n", - "Image_ID\t1562\t-\tDistance\t5.3329050871004755\n", - "Image_ID\t1586\t-\tDistance\t5.583507266395663\n", - "Image_ID\t1362\t-\tDistance\t6.017196001905923\n", - "Image_ID\t1626\t-\tDistance\t6.045998053427588\n", - "Image_ID\t1208\t-\tDistance\t6.051540458349612\n", - "Image_ID\t1374\t-\tDistance\t6.178242313742901\n", - "Image_ID\t1112\t-\tDistance\t6.249956790411116\n", - "Image_ID\t1710\t-\tDistance\t6.310688634541122\n", - "Image_ID\t1490\t-\tDistance\t6.376123320547912\n", + "Image_ID\t2412\t-\tDistance\t1.9079653649524306\n", + "Image_ID\t2138\t-\tDistance\t1.9508782175940445\n", + "Image_ID\t2290\t-\tDistance\t1.9526171427482104\n", + "Image_ID\t2302\t-\tDistance\t1.9769105940849563\n", + "Image_ID\t2640\t-\tDistance\t2.0476236872823406\n", + "Image_ID\t2634\t-\tDistance\t2.058811198055415\n", + "Image_ID\t2648\t-\tDistance\t2.0779524915237726\n", + "Image_ID\t2628\t-\tDistance\t2.1411367238671497\n", + "Image_ID\t2630\t-\tDistance\t2.156701968346356\n", + "Image_ID\t2502\t-\tDistance\t2.1813059883906454\n", "Latent semantic no. 4\n", - "Image_ID\t8282\t-\tDistance\t10.506907762007522\n", - "Image_ID\t8348\t-\tDistance\t10.647963471647738\n", - "Image_ID\t8380\t-\tDistance\t10.715093501411761\n", - "Image_ID\t8228\t-\tDistance\t10.879515968086416\n", - "Image_ID\t8240\t-\tDistance\t10.896279105885796\n", - "Image_ID\t8340\t-\tDistance\t10.952943877775777\n", - "Image_ID\t8174\t-\tDistance\t11.012538653878869\n", - "Image_ID\t8368\t-\tDistance\t11.01584931675634\n", - "Image_ID\t8176\t-\tDistance\t11.074708303511043\n", - "Image_ID\t8386\t-\tDistance\t11.090905861600216\n", + "Image_ID\t2528\t-\tDistance\t1.985388167407023\n", + "Image_ID\t2570\t-\tDistance\t2.020441033596718\n", + "Image_ID\t7000\t-\tDistance\t2.0389617509774554\n", + "Image_ID\t2544\t-\tDistance\t2.0461546917978493\n", + "Image_ID\t6946\t-\tDistance\t2.087028769480915\n", + "Image_ID\t5070\t-\tDistance\t2.093563899781913\n", + "Image_ID\t3884\t-\tDistance\t2.12383247213783\n", + "Image_ID\t6662\t-\tDistance\t2.133611417276695\n", + "Image_ID\t5584\t-\tDistance\t2.134813594870179\n", + "Image_ID\t7592\t-\tDistance\t2.1350058409043253\n", "Latent semantic no. 5\n", - "Image_ID\t7400\t-\tDistance\t9.07340282234228\n", - "Image_ID\t7332\t-\tDistance\t9.27997555888011\n", - "Image_ID\t6626\t-\tDistance\t9.490015364667478\n", - "Image_ID\t7990\t-\tDistance\t9.619812101313876\n", - "Image_ID\t7392\t-\tDistance\t9.640980435311661\n", - "Image_ID\t7404\t-\tDistance\t9.6738734363643\n", - "Image_ID\t7980\t-\tDistance\t9.710518881249477\n", - "Image_ID\t7410\t-\tDistance\t9.778693486707565\n", - "Image_ID\t7950\t-\tDistance\t9.785247539262517\n", - "Image_ID\t7346\t-\tDistance\t9.806294880503\n", + "Image_ID\t2406\t-\tDistance\t1.7192989054765462\n", + "Image_ID\t7736\t-\tDistance\t1.8415960899814483\n", + "Image_ID\t2624\t-\tDistance\t1.890325981685572\n", + "Image_ID\t4782\t-\tDistance\t1.947887574583758\n", + "Image_ID\t2434\t-\tDistance\t2.012480907684106\n", + "Image_ID\t5658\t-\tDistance\t2.0159295631755936\n", + "Image_ID\t5632\t-\tDistance\t2.0209799503972894\n", + "Image_ID\t5390\t-\tDistance\t2.054049699587572\n", + "Image_ID\t3762\t-\tDistance\t2.0632381421057997\n", + "Image_ID\t6922\t-\tDistance\t2.1324100407425832\n", "Latent semantic no. 6\n", - "Image_ID\t8542\t-\tDistance\t11.232961895055158\n", - "Image_ID\t6014\t-\tDistance\t11.304802835945505\n", - "Image_ID\t8566\t-\tDistance\t11.443919577851908\n", - "Image_ID\t7200\t-\tDistance\t11.484387898391537\n", - "Image_ID\t6626\t-\tDistance\t11.48886846539337\n", - "Image_ID\t6620\t-\tDistance\t11.578369802598303\n", - "Image_ID\t6636\t-\tDistance\t11.662783932711658\n", - "Image_ID\t8056\t-\tDistance\t11.74943673802499\n", - "Image_ID\t7700\t-\tDistance\t11.769992973787971\n", - "Image_ID\t6622\t-\tDistance\t11.780162710805048\n", + "Image_ID\t7244\t-\tDistance\t2.0882730827827514\n", + "Image_ID\t7256\t-\tDistance\t2.2363345183902643\n", + "Image_ID\t6946\t-\tDistance\t2.2626049811136104\n", + "Image_ID\t7232\t-\tDistance\t2.3287228186618827\n", + "Image_ID\t7260\t-\tDistance\t2.432017355562297\n", + "Image_ID\t4942\t-\tDistance\t2.5360228464626915\n", + "Image_ID\t3194\t-\tDistance\t2.652196198820196\n", + "Image_ID\t4946\t-\tDistance\t2.707800015244559\n", + "Image_ID\t6972\t-\tDistance\t2.772167403532193\n", + "Image_ID\t3822\t-\tDistance\t2.7757540939652245\n", "Latent semantic no. 7\n", - "Image_ID\t2646\t-\tDistance\t7.514711553618432\n", - "Image_ID\t2260\t-\tDistance\t7.633993639248322\n", - "Image_ID\t2460\t-\tDistance\t7.685809907469392\n", - "Image_ID\t2660\t-\tDistance\t7.701780256364207\n", - "Image_ID\t2418\t-\tDistance\t7.716363257255012\n", - "Image_ID\t2240\t-\tDistance\t7.74734521250179\n", - "Image_ID\t2430\t-\tDistance\t7.784825198465868\n", - "Image_ID\t2264\t-\tDistance\t7.828411523843045\n", - "Image_ID\t2242\t-\tDistance\t7.878806112518542\n", - "Image_ID\t2196\t-\tDistance\t7.918897962650677\n", + "Image_ID\t1234\t-\tDistance\t2.5103511852585627\n", + "Image_ID\t1406\t-\tDistance\t2.5905943688502\n", + "Image_ID\t1582\t-\tDistance\t2.64691846983913\n", + "Image_ID\t1844\t-\tDistance\t2.741629768608531\n", + "Image_ID\t1638\t-\tDistance\t2.7657226276060536\n", + "Image_ID\t1154\t-\tDistance\t2.8386700997389043\n", + "Image_ID\t1286\t-\tDistance\t2.8446264818255877\n", + "Image_ID\t1848\t-\tDistance\t2.8793700988824398\n", + "Image_ID\t1284\t-\tDistance\t2.879846330398362\n", + "Image_ID\t1592\t-\tDistance\t2.8822966091246407\n", "Latent semantic no. 8\n", - "Image_ID\t562\t-\tDistance\t8.552732623243445\n", - "Image_ID\t796\t-\tDistance\t9.316343355329956\n", - "Image_ID\t612\t-\tDistance\t9.451362646413244\n", - "Image_ID\t476\t-\tDistance\t9.458717454426738\n", - "Image_ID\t798\t-\tDistance\t9.853412912988212\n", - "Image_ID\t460\t-\tDistance\t9.859458462429464\n", - "Image_ID\t190\t-\tDistance\t10.065071186269668\n", - "Image_ID\t462\t-\tDistance\t10.065893471754435\n", - "Image_ID\t456\t-\tDistance\t10.099056881970604\n", - "Image_ID\t828\t-\tDistance\t10.29276769283984\n", + "Image_ID\t7686\t-\tDistance\t2.3114266143360425\n", + "Image_ID\t4286\t-\tDistance\t2.3193670377796534\n", + "Image_ID\t7974\t-\tDistance\t2.410584599384146\n", + "Image_ID\t7668\t-\tDistance\t2.4392449505107026\n", + "Image_ID\t3262\t-\tDistance\t2.4432361382128236\n", + "Image_ID\t7856\t-\tDistance\t2.484388558904672\n", + "Image_ID\t6250\t-\tDistance\t2.5139181727884887\n", + "Image_ID\t6982\t-\tDistance\t2.522220046130116\n", + "Image_ID\t4032\t-\tDistance\t2.5671693188571254\n", + "Image_ID\t8610\t-\tDistance\t2.592334945993663\n", "Latent semantic no. 9\n", - "Image_ID\t3124\t-\tDistance\t12.500361886870435\n", - "Image_ID\t8064\t-\tDistance\t12.967833703429173\n", - "Image_ID\t4270\t-\tDistance\t13.225230811650766\n", - "Image_ID\t7720\t-\tDistance\t13.340802785257075\n", - "Image_ID\t8050\t-\tDistance\t13.601572206798334\n", - "Image_ID\t8074\t-\tDistance\t13.693355761074226\n", - "Image_ID\t8042\t-\tDistance\t13.72102497292387\n", - "Image_ID\t6450\t-\tDistance\t13.750626256669166\n", - "Image_ID\t8018\t-\tDistance\t13.768703250806348\n", - "Image_ID\t6628\t-\tDistance\t13.784107713433421\n" + "Image_ID\t8656\t-\tDistance\t0.0\n", + "Image_ID\t5314\t-\tDistance\t7.545361629760217\n", + "Image_ID\t7854\t-\tDistance\t7.706317148014618\n", + "Image_ID\t712\t-\tDistance\t7.812246024712053\n", + "Image_ID\t8170\t-\tDistance\t7.940921127343809\n", + "Image_ID\t496\t-\tDistance\t7.95303740274659\n", + "Image_ID\t662\t-\tDistance\t7.976573111687378\n", + "Image_ID\t3188\t-\tDistance\t7.9858733547811935\n", + "Image_ID\t3116\t-\tDistance\t8.012971090439164\n", + "Image_ID\t3078\t-\tDistance\t8.023521594743528\n" ] } ], @@ -195,7 +194,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.5" + "version": "3.11.4" } }, "nbformat": 4, diff --git a/Phase 2/task_5.ipynb b/Phase 2/task_5.ipynb index bddcc7b..8a2529c 100644 --- a/Phase 2/task_5.ipynb +++ b/Phase 2/task_5.ipynb @@ -2,18 +2,9 @@ "cells": [ { "cell_type": "code", - "execution_count": 12, + "execution_count": 19, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], + "outputs": [], "source": [ "from utils import *\n", "warnings.filterwarnings('ignore')\n", @@ -22,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -31,124 +22,156 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Applying svd on the given similarity matrix to get 10 latent semantics (showing only top 10 label-weight pairs for each latent semantic)...\n", + "Applying kmeans on the given similarity matrix to get 10 latent semantics (showing only top 10 label-weight pairs for each latent semantic)...\n", + "Initialized centroids\n", + "Iteration 0\n", + "Iteration 1\n", + "Iteration 2\n", + "Iteration 3\n", + "Iteration 4\n", + "Iteration 5\n", + "Iteration 6\n", + "Iteration 7\n", + "Iteration 8\n", + "Iteration 9\n", + "Iteration 10\n", + "Iteration 11\n", + "Iter 11 - Converged\n", "Latent semantic no. 0\n", - "label\t28\t-\tWeight\t0.2583354411312026\n", - "label\t29\t-\tWeight\t0.2301362547676974\n", - "label\t33\t-\tWeight\t0.2129183683279978\n", - "label\t9\t-\tWeight\t0.17625685452423093\n", - "label\t95\t-\tWeight\t0.16277551497836534\n", - "label\t47\t-\tWeight\t0.1424860388015467\n", - "label\t39\t-\tWeight\t0.1349747704005884\n", - "label\t30\t-\tWeight\t0.13251434767496492\n", - "label\t52\t-\tWeight\t0.12669069496270755\n", - "label\t8\t-\tWeight\t0.1257730807471899\n", + "label\t84\t-\tWeight\t16.953715652557495\n", + "label\t34\t-\tWeight\t17.25164883471016\n", + "label\t1\t-\tWeight\t17.379970016799952\n", + "label\t72\t-\tWeight\t17.439397073433092\n", + "label\t32\t-\tWeight\t17.447297173030393\n", + "label\t31\t-\tWeight\t17.448932606262144\n", + "label\t40\t-\tWeight\t17.561159943630802\n", + "label\t79\t-\tWeight\t17.572813876633166\n", + "label\t5\t-\tWeight\t17.721278660592027\n", + "label\t56\t-\tWeight\t17.731177291838822\n", "Latent semantic no. 1\n", - "label\t96\t-\tWeight\t0.2666765976054894\n", - "label\t97\t-\tWeight\t0.19087869496500426\n", - "label\t25\t-\tWeight\t0.17776094778851348\n", - "label\t3\t-\tWeight\t0.1759798805642099\n", - "label\t98\t-\tWeight\t0.16951497899752574\n", - "label\t22\t-\tWeight\t0.1667032655640346\n", - "label\t24\t-\tWeight\t0.16034180060184824\n", - "label\t19\t-\tWeight\t0.15345532912389587\n", - "label\t52\t-\tWeight\t0.13271640119612757\n", - "label\t29\t-\tWeight\t0.12856388746021633\n", + "label\t84\t-\tWeight\t19.27643729221191\n", + "label\t5\t-\tWeight\t19.449814613173483\n", + "label\t32\t-\tWeight\t19.684592406270944\n", + "label\t63\t-\tWeight\t19.911988624963808\n", + "label\t79\t-\tWeight\t19.930151237028223\n", + "label\t38\t-\tWeight\t19.948477661871497\n", + "label\t89\t-\tWeight\t19.965086791647906\n", + "label\t94\t-\tWeight\t19.990956583854018\n", + "label\t72\t-\tWeight\t19.99680017871235\n", + "label\t45\t-\tWeight\t20.058898160614795\n", "Latent semantic no. 2\n", - "label\t46\t-\tWeight\t0.21813474254675366\n", - "label\t79\t-\tWeight\t0.19091788352587957\n", - "label\t55\t-\tWeight\t0.1871080482210247\n", - "label\t56\t-\tWeight\t0.18322792605578184\n", - "label\t78\t-\tWeight\t0.17506936966351683\n", - "label\t98\t-\tWeight\t0.1733164832137484\n", - "label\t22\t-\tWeight\t0.17114312653027375\n", - "label\t38\t-\tWeight\t0.16928636840289424\n", - "label\t45\t-\tWeight\t0.1567042877228484\n", - "label\t4\t-\tWeight\t0.15108693899889344\n", + "label\t0\t-\tWeight\tnan\n", + "label\t1\t-\tWeight\tnan\n", + "label\t2\t-\tWeight\tnan\n", + "label\t3\t-\tWeight\tnan\n", + "label\t4\t-\tWeight\tnan\n", + "label\t5\t-\tWeight\tnan\n", + "label\t6\t-\tWeight\tnan\n", + "label\t7\t-\tWeight\tnan\n", + "label\t8\t-\tWeight\tnan\n", + "label\t9\t-\tWeight\tnan\n", "Latent semantic no. 3\n", - "label\t96\t-\tWeight\t0.2736613529052896\n", - "label\t98\t-\tWeight\t0.218185914155306\n", - "label\t22\t-\tWeight\t0.1963451355822489\n", - "label\t3\t-\tWeight\t0.17627732148468614\n", - "label\t39\t-\tWeight\t0.1728992502839298\n", - "label\t52\t-\tWeight\t0.15597562436756945\n", - "label\t51\t-\tWeight\t0.1291470561734402\n", - "label\t30\t-\tWeight\t0.12453129554714541\n", - "label\t18\t-\tWeight\t0.1236867360720947\n", - "label\t38\t-\tWeight\t0.12184856229773917\n", + "label\t0\t-\tWeight\tnan\n", + "label\t1\t-\tWeight\tnan\n", + "label\t2\t-\tWeight\tnan\n", + "label\t3\t-\tWeight\tnan\n", + "label\t4\t-\tWeight\tnan\n", + "label\t5\t-\tWeight\tnan\n", + "label\t6\t-\tWeight\tnan\n", + "label\t7\t-\tWeight\tnan\n", + "label\t8\t-\tWeight\tnan\n", + "label\t9\t-\tWeight\tnan\n", "Latent semantic no. 4\n", - "label\t6\t-\tWeight\t0.23875690719216863\n", - "label\t67\t-\tWeight\t0.21007869938490106\n", - "label\t63\t-\tWeight\t0.18822840034389135\n", - "label\t14\t-\tWeight\t0.18738002200878218\n", - "label\t87\t-\tWeight\t0.17508576062247283\n", - "label\t23\t-\tWeight\t0.167492867766091\n", - "label\t15\t-\tWeight\t0.15522709562173342\n", - "label\t61\t-\tWeight\t0.13244353806854162\n", - "label\t45\t-\tWeight\t0.12833204093005665\n", - "label\t68\t-\tWeight\t0.12622315521729294\n", + "label\t32\t-\tWeight\t18.607843379925203\n", + "label\t89\t-\tWeight\t18.671771165930238\n", + "label\t84\t-\tWeight\t18.83858895833768\n", + "label\t79\t-\tWeight\t18.84775924713071\n", + "label\t55\t-\tWeight\t18.88614269359777\n", + "label\t34\t-\tWeight\t18.891433443455583\n", + "label\t11\t-\tWeight\t19.034715149675442\n", + "label\t63\t-\tWeight\t19.042445031693624\n", + "label\t5\t-\tWeight\t19.075471660855772\n", + "label\t59\t-\tWeight\t19.096232354525338\n", "Latent semantic no. 5\n", - "label\t30\t-\tWeight\t0.17385975982344382\n", - "label\t25\t-\tWeight\t0.14655711054814133\n", - "label\t39\t-\tWeight\t0.13307896633493813\n", - "label\t68\t-\tWeight\t0.12851498788897622\n", - "label\t24\t-\tWeight\t0.12828250585375986\n", - "label\t0\t-\tWeight\t0.12500243174429157\n", - "label\t1\t-\tWeight\t0.12371257574727512\n", - "label\t77\t-\tWeight\t0.12370279647800499\n", - "label\t89\t-\tWeight\t0.12233344688386875\n", - "label\t83\t-\tWeight\t0.11445596984835589\n", + "label\t88\t-\tWeight\t17.332684081151356\n", + "label\t100\t-\tWeight\t17.414638052725692\n", + "label\t89\t-\tWeight\t17.64193670680817\n", + "label\t46\t-\tWeight\t17.663677856892257\n", + "label\t5\t-\tWeight\t17.750606635854105\n", + "label\t11\t-\tWeight\t17.921812162626082\n", + "label\t17\t-\tWeight\t17.99728875058849\n", + "label\t64\t-\tWeight\t18.20535869665654\n", + "label\t84\t-\tWeight\t18.280826365832894\n", + "label\t59\t-\tWeight\t18.48939095974247\n", "Latent semantic no. 6\n", - "label\t17\t-\tWeight\t0.2335282879255542\n", - "label\t48\t-\tWeight\t0.19418795795666355\n", - "label\t21\t-\tWeight\t0.19013440200231033\n", - "label\t85\t-\tWeight\t0.17503295059460947\n", - "label\t11\t-\tWeight\t0.14933372636956993\n", - "label\t1\t-\tWeight\t0.1384254243377172\n", - "label\t0\t-\tWeight\t0.13078647401074162\n", - "label\t57\t-\tWeight\t0.11374248801163754\n", - "label\t10\t-\tWeight\t0.10468223841103744\n", - "label\t99\t-\tWeight\t0.10191451131216464\n", + "label\t0\t-\tWeight\tnan\n", + "label\t1\t-\tWeight\tnan\n", + "label\t2\t-\tWeight\tnan\n", + "label\t3\t-\tWeight\tnan\n", + "label\t4\t-\tWeight\tnan\n", + "label\t5\t-\tWeight\tnan\n", + "label\t6\t-\tWeight\tnan\n", + "label\t7\t-\tWeight\tnan\n", + "label\t8\t-\tWeight\tnan\n", + "label\t9\t-\tWeight\tnan\n", "Latent semantic no. 7\n", - "label\t82\t-\tWeight\t0.23372455436757703\n", - "label\t95\t-\tWeight\t0.21795238756371887\n", - "label\t60\t-\tWeight\t0.18080422229063045\n", - "label\t16\t-\tWeight\t0.1806105172209771\n", - "label\t27\t-\tWeight\t0.17365150902149876\n", - "label\t59\t-\tWeight\t0.17250044548228938\n", - "label\t26\t-\tWeight\t0.1661853291143862\n", - "label\t13\t-\tWeight\t0.16331211225170805\n", - "label\t34\t-\tWeight\t0.1523080193090529\n", - "label\t67\t-\tWeight\t0.13577900574984025\n", + "label\t59\t-\tWeight\t19.676597202857955\n", + "label\t72\t-\tWeight\t19.687934144875545\n", + "label\t89\t-\tWeight\t19.830805124280474\n", + "label\t90\t-\tWeight\t20.021426354120276\n", + "label\t77\t-\tWeight\t20.05776182294002\n", + "label\t34\t-\tWeight\t20.058245159709028\n", + "label\t70\t-\tWeight\t20.117786048649382\n", + "label\t68\t-\tWeight\t20.139598145778074\n", + "label\t88\t-\tWeight\t20.185751240083068\n", + "label\t38\t-\tWeight\t20.208902223231863\n", "Latent semantic no. 8\n", - "label\t53\t-\tWeight\t0.2259481751468642\n", - "label\t37\t-\tWeight\t0.21583443408756542\n", - "label\t76\t-\tWeight\t0.20483376297311964\n", - "label\t44\t-\tWeight\t0.1690198227623472\n", - "label\t68\t-\tWeight\t0.1650723880318989\n", - "label\t28\t-\tWeight\t0.15689929414378492\n", - "label\t14\t-\tWeight\t0.1564371673909956\n", - "label\t54\t-\tWeight\t0.1553627917623035\n", - "label\t51\t-\tWeight\t0.14380435363337046\n", - "label\t36\t-\tWeight\t0.13510425005259438\n", + "label\t0\t-\tWeight\tnan\n", + "label\t1\t-\tWeight\tnan\n", + "label\t2\t-\tWeight\tnan\n", + "label\t3\t-\tWeight\tnan\n", + "label\t4\t-\tWeight\tnan\n", + "label\t5\t-\tWeight\tnan\n", + "label\t6\t-\tWeight\tnan\n", + "label\t7\t-\tWeight\tnan\n", + "label\t8\t-\tWeight\tnan\n", + "label\t9\t-\tWeight\tnan\n", "Latent semantic no. 9\n", - "label\t19\t-\tWeight\t0.11741024839079275\n", - "label\t40\t-\tWeight\t0.11107319334138463\n", - "label\t53\t-\tWeight\t0.11058750626248925\n", - "label\t51\t-\tWeight\t0.10794606425819818\n", - "label\t96\t-\tWeight\t0.10735468567860716\n", - "label\t55\t-\tWeight\t0.10731282010915796\n", - "label\t50\t-\tWeight\t0.10703093662670059\n", - "label\t1\t-\tWeight\t0.10651036503732043\n", - "label\t79\t-\tWeight\t0.10640855392103846\n", - "label\t47\t-\tWeight\t0.10594110421348357\n" + "label\t0\t-\tWeight\tnan\n", + "label\t1\t-\tWeight\tnan\n", + "label\t2\t-\tWeight\tnan\n", + "label\t3\t-\tWeight\tnan\n", + "label\t4\t-\tWeight\tnan\n", + "label\t5\t-\tWeight\tnan\n", + "label\t6\t-\tWeight\tnan\n", + "label\t7\t-\tWeight\tnan\n", + "label\t8\t-\tWeight\tnan\n", + "label\t9\t-\tWeight\tnan\n" + ] + }, + { + "ename": "TypeError", + "evalue": "Object of type ndarray is not JSON serializable", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32me:\\Fall 23\\CSE 515 - Multimedia and web databases\\CSE515_MWDB_Project\\Phase 2\\task_5.ipynb Cell 3\u001b[0m line \u001b[0;36m1\n\u001b[0;32m 9\u001b[0m selected_dim_reduction_method \u001b[39m=\u001b[39m \u001b[39mstr\u001b[39m(\n\u001b[0;32m 10\u001b[0m \u001b[39minput\u001b[39m(\n\u001b[0;32m 11\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mEnter dimensionality reduction method - one of \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 12\u001b[0m \u001b[39m+\u001b[39m \u001b[39mstr\u001b[39m(\u001b[39mlist\u001b[39m(valid_dim_reduction_methods\u001b[39m.\u001b[39mkeys()))\n\u001b[0;32m 13\u001b[0m )\n\u001b[0;32m 14\u001b[0m )\n\u001b[0;32m 16\u001b[0m label_sim_matrix \u001b[39m=\u001b[39m find_label_label_similarity(fd_collection,selected_feature_model)\n\u001b[1;32m---> 18\u001b[0m extract_latent_semantics_from_sim_matrix(\n\u001b[0;32m 19\u001b[0m label_sim_matrix,\n\u001b[0;32m 20\u001b[0m selected_feature_model,\n\u001b[0;32m 21\u001b[0m \u001b[39m\"\u001b[39;49m\u001b[39mlabel\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[0;32m 22\u001b[0m k,\n\u001b[0;32m 23\u001b[0m selected_dim_reduction_method,\n\u001b[0;32m 24\u001b[0m top_images\u001b[39m=\u001b[39;49m\u001b[39m10\u001b[39;49m,\n\u001b[0;32m 25\u001b[0m )\n", + "File \u001b[1;32me:\\Fall 23\\CSE 515 - Multimedia and web databases\\CSE515_MWDB_Project\\Phase 2\\utils.py:1193\u001b[0m, in \u001b[0;36mextract_latent_semantics_from_sim_matrix\u001b[1;34m(sim_matrix, feature_model, sim_type, k, dim_reduction_method, top_images)\u001b[0m\n\u001b[0;32m 1187\u001b[0m \u001b[39mfor\u001b[39;00m label \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(num_labels):\n\u001b[0;32m 1188\u001b[0m \u001b[39m# get representative vectors for the label\u001b[39;00m\n\u001b[0;32m 1189\u001b[0m label_mean_vectors\u001b[39m.\u001b[39mappend(\n\u001b[0;32m 1190\u001b[0m calculate_label_representatives(fd_collection, label, feature_model)\n\u001b[0;32m 1191\u001b[0m )\n\u001b[1;32m-> 1193\u001b[0m label_sim_matrix \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mzeros((num_labels, num_labels))\n\u001b[0;32m 1195\u001b[0m \u001b[39m# Calculate half and fill the other\u001b[39;00m\n\u001b[0;32m 1196\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(num_labels):\n", + "File \u001b[1;32mc:\\Users\\Pranav\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\json\\__init__.py:179\u001b[0m, in \u001b[0;36mdump\u001b[1;34m(obj, fp, skipkeys, ensure_ascii, check_circular, allow_nan, cls, indent, separators, default, sort_keys, **kw)\u001b[0m\n\u001b[0;32m 173\u001b[0m iterable \u001b[39m=\u001b[39m \u001b[39mcls\u001b[39m(skipkeys\u001b[39m=\u001b[39mskipkeys, ensure_ascii\u001b[39m=\u001b[39mensure_ascii,\n\u001b[0;32m 174\u001b[0m check_circular\u001b[39m=\u001b[39mcheck_circular, allow_nan\u001b[39m=\u001b[39mallow_nan, indent\u001b[39m=\u001b[39mindent,\n\u001b[0;32m 175\u001b[0m separators\u001b[39m=\u001b[39mseparators,\n\u001b[0;32m 176\u001b[0m default\u001b[39m=\u001b[39mdefault, sort_keys\u001b[39m=\u001b[39msort_keys, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkw)\u001b[39m.\u001b[39miterencode(obj)\n\u001b[0;32m 177\u001b[0m \u001b[39m# could accelerate with writelines in some versions of Python, at\u001b[39;00m\n\u001b[0;32m 178\u001b[0m \u001b[39m# a debuggability cost\u001b[39;00m\n\u001b[1;32m--> 179\u001b[0m \u001b[39mfor\u001b[39;49;00m chunk \u001b[39min\u001b[39;49;00m iterable:\n\u001b[0;32m 180\u001b[0m fp\u001b[39m.\u001b[39;49mwrite(chunk)\n", + "File \u001b[1;32mc:\\Users\\Pranav\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\json\\encoder.py:432\u001b[0m, in \u001b[0;36m_make_iterencode.._iterencode\u001b[1;34m(o, _current_indent_level)\u001b[0m\n\u001b[0;32m 430\u001b[0m \u001b[39myield from\u001b[39;00m _iterencode_list(o, _current_indent_level)\n\u001b[0;32m 431\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(o, \u001b[39mdict\u001b[39m):\n\u001b[1;32m--> 432\u001b[0m \u001b[39myield from\u001b[39;00m _iterencode_dict(o, _current_indent_level)\n\u001b[0;32m 433\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 434\u001b[0m \u001b[39mif\u001b[39;00m markers \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n", + "File \u001b[1;32mc:\\Users\\Pranav\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\json\\encoder.py:406\u001b[0m, in \u001b[0;36m_make_iterencode.._iterencode_dict\u001b[1;34m(dct, _current_indent_level)\u001b[0m\n\u001b[0;32m 404\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 405\u001b[0m chunks \u001b[39m=\u001b[39m _iterencode(value, _current_indent_level)\n\u001b[1;32m--> 406\u001b[0m \u001b[39myield from\u001b[39;00m chunks\n\u001b[0;32m 407\u001b[0m \u001b[39mif\u001b[39;00m newline_indent \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 408\u001b[0m _current_indent_level \u001b[39m-\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n", + "File \u001b[1;32mc:\\Users\\Pranav\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\json\\encoder.py:326\u001b[0m, in \u001b[0;36m_make_iterencode.._iterencode_list\u001b[1;34m(lst, _current_indent_level)\u001b[0m\n\u001b[0;32m 324\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 325\u001b[0m chunks \u001b[39m=\u001b[39m _iterencode(value, _current_indent_level)\n\u001b[1;32m--> 326\u001b[0m \u001b[39myield from\u001b[39;00m chunks\n\u001b[0;32m 327\u001b[0m \u001b[39mif\u001b[39;00m newline_indent \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 328\u001b[0m _current_indent_level \u001b[39m-\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n", + "File \u001b[1;32mc:\\Users\\Pranav\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\json\\encoder.py:439\u001b[0m, in \u001b[0;36m_make_iterencode.._iterencode\u001b[1;34m(o, _current_indent_level)\u001b[0m\n\u001b[0;32m 437\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mCircular reference detected\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m 438\u001b[0m markers[markerid] \u001b[39m=\u001b[39m o\n\u001b[1;32m--> 439\u001b[0m o \u001b[39m=\u001b[39m _default(o)\n\u001b[0;32m 440\u001b[0m \u001b[39myield from\u001b[39;00m _iterencode(o, _current_indent_level)\n\u001b[0;32m 441\u001b[0m \u001b[39mif\u001b[39;00m markers \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n", + "File \u001b[1;32mc:\\Users\\Pranav\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\json\\encoder.py:180\u001b[0m, in \u001b[0;36mJSONEncoder.default\u001b[1;34m(self, o)\u001b[0m\n\u001b[0;32m 161\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdefault\u001b[39m(\u001b[39mself\u001b[39m, o):\n\u001b[0;32m 162\u001b[0m \u001b[39m \u001b[39m\u001b[39m\"\"\"Implement this method in a subclass such that it returns\u001b[39;00m\n\u001b[0;32m 163\u001b[0m \u001b[39m a serializable object for ``o``, or calls the base implementation\u001b[39;00m\n\u001b[0;32m 164\u001b[0m \u001b[39m (to raise a ``TypeError``).\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 178\u001b[0m \n\u001b[0;32m 179\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 180\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mTypeError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mObject of type \u001b[39m\u001b[39m{\u001b[39;00mo\u001b[39m.\u001b[39m\u001b[39m__class__\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m \u001b[39m\u001b[39m'\u001b[39m\n\u001b[0;32m 181\u001b[0m \u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mis not JSON serializable\u001b[39m\u001b[39m'\u001b[39m)\n", + "\u001b[1;31mTypeError\u001b[0m: Object of type ndarray is not JSON serializable" ] } ], diff --git a/Phase 2/task_6.ipynb b/Phase 2/task_6.ipynb index 15a3afb..46c4787 100644 --- a/Phase 2/task_6.ipynb +++ b/Phase 2/task_6.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -22,9 +22,127 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Applying svd on the given similarity matrix to get 10 latent semantics (showing only top 10 image-weight pairs for each latent semantic)...\n", + "Latent semantic no. 0\n", + "image\t4327\t-\tWeight\t0.06387300915798859\n", + "image\t1653\t-\tWeight\t0.06225930582406118\n", + "image\t4309\t-\tWeight\t0.06095954690299202\n", + "image\t4329\t-\tWeight\t0.05889167793151601\n", + "image\t4318\t-\tWeight\t0.057637806985593974\n", + "image\t2325\t-\tWeight\t0.05612114732712442\n", + "image\t106\t-\tWeight\t0.0553241190050714\n", + "image\t4315\t-\tWeight\t0.05421665176601695\n", + "image\t3627\t-\tWeight\t0.05399516331236024\n", + "image\t4310\t-\tWeight\t0.053878520320048216\n", + "Latent semantic no. 1\n", + "image\t926\t-\tWeight\t0.05969523866379485\n", + "image\t900\t-\tWeight\t0.05738047297133547\n", + "image\t826\t-\tWeight\t0.05737642454934881\n", + "image\t868\t-\tWeight\t0.05734058503897999\n", + "image\t895\t-\tWeight\t0.05679076771674271\n", + "image\t904\t-\tWeight\t0.05665906456098433\n", + "image\t894\t-\tWeight\t0.05659729687888854\n", + "image\t892\t-\tWeight\t0.05637734281187336\n", + "image\t896\t-\tWeight\t0.05626000549868052\n", + "image\t901\t-\tWeight\t0.05621099897239924\n", + "Latent semantic no. 2\n", + "image\t3685\t-\tWeight\t0.03814215214744671\n", + "image\t3049\t-\tWeight\t0.036692586102556525\n", + "image\t4152\t-\tWeight\t0.03586290113404638\n", + "image\t4219\t-\tWeight\t0.03489203423252102\n", + "image\t3208\t-\tWeight\t0.034624460148066645\n", + "image\t4015\t-\tWeight\t0.034128851806262454\n", + "image\t3838\t-\tWeight\t0.03287941968649245\n", + "image\t3544\t-\tWeight\t0.03241416766297603\n", + "image\t4313\t-\tWeight\t0.03231321369753675\n", + "image\t3999\t-\tWeight\t0.0320836528785244\n", + "Latent semantic no. 3\n", + "image\t3892\t-\tWeight\t0.06648490667589399\n", + "image\t3827\t-\tWeight\t0.06563860391481106\n", + "image\t4285\t-\tWeight\t0.06444388987867274\n", + "image\t2076\t-\tWeight\t0.0632874253246352\n", + "image\t3745\t-\tWeight\t0.059842032954945085\n", + "image\t2563\t-\tWeight\t0.058523031976053054\n", + "image\t3884\t-\tWeight\t0.05599712556112116\n", + "image\t3890\t-\tWeight\t0.05593428984607223\n", + "image\t1402\t-\tWeight\t0.055899080338839564\n", + "image\t2665\t-\tWeight\t0.05546907037179164\n", + "Latent semantic no. 4\n", + "image\t3915\t-\tWeight\t0.046851797776368324\n", + "image\t474\t-\tWeight\t0.04434329032960532\n", + "image\t1536\t-\tWeight\t0.04432346480047559\n", + "image\t475\t-\tWeight\t0.04072813117892643\n", + "image\t4255\t-\tWeight\t0.040667016325110085\n", + "image\t3406\t-\tWeight\t0.04040216466729356\n", + "image\t525\t-\tWeight\t0.04029068846509459\n", + "image\t470\t-\tWeight\t0.03997152944980991\n", + "image\t1384\t-\tWeight\t0.03934508125909288\n", + "image\t3357\t-\tWeight\t0.038333999492323516\n", + "Latent semantic no. 5\n", + "image\t3956\t-\tWeight\t0.060011760982094924\n", + "image\t2767\t-\tWeight\t0.056207067311306175\n", + "image\t2775\t-\tWeight\t0.05358938347103485\n", + "image\t3902\t-\tWeight\t0.04905728202908321\n", + "image\t3099\t-\tWeight\t0.048312802387355414\n", + "image\t2794\t-\tWeight\t0.04816327335760051\n", + "image\t3638\t-\tWeight\t0.047326856090526045\n", + "image\t2951\t-\tWeight\t0.04694598325565932\n", + "image\t2493\t-\tWeight\t0.04693933314957065\n", + "image\t1519\t-\tWeight\t0.04692793968385709\n", + "Latent semantic no. 6\n", + "image\t899\t-\tWeight\t0.0557700158434989\n", + "image\t901\t-\tWeight\t0.05385589776037553\n", + "image\t903\t-\tWeight\t0.05326609068648323\n", + "image\t892\t-\tWeight\t0.05278963837571468\n", + "image\t893\t-\tWeight\t0.05252069078249831\n", + "image\t895\t-\tWeight\t0.052452644917562574\n", + "image\t894\t-\tWeight\t0.050699354240581404\n", + "image\t896\t-\tWeight\t0.049699837882682285\n", + "image\t898\t-\tWeight\t0.04862913600225998\n", + "image\t821\t-\tWeight\t0.048576878816213136\n", + "Latent semantic no. 7\n", + "image\t1171\t-\tWeight\t0.03313529748848967\n", + "image\t1350\t-\tWeight\t0.0331120998877209\n", + "image\t1069\t-\tWeight\t0.03307893455959305\n", + "image\t1145\t-\tWeight\t0.032922013181510126\n", + "image\t1324\t-\tWeight\t0.03250158367280327\n", + "image\t1320\t-\tWeight\t0.03214994190354176\n", + "image\t1251\t-\tWeight\t0.0320119250948106\n", + "image\t1353\t-\tWeight\t0.031686689071375326\n", + "image\t1341\t-\tWeight\t0.031629828629460004\n", + "image\t1314\t-\tWeight\t0.03156526413086752\n", + "Latent semantic no. 8\n", + "image\t4291\t-\tWeight\t0.030379982764735483\n", + "image\t3659\t-\tWeight\t0.030303639415262738\n", + "image\t4062\t-\tWeight\t0.03009699185922372\n", + "image\t3645\t-\tWeight\t0.030070839535056917\n", + "image\t4186\t-\tWeight\t0.029988779115247072\n", + "image\t3651\t-\tWeight\t0.02991186566754093\n", + "image\t4306\t-\tWeight\t0.029842728762290226\n", + "image\t4290\t-\tWeight\t0.029821657616221558\n", + "image\t4295\t-\tWeight\t0.029776560377156956\n", + "image\t4063\t-\tWeight\t0.029718537108632898\n", + "Latent semantic no. 9\n", + "image\t3461\t-\tWeight\t-0.01124924934362133\n", + "image\t3690\t-\tWeight\t-0.011359970957367781\n", + "image\t3677\t-\tWeight\t-0.011388302213245766\n", + "image\t2274\t-\tWeight\t-0.011401573573016426\n", + "image\t2695\t-\tWeight\t-0.011431074774005161\n", + "image\t3868\t-\tWeight\t-0.011463898013717732\n", + "image\t1137\t-\tWeight\t-0.011497915659232156\n", + "image\t1647\t-\tWeight\t-0.01152169864389044\n", + "image\t1203\t-\tWeight\t-0.011532017314265241\n", + "image\t2391\t-\tWeight\t-0.011548629237775063\n" + ] + } + ], "source": [ "selected_feature_model = valid_feature_models[\n", " str(input(\"Enter feature model - one of \" + str(list(valid_feature_models.keys()))))\n", @@ -70,7 +188,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.5" + "version": "3.11.4" } }, "nbformat": 4, diff --git a/Phase 2/task_9.ipynb b/Phase 2/task_9.ipynb index dda192e..a034da5 100644 --- a/Phase 2/task_9.ipynb +++ b/Phase 2/task_9.ipynb @@ -120,6 +120,8 @@ " comparison_feature_space = np.array(data['image-semantic'])\n", " comparison_vector = np.matmul(label_rep, np.transpose(H))\n", "\n", + " case \"\"\n", + "\n", " print(comparison_feature_space.shape)\n", " n = len(comparison_feature_space)\n", " \n", From 53e284ae951a554485b1106dc842bcfa8add33de Mon Sep 17 00:00:00 2001 From: pranavbrkr Date: Fri, 13 Oct 2023 11:28:19 -0700 Subject: [PATCH 04/10] utils --- Phase 2/utils.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/Phase 2/utils.py b/Phase 2/utils.py index 4f03f2a..e607ac2 100644 --- a/Phase 2/utils.py +++ b/Phase 2/utils.py @@ -998,6 +998,7 @@ def extract_latent_semantics_from_feature_model( ) as output_file: json.dump(all_latent_semantics, output_file, ensure_ascii=False) + def extract_latent_semantics_from_sim_matrix( sim_matrix, feature_model, @@ -1080,7 +1081,8 @@ def extract_latent_semantics_from_sim_matrix( ) model.fit(feature_vectors_shifted) - W, H = nmf(feature_vectors_shifted, k = k) + W = model.transform(feature_vectors_shifted) + H = model.components_ all_latent_semantics = { "image-semantic": W.tolist(), @@ -1171,6 +1173,7 @@ def extract_latent_semantics_from_sim_matrix( ) as output_file: json.dump(all_latent_semantics, output_file, ensure_ascii=False) + def find_label_label_similarity(fd_collection, feature_model): """ Calculate similarity between labels. Lower values indicate higher similarities @@ -1226,4 +1229,4 @@ def find_image_image_similarity(fd_collection, feature_model): image_sim_matrix[i][j] = image_sim_matrix[j][i] = feature_distance_matches[ feature_model ](np.array(feature_vectors[i]), np.array(feature_vectors[j])) - return image_sim_matrix + return image_sim_matrix \ No newline at end of file From a5b877b6a304a7430630c8440584d78b5d5c2ac5 Mon Sep 17 00:00:00 2001 From: pranavbrkr Date: Fri, 13 Oct 2023 16:29:22 -0700 Subject: [PATCH 05/10] utils --- Phase 2/utils.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Phase 2/utils.py b/Phase 2/utils.py index 8353072..a3a892f 100644 --- a/Phase 2/utils.py +++ b/Phase 2/utils.py @@ -878,7 +878,7 @@ def extract_latent_semantics_from_feature_model( # singular value decomposition # sparse version of SVD to get only k singular values case 1: - U, S, V_T = svd(feature_vectors, k=k) + U, S, V_T = svds(feature_vectors, k=k) all_latent_semantics = { "image-semantic": U.tolist(), @@ -1246,4 +1246,4 @@ def compute_cp_decomposition(fd_collection, feature_model, rank): data_tensor[id, :, label] = all_images[id][feature_model] weights_tensor, factor_matrices = tl.decomposition.parafac(data_tensor, rank=rank, normalize_factors=True) - return weights_tensor, factor_matrices + return weights_tensor, factor_matrices \ No newline at end of file From 3ca1614746080cb2b038383daeac3eb4302fec21 Mon Sep 17 00:00:00 2001 From: pranavbrkr Date: Fri, 13 Oct 2023 17:59:45 -0700 Subject: [PATCH 06/10] ls4 cases for svd and nmf --- Phase 2/task_9.ipynb | 152 ++++++++++++++++++++++--------------------- 1 file changed, 79 insertions(+), 73 deletions(-) diff --git a/Phase 2/task_9.ipynb b/Phase 2/task_9.ipynb index a034da5..3985b51 100644 --- a/Phase 2/task_9.ipynb +++ b/Phase 2/task_9.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 62, + "execution_count": 64, "metadata": {}, "outputs": [ { @@ -21,7 +21,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 65, "metadata": {}, "outputs": [], "source": [ @@ -35,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 66, "metadata": {}, "outputs": [], "source": [ @@ -45,9 +45,17 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 67, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "image_sim-cm_fd-nmf-10-semantics.json loaded\n" + ] + } + ], "source": [ "selected_latent_space = valid_latent_spaces[\n", " str(input(\"Enter latent space - one of \" + str(list(valid_latent_spaces.keys()))))\n", @@ -82,53 +90,87 @@ " case \"\":\n", " if os.path.exists(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"):\n", " data = json.load(open(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"))\n", + " print(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json loaded\")\n", " else:\n", - " print(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json does not exist\" )\n", + " print(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json does not exist\")\n", " case \"cp\":\n", " if os.path.exists(f\"{selected_feature_model}-cp-{k}-semantics.json\"):\n", " data = json.load(open(f\"{selected_feature_model}-cp-{k}-semantics.json\"))\n", - " else:\n", - " \n", - " print(f\"{selected_feature_model}-cp-{k}-semantics.json does not exist\" )\n", + " print(f\"{selected_feature_model}-cp-{k}-semantics.json loaded\")\n", + " else: \n", + " print(f\"{selected_feature_model}-cp-{k}-semantics.json does not exist\")\n", " case _:\n", " if os.path.exists(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"):\n", " data = json.load(open(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"))\n", + " print(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json loaded\")\n", " else:\n", - " print(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json does not exist\" )\n" + " print(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json does not exist\")\n" ] }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 83, "metadata": {}, "outputs": [], "source": [ - "def extract_similarities_ls1(dim_reduction, data, label, label_rep):\n", + "def extract_similarities_ls1_ls4(latent_space, dim_reduction, data, label, label_rep):\n", "\n", " match dim_reduction:\n", "\n", " case 'svd':\n", " U = np.array(data[\"image-semantic\"])\n", " S = np.array(data[\"semantics-core\"])\n", + " if len(S.shape) == 1:\n", + " S = np.diag(S)\n", " V = np.transpose(np.array(data[\"semantic-feature\"]))\n", "\n", + " if latent_space == \"image_sim\":\n", + " label_vectors = []\n", + " length = len(U)\n", + " for i in range(length):\n", + " if all_images[i][\"true_label\"] == label:\n", + " label_vectors.append(U[i])\n", + " label_rep = [sum(col) / len(col) for col in zip(*label_vectors)]\n", + " \n", " comparison_feature_space = np.matmul(U, S)\n", - " comparison_vector = np.matmul(np.matmul(label_rep, V), S)\n", + "\n", + " if latent_space == \"image_sim\":\n", + " print(np.array(label_rep).shape)\n", + " print(np.array(S).shape)\n", + " comparison_vector = np.matmul(label_rep, S)\n", + " else:\n", + " comparison_vector = np.matmul(np.matmul(label_rep, V), S)\n", " \n", " case \"nmf\":\n", " H = np.array(data['semantic-feature'])\n", - " comparison_feature_space = np.array(data['image-semantic'])\n", - " comparison_vector = np.matmul(label_rep, np.transpose(H))\n", + " comparison_feature_space = W = np.array(data['image-semantic'])\n", + " if latent_space == \"image_sim\":\n", + " label_vectors = []\n", + " length = len(W)\n", + " for i in range(length):\n", + " if all_images[i][\"true_label\"] == label:\n", + " label_vectors.append(W[i])\n", + " label_rep = [sum(col) / len(col) for col in zip(*label_vectors)]\n", "\n", - " case \"\"\n", + " if latent_space == \"image_sim\":\n", + " comparison_vector = label_rep\n", + " else:\n", + " comparison_vector = np.matmul(label_rep, np.transpose(H))\n", + "\n", + " case \"kmeans\":\n", + " comparison_vector = []\n", + " comparison_feature_space = np.array(data[\"image-semantic\"])\n", + " S = np.array(data[\"semantic-feature\"])\n", + "\n", + " for centroid in S:\n", + " comparison_vector.append(math.dist(label_rep, centroid))\n", "\n", - " print(comparison_feature_space.shape)\n", " n = len(comparison_feature_space)\n", - " \n", + "\n", " distances = []\n", " for i in range(n):\n", " if i != label:\n", - " distances.append({\"image_id\": i, \"label\": all_images[i][\"true_label\"],\"distance\": math.dist(comparison_vector, comparison_feature_space[i])})\n", + " distances.append({\"image_id\": i, \"label\": all_images[i][\"true_label\"], \"distance\": math.dist(comparison_vector, comparison_feature_space[i])})\n", "\n", " distances = sorted(distances, key=lambda x: x[\"distance\"], reverse=False)\n", "\n", @@ -150,7 +192,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 84, "metadata": {}, "outputs": [], "source": [ @@ -170,6 +212,10 @@ " comparison_feature_space = np.array(data['image-semantic'])\n", " comparison_vector = comparison_feature_space[label]\n", "\n", + " case \"kmeans\":\n", + " comparison_feature_space = np.array(data[\"image-semantic\"])\n", + " comparison_vector = comparison_feature_space[label]\n", + "\n", " n = len(comparison_feature_space)\n", " distances = []\n", " for i in range(n):\n", @@ -184,72 +230,32 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 85, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'label': 4, 'distance': 0.9931105104385977}\n", - "{'label': 92, 'distance': 1.1209182190288185}\n", - "{'label': 65, 'distance': 1.2107732156271573}\n", - "{'label': 21, 'distance': 1.5053484881391492}\n", - "{'label': 2, 'distance': 1.698430977110922}\n", - "{'label': 100, 'distance': 1.8636096001573115}\n", - "{'label': 95, 'distance': 2.003755992104511}\n", - "{'label': 11, 'distance': 2.069066281581252}\n", - "{'label': 60, 'distance': 2.070894540798742}\n", - "{'label': 88, 'distance': 2.0925931256031}\n", - "{'label': 43, 'distance': 2.1056747598887218}\n", - "{'label': 33, 'distance': 2.165431005806523}\n", - "{'label': 90, 'distance': 2.174626607979455}\n", - "{'label': 83, 'distance': 2.188609736988739}\n", - "{'label': 68, 'distance': 2.209562202827548}\n", - "{'label': 59, 'distance': 2.27130902508622}\n", - "{'label': 35, 'distance': 2.276916489521396}\n", - "{'label': 70, 'distance': 2.283111150497479}\n", - "{'label': 53, 'distance': 2.2871296343421075}\n", - "{'label': 42, 'distance': 2.2943393449254192}\n", - "{'label': 1, 'distance': 2.299515307388396}\n", - "{'label': 89, 'distance': 2.300444335700286}\n", - "{'label': 64, 'distance': 2.3105619552648906}\n", - "{'label': 47, 'distance': 2.3258018764464126}\n", - "{'label': 28, 'distance': 2.33793138436563}\n", - "{'label': 91, 'distance': 2.348432279582375}\n", - "{'label': 66, 'distance': 2.378823252101462}\n", - "{'label': 52, 'distance': 2.3845656934663344}\n", - "{'label': 17, 'distance': 2.3851103284430946}\n", - "{'label': 29, 'distance': 2.392106657184808}\n", - "{'label': 46, 'distance': 2.4059349825734024}\n", - "{'label': 98, 'distance': 2.425981349727766}\n", - "{'label': 12, 'distance': 2.4320238781945878}\n", - "{'label': 5, 'distance': 2.433658250868235}\n", - "{'label': 72, 'distance': 2.4438014606638965}\n", - "{'label': 96, 'distance': 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'distance': 0.6885146297494956}\n", + "{'image_id': 3574, 'label': 77, 'distance': 0.6970323320729979}\n", + "{'image_id': 2202, 'label': 31, 'distance': 0.6975621319006345}\n", + "{'image_id': 2917, 'label': 54, 'distance': 0.7112049573397025}\n", + "{'image_id': 4325, 'label': 100, 'distance': 0.7394787087142192}\n", + "{'image_id': 1543, 'label': 12, 'distance': 0.7404143327603417}\n", + "{'image_id': 2333, 'label': 35, 'distance': 0.7432769566450207}\n" ] } ], "source": [ "match selected_latent_space:\n", "\n", - " case \"\":\n", + " case \"\" | \"image_sim\":\n", " \n", - " extract_similarities_ls1(selected_dim_reduction_method, data, label, label_rep)\n", + " extract_similarities_ls1_ls4(selected_latent_space, selected_dim_reduction_method, data, label, label_rep)\n", "\n", " case \"label_sim\":\n", "\n", From 2305b2ea9bd3774fcf59c18d7836194b96f101df Mon Sep 17 00:00:00 2001 From: pranavbrkr Date: Fri, 13 Oct 2023 18:49:13 -0700 Subject: [PATCH 07/10] ls4 added --- Phase 2/label_sim-cm_fd-svd-10-semantics.json | 1 - Phase 2/task6.ipynb | 39 +++- Phase 2/task_5.ipynb | 128 ++++++++++- Phase 2/task_6.ipynb | 205 +++++++++--------- Phase 2/task_9.ipynb | 50 +++-- 5 files changed, 289 insertions(+), 134 deletions(-) delete mode 100644 Phase 2/label_sim-cm_fd-svd-10-semantics.json diff --git a/Phase 2/label_sim-cm_fd-svd-10-semantics.json b/Phase 2/label_sim-cm_fd-svd-10-semantics.json deleted file mode 100644 index 3cbef2c..0000000 --- a/Phase 2/label_sim-cm_fd-svd-10-semantics.json +++ /dev/null @@ -1 +0,0 @@ -{"image-semantic": [[-0.12970349333930165, -0.10134391389040841, 0.1384139791414865, -0.07864531657634273, -0.08204300358429883, 0.1370982739579513, 0.05519373317570632, 0.18579423291522054, 0.15145696367688846, 0.0941041888294147], [-0.15226070794899815, -0.14730381908164875, 0.09603525442217248, -0.13142163103675486, -0.12412208844956082, 0.15830010074390483, 0.08762804925031414, 0.135886756644037, 0.17626949463475755, 0.10346258548084963], 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"execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -35,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -76,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -107,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -141,9 +141,34 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "1: Color moments\n", + "2: HOG\n", + "3: Resnet50 Avgpool layer\n", + "4: Resnet50 Layer 3\n", + "5: Resnet50 FC layer\n" + ] + }, + { + "ename": "ValueError", + "evalue": "invalid literal for int() with base 10: 'cm'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32me:\\Fall 23\\CSE 515 - Multimedia and web databases\\CSE515_MWDB_Project\\Phase 2\\task6.ipynb Cell 6\u001b[0m line \u001b[0;36m2\n\u001b[0;32m 1\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----> 2\u001b[0m main()\n", + "\u001b[1;32me:\\Fall 23\\CSE 515 - Multimedia and web databases\\CSE515_MWDB_Project\\Phase 2\\task6.ipynb Cell 6\u001b[0m line \u001b[0;36m1\n\u001b[0;32m 11\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39m4: Resnet50 Layer 3\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m 12\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39m5: Resnet50 FC layer\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m---> 13\u001b[0m feature_model \u001b[39m=\u001b[39m features[\u001b[39mint\u001b[39;49m(\u001b[39minput\u001b[39;49m(\u001b[39m\"\u001b[39;49m\u001b[39mSelect the feature model: \u001b[39;49m\u001b[39m\"\u001b[39;49m)) \u001b[39m-\u001b[39m \u001b[39m1\u001b[39m]\n\u001b[0;32m 15\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m1. SVD\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m 16\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39m2. NNMF\u001b[39m\u001b[39m\"\u001b[39m)\n", + "\u001b[1;31mValueError\u001b[0m: invalid literal for int() with base 10: 'cm'" + ] + } + ], "source": [ "if __name__ == \"__main__\":\n", " main()" diff --git a/Phase 2/task_5.ipynb b/Phase 2/task_5.ipynb index c39037e..460d6ba 100644 --- a/Phase 2/task_5.ipynb +++ b/Phase 2/task_5.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -22,9 +22,129 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Applying kmeans on the given similarity matrix to get 10 latent semantics (showing only top 10 label-weight pairs for each latent semantic)...\n", + "Initialized centroids\n", + "Note: for K-Means we display distances, in ascending order\n", + "Latent semantic no. 0\n", + "label\t38\t-\tDistance\t2.0070855260713345\n", + "label\t71\t-\tDistance\t2.224974820168396\n", + "label\t94\t-\tDistance\t2.341857909278956\n", + "label\t45\t-\tDistance\t2.99059339026617\n", + "label\t34\t-\tDistance\t3.2010802901998034\n", + "label\t57\t-\tDistance\t3.248469772417219\n", + "label\t77\t-\tDistance\t3.5731245496083677\n", + "label\t84\t-\tDistance\t4.026791789923078\n", + "label\t50\t-\tDistance\t4.144496651017247\n", + "label\t74\t-\tDistance\t4.614517493407895\n", + "Latent semantic no. 1\n", + "label\t92\t-\tDistance\t3.230292112512146\n", + "label\t4\t-\tDistance\t3.5335656340201087\n", + "label\t2\t-\tDistance\t4.905027845590568\n", + "label\t69\t-\tDistance\t4.993399423965622\n", + "label\t65\t-\tDistance\t6.275170101152081\n", + "label\t21\t-\tDistance\t6.792963383606834\n", + "label\t95\t-\tDistance\t9.460863854781731\n", + "label\t60\t-\tDistance\t10.659440914917885\n", + "label\t82\t-\tDistance\t14.23961431596092\n", + "label\t51\t-\tDistance\t14.308250416010853\n", + "Latent semantic no. 2\n", + "label\t98\t-\tDistance\t4.084187568594383\n", + "label\t75\t-\tDistance\t4.208154727653996\n", + "label\t59\t-\tDistance\t4.267012427049042\n", + "label\t11\t-\tDistance\t4.3719751047928685\n", + "label\t63\t-\tDistance\t4.389793026579887\n", + "label\t64\t-\tDistance\t4.534510062334466\n", + "label\t32\t-\tDistance\t4.596340579479344\n", + "label\t79\t-\tDistance\t4.97413168034284\n", + "label\t55\t-\tDistance\t5.180445076965457\n", + "label\t84\t-\tDistance\t5.321702524477488\n", + "Latent semantic no. 3\n", + "label\t73\t-\tDistance\t2.2337776135986673\n", + "label\t77\t-\tDistance\t2.446394227315699\n", + "label\t22\t-\tDistance\t2.8266085928002305\n", + "label\t96\t-\tDistance\t2.951528289863372\n", + "label\t72\t-\tDistance\t3.0039788225292554\n", + "label\t45\t-\tDistance\t3.109522101340006\n", + "label\t74\t-\tDistance\t3.519269143632249\n", + "label\t57\t-\tDistance\t3.589490130921498\n", + "label\t50\t-\tDistance\t3.6391055564874\n", + "label\t18\t-\tDistance\t4.109290572000071\n", + "Latent semantic no. 4\n", + "label\t78\t-\tDistance\t1.8064076815500691\n", + "label\t6\t-\tDistance\t1.960264623688121\n", + "label\t7\t-\tDistance\t2.1426433652644246\n", + "label\t61\t-\tDistance\t2.211884975823563\n", + "label\t67\t-\tDistance\t2.2819452598845484\n", + "label\t20\t-\tDistance\t2.3104854768313308\n", + "label\t62\t-\tDistance\t2.4074452247279643\n", + "label\t23\t-\tDistance\t2.4123612373578465\n", + "label\t27\t-\tDistance\t2.5964084026274183\n", + "label\t26\t-\tDistance\t2.6484422926018762\n", + "Latent semantic no. 5\n", + "label\t1\t-\tDistance\t0.0\n", + "label\t66\t-\tDistance\t6.283884339485376\n", + "label\t0\t-\tDistance\t7.134186839941345\n", + "label\t68\t-\tDistance\t7.6014631552864165\n", + "label\t42\t-\tDistance\t7.699614640935743\n", + "label\t90\t-\tDistance\t7.745628525155249\n", + "label\t35\t-\tDistance\t7.888542444783939\n", + "label\t89\t-\tDistance\t8.296957431371565\n", + "label\t19\t-\tDistance\t8.389232149750157\n", + "label\t70\t-\tDistance\t8.416181651996403\n", + "Latent semantic no. 6\n", + "label\t58\t-\tDistance\t1.4491641647189777\n", + "label\t37\t-\tDistance\t1.5439214839372046\n", + "label\t24\t-\tDistance\t1.5452615348627594\n", + "label\t8\t-\tDistance\t1.7715264047899464\n", + "label\t25\t-\tDistance\t1.86516161648985\n", + "label\t87\t-\tDistance\t2.077478215700691\n", + "label\t15\t-\tDistance\t2.225120843217057\n", + "label\t16\t-\tDistance\t2.267782774837321\n", + "label\t39\t-\tDistance\t2.395932754700218\n", + "label\t36\t-\tDistance\t2.6959359474526083\n", + "Latent semantic no. 7\n", + "label\t44\t-\tDistance\t2.2303295853566074\n", + "label\t19\t-\tDistance\t2.233360628309259\n", + "label\t76\t-\tDistance\t2.2873290684053234\n", + "label\t49\t-\tDistance\t2.4188703660528543\n", + "label\t9\t-\tDistance\t2.4470406114525685\n", + "label\t54\t-\tDistance\t2.4930648472372385\n", + "label\t10\t-\tDistance\t2.5342743763741615\n", + "label\t33\t-\tDistance\t2.6761306270075935\n", + "label\t28\t-\tDistance\t2.76245021657484\n", + "label\t36\t-\tDistance\t2.8111765962163813\n", + "Latent semantic no. 8\n", + "label\t48\t-\tDistance\t3.1737547288361596\n", + "label\t85\t-\tDistance\t4.021946100259249\n", + "label\t91\t-\tDistance\t5.424003509894085\n", + "label\t52\t-\tDistance\t5.537608967567619\n", + "label\t43\t-\tDistance\t5.53813149000202\n", + "label\t80\t-\tDistance\t5.6425678719484225\n", + "label\t14\t-\tDistance\t5.87213105210823\n", + "label\t83\t-\tDistance\t7.001763109529681\n", + "label\t93\t-\tDistance\t7.563499549838895\n", + "label\t3\t-\tDistance\t8.036164382755217\n", + "Latent semantic no. 9\n", + "label\t51\t-\tDistance\t1.871618718249688\n", + "label\t82\t-\tDistance\t2.0311106628896\n", + "label\t29\t-\tDistance\t2.6509535626831533\n", + "label\t42\t-\tDistance\t3.083958153652753\n", + "label\t47\t-\tDistance\t3.198020753679005\n", + "label\t66\t-\tDistance\t3.5690360028582857\n", + "label\t35\t-\tDistance\t4.033659067172662\n", + "label\t60\t-\tDistance\t4.979629225985197\n", + "label\t33\t-\tDistance\t6.016698032150541\n", + "label\t95\t-\tDistance\t6.119613727077633\n" + ] + } + ], "source": [ "selected_feature_model = valid_feature_models[\n", " str(input(\"Enter feature model - one of \" + str(list(valid_feature_models.keys()))))\n", diff --git a/Phase 2/task_6.ipynb b/Phase 2/task_6.ipynb index 46c4787..c10883f 100644 --- a/Phase 2/task_6.ipynb +++ b/Phase 2/task_6.ipynb @@ -29,117 +29,120 @@ "name": "stdout", "output_type": "stream", "text": [ - "Applying svd on the given similarity matrix to get 10 latent semantics (showing only top 10 image-weight pairs for each latent semantic)...\n", + "Applying kmeans on the given similarity matrix to get 10 latent semantics (showing only top 10 image-weight pairs for each latent semantic)...\n", + "Initialized centroids\n", + "Iteration 78 - Converged\n", + "Note: for K-Means we display distances, in ascending order\n", "Latent semantic no. 0\n", - "image\t4327\t-\tWeight\t0.06387300915798859\n", - "image\t1653\t-\tWeight\t0.06225930582406118\n", - "image\t4309\t-\tWeight\t0.06095954690299202\n", - "image\t4329\t-\tWeight\t0.05889167793151601\n", - "image\t4318\t-\tWeight\t0.057637806985593974\n", - "image\t2325\t-\tWeight\t0.05612114732712442\n", - "image\t106\t-\tWeight\t0.0553241190050714\n", - "image\t4315\t-\tWeight\t0.05421665176601695\n", - "image\t3627\t-\tWeight\t0.05399516331236024\n", - "image\t4310\t-\tWeight\t0.053878520320048216\n", + "image\t2035\t-\tDistance\t15.193245192997269\n", + "image\t3813\t-\tDistance\t16.04888912166159\n", + "image\t3846\t-\tDistance\t16.9147608871532\n", + "image\t2925\t-\tDistance\t17.10454309878603\n", + "image\t3455\t-\tDistance\t18.102307562986407\n", + "image\t2410\t-\tDistance\t18.94942620886487\n", + "image\t2107\t-\tDistance\t19.47309707424604\n", + "image\t169\t-\tDistance\t19.533352544481723\n", + "image\t2584\t-\tDistance\t20.078954258235058\n", + "image\t2554\t-\tDistance\t20.31870945722174\n", "Latent semantic no. 1\n", - "image\t926\t-\tWeight\t0.05969523866379485\n", - "image\t900\t-\tWeight\t0.05738047297133547\n", - "image\t826\t-\tWeight\t0.05737642454934881\n", - "image\t868\t-\tWeight\t0.05734058503897999\n", - "image\t895\t-\tWeight\t0.05679076771674271\n", - "image\t904\t-\tWeight\t0.05665906456098433\n", - "image\t894\t-\tWeight\t0.05659729687888854\n", - "image\t892\t-\tWeight\t0.05637734281187336\n", - "image\t896\t-\tWeight\t0.05626000549868052\n", - "image\t901\t-\tWeight\t0.05621099897239924\n", + "image\t4287\t-\tDistance\t19.736469352294893\n", + "image\t1903\t-\tDistance\t20.625366718297947\n", + "image\t4283\t-\tDistance\t21.293764261460364\n", + "image\t2020\t-\tDistance\t21.439372707924147\n", + "image\t2216\t-\tDistance\t21.50575404583331\n", + "image\t4272\t-\tDistance\t22.731747308700246\n", + "image\t73\t-\tDistance\t22.984525532773183\n", + "image\t3118\t-\tDistance\t23.05742728400208\n", + "image\t622\t-\tDistance\t23.38211853588565\n", + "image\t4257\t-\tDistance\t23.952073196825147\n", "Latent semantic no. 2\n", - "image\t3685\t-\tWeight\t0.03814215214744671\n", - "image\t3049\t-\tWeight\t0.036692586102556525\n", - "image\t4152\t-\tWeight\t0.03586290113404638\n", - "image\t4219\t-\tWeight\t0.03489203423252102\n", - "image\t3208\t-\tWeight\t0.034624460148066645\n", - "image\t4015\t-\tWeight\t0.034128851806262454\n", - "image\t3838\t-\tWeight\t0.03287941968649245\n", - "image\t3544\t-\tWeight\t0.03241416766297603\n", - "image\t4313\t-\tWeight\t0.03231321369753675\n", - "image\t3999\t-\tWeight\t0.0320836528785244\n", + "image\t1607\t-\tDistance\t19.475143670438978\n", + "image\t1946\t-\tDistance\t21.51656357453921\n", + "image\t2356\t-\tDistance\t21.828572283680128\n", + "image\t3908\t-\tDistance\t22.05022203753488\n", + "image\t3547\t-\tDistance\t22.443143797259534\n", + "image\t2199\t-\tDistance\t22.571833277582463\n", + "image\t3519\t-\tDistance\t22.86028550513413\n", + "image\t1890\t-\tDistance\t23.30071966551519\n", + "image\t173\t-\tDistance\t23.303977110625112\n", + "image\t4270\t-\tDistance\t23.49369113941158\n", "Latent semantic no. 3\n", - "image\t3892\t-\tWeight\t0.06648490667589399\n", - "image\t3827\t-\tWeight\t0.06563860391481106\n", - "image\t4285\t-\tWeight\t0.06444388987867274\n", - "image\t2076\t-\tWeight\t0.0632874253246352\n", - "image\t3745\t-\tWeight\t0.059842032954945085\n", - "image\t2563\t-\tWeight\t0.058523031976053054\n", - "image\t3884\t-\tWeight\t0.05599712556112116\n", - "image\t3890\t-\tWeight\t0.05593428984607223\n", - "image\t1402\t-\tWeight\t0.055899080338839564\n", - "image\t2665\t-\tWeight\t0.05546907037179164\n", + "image\t3877\t-\tDistance\t26.69192882188752\n", + "image\t3763\t-\tDistance\t30.515760593946236\n", + "image\t3788\t-\tDistance\t32.71038293371164\n", + "image\t2735\t-\tDistance\t33.09699801502328\n", + "image\t1506\t-\tDistance\t36.057724149884244\n", + "image\t1686\t-\tDistance\t36.473691930187435\n", + "image\t3485\t-\tDistance\t36.49488863581563\n", + "image\t3920\t-\tDistance\t36.56582383384961\n", + "image\t3403\t-\tDistance\t37.44068139304385\n", + "image\t3762\t-\tDistance\t37.70577701904375\n", "Latent semantic no. 4\n", - "image\t3915\t-\tWeight\t0.046851797776368324\n", - "image\t474\t-\tWeight\t0.04434329032960532\n", - "image\t1536\t-\tWeight\t0.04432346480047559\n", - "image\t475\t-\tWeight\t0.04072813117892643\n", - "image\t4255\t-\tWeight\t0.040667016325110085\n", - "image\t3406\t-\tWeight\t0.04040216466729356\n", - "image\t525\t-\tWeight\t0.04029068846509459\n", - "image\t470\t-\tWeight\t0.03997152944980991\n", - "image\t1384\t-\tWeight\t0.03934508125909288\n", - "image\t3357\t-\tWeight\t0.038333999492323516\n", + "image\t1783\t-\tDistance\t16.117313734508425\n", + "image\t1395\t-\tDistance\t16.95429167145128\n", + "image\t1784\t-\tDistance\t17.57009310160933\n", + "image\t1789\t-\tDistance\t17.973453810738004\n", + "image\t1765\t-\tDistance\t18.610362195798043\n", + "image\t1773\t-\tDistance\t19.041096692299885\n", + "image\t2926\t-\tDistance\t19.11502996606766\n", + "image\t1685\t-\tDistance\t19.414760349222448\n", + "image\t2841\t-\tDistance\t19.81113964538446\n", + "image\t1460\t-\tDistance\t19.898834382884864\n", "Latent semantic no. 5\n", - "image\t3956\t-\tWeight\t0.060011760982094924\n", - "image\t2767\t-\tWeight\t0.056207067311306175\n", - "image\t2775\t-\tWeight\t0.05358938347103485\n", - "image\t3902\t-\tWeight\t0.04905728202908321\n", - "image\t3099\t-\tWeight\t0.048312802387355414\n", - "image\t2794\t-\tWeight\t0.04816327335760051\n", - "image\t3638\t-\tWeight\t0.047326856090526045\n", - "image\t2951\t-\tWeight\t0.04694598325565932\n", - "image\t2493\t-\tWeight\t0.04693933314957065\n", - "image\t1519\t-\tWeight\t0.04692793968385709\n", + "image\t3303\t-\tDistance\t20.969885324908756\n", + "image\t2045\t-\tDistance\t21.631692888420304\n", + "image\t3825\t-\tDistance\t21.716033288921732\n", + "image\t3929\t-\tDistance\t22.774521397811917\n", + "image\t1859\t-\tDistance\t23.372667860565386\n", + "image\t3232\t-\tDistance\t26.60481926894494\n", + "image\t3149\t-\tDistance\t26.715214213345696\n", + "image\t1502\t-\tDistance\t27.72700742629819\n", + "image\t1579\t-\tDistance\t28.000908326829553\n", + "image\t3021\t-\tDistance\t28.227049715323034\n", "Latent semantic no. 6\n", - "image\t899\t-\tWeight\t0.0557700158434989\n", - "image\t901\t-\tWeight\t0.05385589776037553\n", - "image\t903\t-\tWeight\t0.05326609068648323\n", - "image\t892\t-\tWeight\t0.05278963837571468\n", - "image\t893\t-\tWeight\t0.05252069078249831\n", - "image\t895\t-\tWeight\t0.052452644917562574\n", - "image\t894\t-\tWeight\t0.050699354240581404\n", - "image\t896\t-\tWeight\t0.049699837882682285\n", - "image\t898\t-\tWeight\t0.04862913600225998\n", - "image\t821\t-\tWeight\t0.048576878816213136\n", + "image\t1576\t-\tDistance\t18.87169047631405\n", + "image\t2858\t-\tDistance\t20.03847155817962\n", + "image\t1586\t-\tDistance\t20.080662203948876\n", + "image\t2850\t-\tDistance\t20.838413387796493\n", + "image\t2028\t-\tDistance\t21.169189788615924\n", + "image\t2716\t-\tDistance\t21.48136423054197\n", + "image\t4102\t-\tDistance\t21.660099255138686\n", + "image\t3457\t-\tDistance\t21.769184940550623\n", + "image\t1736\t-\tDistance\t21.85959261306364\n", + "image\t4314\t-\tDistance\t22.54861914619658\n", "Latent semantic no. 7\n", - "image\t1171\t-\tWeight\t0.03313529748848967\n", - "image\t1350\t-\tWeight\t0.0331120998877209\n", - "image\t1069\t-\tWeight\t0.03307893455959305\n", - "image\t1145\t-\tWeight\t0.032922013181510126\n", - "image\t1324\t-\tWeight\t0.03250158367280327\n", - "image\t1320\t-\tWeight\t0.03214994190354176\n", - "image\t1251\t-\tWeight\t0.0320119250948106\n", - "image\t1353\t-\tWeight\t0.031686689071375326\n", - "image\t1341\t-\tWeight\t0.031629828629460004\n", - "image\t1314\t-\tWeight\t0.03156526413086752\n", + "image\t3950\t-\tDistance\t25.18109439269185\n", + "image\t2023\t-\tDistance\t25.789279523766343\n", + "image\t3932\t-\tDistance\t27.90805288360532\n", + "image\t1434\t-\tDistance\t28.944408149134258\n", + "image\t2330\t-\tDistance\t29.442864276116474\n", + "image\t2349\t-\tDistance\t30.24950272216615\n", + "image\t3252\t-\tDistance\t30.39413795688458\n", + "image\t3526\t-\tDistance\t30.998757072825036\n", + "image\t1504\t-\tDistance\t31.665045303749636\n", + "image\t3117\t-\tDistance\t32.420320427638046\n", "Latent semantic no. 8\n", - "image\t4291\t-\tWeight\t0.030379982764735483\n", - "image\t3659\t-\tWeight\t0.030303639415262738\n", - "image\t4062\t-\tWeight\t0.03009699185922372\n", - "image\t3645\t-\tWeight\t0.030070839535056917\n", - "image\t4186\t-\tWeight\t0.029988779115247072\n", - "image\t3651\t-\tWeight\t0.02991186566754093\n", - "image\t4306\t-\tWeight\t0.029842728762290226\n", - "image\t4290\t-\tWeight\t0.029821657616221558\n", - "image\t4295\t-\tWeight\t0.029776560377156956\n", - "image\t4063\t-\tWeight\t0.029718537108632898\n", + "image\t2077\t-\tDistance\t13.954066038827099\n", + "image\t3402\t-\tDistance\t14.458733182318412\n", + "image\t1563\t-\tDistance\t16.656651855034323\n", + "image\t1846\t-\tDistance\t17.052974589353724\n", + "image\t730\t-\tDistance\t17.25680776558567\n", + "image\t3503\t-\tDistance\t17.367570425682572\n", + "image\t3405\t-\tDistance\t18.378448265500502\n", + "image\t4284\t-\tDistance\t19.017406411077424\n", + "image\t3809\t-\tDistance\t19.162266095814548\n", + "image\t2510\t-\tDistance\t19.361042141304708\n", "Latent semantic no. 9\n", - "image\t3461\t-\tWeight\t-0.01124924934362133\n", - "image\t3690\t-\tWeight\t-0.011359970957367781\n", - "image\t3677\t-\tWeight\t-0.011388302213245766\n", - "image\t2274\t-\tWeight\t-0.011401573573016426\n", - "image\t2695\t-\tWeight\t-0.011431074774005161\n", - "image\t3868\t-\tWeight\t-0.011463898013717732\n", - "image\t1137\t-\tWeight\t-0.011497915659232156\n", - "image\t1647\t-\tWeight\t-0.01152169864389044\n", - "image\t1203\t-\tWeight\t-0.011532017314265241\n", - "image\t2391\t-\tWeight\t-0.011548629237775063\n" + "image\t3393\t-\tDistance\t18.57180005298004\n", + "image\t2544\t-\tDistance\t20.113218222781388\n", + "image\t1930\t-\tDistance\t20.383444354935005\n", + "image\t1682\t-\tDistance\t21.019603660594967\n", + "image\t3155\t-\tDistance\t21.019680109622932\n", + "image\t4000\t-\tDistance\t21.85089581447219\n", + "image\t2815\t-\tDistance\t21.85923223224687\n", + "image\t2524\t-\tDistance\t22.415510254934645\n", + "image\t2907\t-\tDistance\t22.896560385522896\n", + "image\t2434\t-\tDistance\t22.9031446197451\n" ] } ], diff --git a/Phase 2/task_9.ipynb b/Phase 2/task_9.ipynb index 3985b51..5c99624 100644 --- a/Phase 2/task_9.ipynb +++ b/Phase 2/task_9.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 64, + "execution_count": 197, "metadata": {}, "outputs": [ { @@ -21,7 +21,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 198, "metadata": {}, "outputs": [], "source": [ @@ -35,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 199, "metadata": {}, "outputs": [], "source": [ @@ -45,14 +45,14 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 200, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "image_sim-cm_fd-nmf-10-semantics.json loaded\n" + "image_sim-cm_fd-kmeans-10-semantics.json loaded\n" ] } ], @@ -109,7 +109,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 201, "metadata": {}, "outputs": [], "source": [ @@ -135,8 +135,6 @@ " comparison_feature_space = np.matmul(U, S)\n", "\n", " if latent_space == \"image_sim\":\n", - " print(np.array(label_rep).shape)\n", - " print(np.array(S).shape)\n", " comparison_vector = np.matmul(label_rep, S)\n", " else:\n", " comparison_vector = np.matmul(np.matmul(label_rep, V), S)\n", @@ -162,6 +160,16 @@ " comparison_feature_space = np.array(data[\"image-semantic\"])\n", " S = np.array(data[\"semantic-feature\"])\n", "\n", + " if latent_space == \"image_sim\":\n", + " sim_matrix = np.array(data[\"sim-matrix\"])\n", + " label_vectors = []\n", + " length = len(sim_matrix)\n", + " for i in range(length):\n", + " if all_images[i][\"true_label\"] == label:\n", + " label_vectors.append(sim_matrix[i])\n", + " label_rep = [sum(col) / len(col) for col in zip(*label_vectors)]\n", + "\n", + "\n", " for centroid in S:\n", " comparison_vector.append(math.dist(label_rep, centroid))\n", "\n", @@ -169,7 +177,7 @@ "\n", " distances = []\n", " for i in range(n):\n", - " if i != label:\n", + " if all_images[i][\"true_label\"] != label:\n", " distances.append({\"image_id\": i, \"label\": all_images[i][\"true_label\"], \"distance\": math.dist(comparison_vector, comparison_feature_space[i])})\n", "\n", " distances = sorted(distances, key=lambda x: x[\"distance\"], reverse=False)\n", @@ -192,7 +200,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 202, "metadata": {}, "outputs": [], "source": [ @@ -230,23 +238,23 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 203, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'image_id': 1102, 'label': 5, 'distance': 0.4995439271653643}\n", - "{'image_id': 637, 'label': 3, 'distance': 0.6162759255696203}\n", - "{'image_id': 1450, 'label': 9, 'distance': 0.6537940561051517}\n", - "{'image_id': 2148, 'label': 30, 'distance': 0.6885146297494956}\n", - "{'image_id': 3574, 'label': 77, 'distance': 0.6970323320729979}\n", - "{'image_id': 2202, 'label': 31, 'distance': 0.6975621319006345}\n", - "{'image_id': 2917, 'label': 54, 'distance': 0.7112049573397025}\n", - "{'image_id': 4325, 'label': 100, 'distance': 0.7394787087142192}\n", - "{'image_id': 1543, 'label': 12, 'distance': 0.7404143327603417}\n", - "{'image_id': 2333, 'label': 35, 'distance': 0.7432769566450207}\n" + "{'image_id': 1596, 'label': 13, 'distance': 10.699607616770502}\n", + "{'image_id': 81, 'label': 0, 'distance': 11.42726536242745}\n", + "{'image_id': 3045, 'label': 57, 'distance': 12.5398964971548}\n", + "{'image_id': 311, 'label': 1, 'distance': 13.106117374912184}\n", + "{'image_id': 2671, 'label': 47, 'distance': 14.239608716065096}\n", + "{'image_id': 1923, 'label': 23, 'distance': 15.409297843450119}\n", + "{'image_id': 3471, 'label': 74, 'distance': 15.417780769047727}\n", + "{'image_id': 4108, 'label': 94, 'distance': 17.628035952336866}\n", + "{'image_id': 1547, 'label': 12, 'distance': 19.28128511589925}\n", + "{'image_id': 3115, 'label': 59, 'distance': 19.762521112658867}\n" ] } ], From e463f3ab58a56022cfaf876936b98206286b1ff6 Mon Sep 17 00:00:00 2001 From: pranavbrkr Date: Fri, 13 Oct 2023 19:32:14 -0700 Subject: [PATCH 08/10] ls2 for task 9 --- Phase 2/task_4.ipynb | 352 ++++++++++++++++++++++++++++++++++++++++++- Phase 2/task_9.ipynb | 70 ++++++--- 2 files changed, 399 insertions(+), 23 deletions(-) diff --git a/Phase 2/task_4.ipynb b/Phase 2/task_4.ipynb index 9d8d25c..25d660c 100644 --- a/Phase 2/task_4.ipynb +++ b/Phase 2/task_4.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -22,9 +22,351 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Applying CP decomposition on the cm_fd space to get 10 latent semantics (showing only top 10 image-weight pairs for each latent semantic)...\n", + "(4339, 900, 101)\n", + "Showing image-weight latent semantic\n", + "Latent semantic no. 0\n", + "image\t823\t-\tweight\t0.06101574157129977\n", + "image\t809\t-\tweight\t0.06062830674568429\n", + "image\t806\t-\tweight\t0.060448512391290884\n", + "image\t832\t-\tweight\t0.06044200170224582\n", + "image\t830\t-\tweight\t0.06028043498591822\n", + "image\t808\t-\tweight\t0.06026752408221666\n", + "image\t772\t-\tweight\t0.06021140705672949\n", + "image\t750\t-\tweight\t0.060162025437143496\n", + "image\t844\t-\tweight\t0.060101929899988096\n", + "image\t784\t-\tweight\t0.06003388853666149\n", + "Latent semantic no. 1\n", + "image\t980\t-\tweight\t0.0754118902341204\n", + "image\t1084\t-\tweight\t0.07362125136812943\n", + "image\t1011\t-\tweight\t0.06967853969554338\n", + "image\t997\t-\tweight\t0.06873541509302017\n", + "image\t1065\t-\tweight\t0.06811244748351335\n", + "image\t1184\t-\tweight\t0.06715708987702379\n", + "image\t1053\t-\tweight\t0.06697645698011236\n", + "image\t962\t-\tweight\t0.06677715910430597\n", + "image\t1287\t-\tweight\t0.06650715541378867\n", + "image\t968\t-\tweight\t0.065552599099574\n", + "Latent semantic no. 2\n", + "image\t0\t-\tweight\t0.0\n", + "image\t1\t-\tweight\t0.0\n", + "image\t2\t-\tweight\t0.0\n", + "image\t3\t-\tweight\t0.0\n", + "image\t4\t-\tweight\t0.0\n", + "image\t5\t-\tweight\t0.0\n", + "image\t6\t-\tweight\t0.0\n", + "image\t7\t-\tweight\t0.0\n", + "image\t8\t-\tweight\t0.0\n", + "image\t9\t-\tweight\t0.0\n", + "Latent semantic no. 3\n", + "image\t218\t-\tweight\t0.0\n", + "image\t219\t-\tweight\t0.0\n", + "image\t220\t-\tweight\t0.0\n", + "image\t221\t-\tweight\t0.0\n", + "image\t222\t-\tweight\t0.0\n", + "image\t223\t-\tweight\t0.0\n", + "image\t224\t-\tweight\t0.0\n", + "image\t225\t-\tweight\t0.0\n", + "image\t226\t-\tweight\t0.0\n", + "image\t227\t-\tweight\t0.0\n", + "Latent semantic no. 4\n", + "image\t4178\t-\tweight\t0.13411466061203375\n", + "image\t4133\t-\tweight\t0.13381869962849108\n", + "image\t4186\t-\tweight\t0.1328204635772519\n", + "image\t4112\t-\tweight\t0.13246858130287337\n", + "image\t4165\t-\tweight\t0.13047834274654035\n", + "image\t4096\t-\tweight\t0.12970904464417174\n", + "image\t4130\t-\tweight\t0.1276357487547854\n", + "image\t4163\t-\tweight\t0.12611220410021198\n", + "image\t4175\t-\tweight\t0.12609814994237703\n", + "image\t4125\t-\tweight\t0.12492475451893506\n", + "Latent semantic no. 5\n", + "image\t0\t-\tweight\t0.0\n", + "image\t1\t-\tweight\t0.0\n", + "image\t2\t-\tweight\t0.0\n", + "image\t3\t-\tweight\t0.0\n", + "image\t4\t-\tweight\t0.0\n", + "image\t5\t-\tweight\t0.0\n", + "image\t6\t-\tweight\t0.0\n", + "image\t7\t-\tweight\t0.0\n", + "image\t8\t-\tweight\t0.0\n", + "image\t9\t-\tweight\t0.0\n", + "Latent semantic no. 6\n", + "image\t0\t-\tweight\t0.0\n", + "image\t1\t-\tweight\t0.0\n", + "image\t2\t-\tweight\t0.0\n", + "image\t3\t-\tweight\t0.0\n", + "image\t4\t-\tweight\t0.0\n", + "image\t5\t-\tweight\t0.0\n", + "image\t6\t-\tweight\t0.0\n", + "image\t7\t-\tweight\t0.0\n", + "image\t8\t-\tweight\t0.0\n", + "image\t9\t-\tweight\t0.0\n", + "Latent semantic no. 7\n", + "image\t0\t-\tweight\t0.0\n", + "image\t1\t-\tweight\t0.0\n", + "image\t2\t-\tweight\t0.0\n", + "image\t3\t-\tweight\t0.0\n", + "image\t4\t-\tweight\t0.0\n", + "image\t5\t-\tweight\t0.0\n", + "image\t6\t-\tweight\t0.0\n", + "image\t7\t-\tweight\t0.0\n", + "image\t8\t-\tweight\t0.0\n", + "image\t9\t-\tweight\t0.0\n", + "Latent semantic no. 8\n", + "image\t0\t-\tweight\t0.0\n", + "image\t1\t-\tweight\t0.0\n", + "image\t2\t-\tweight\t0.0\n", + "image\t3\t-\tweight\t0.0\n", + "image\t4\t-\tweight\t0.0\n", + "image\t5\t-\tweight\t0.0\n", + "image\t6\t-\tweight\t0.0\n", + "image\t7\t-\tweight\t0.0\n", + "image\t8\t-\tweight\t0.0\n", + "image\t9\t-\tweight\t0.0\n", + "Latent semantic no. 9\n", + "image\t0\t-\tweight\t0.0\n", + "image\t1\t-\tweight\t0.0\n", + "image\t2\t-\tweight\t0.0\n", + "image\t3\t-\tweight\t0.0\n", + "image\t4\t-\tweight\t0.0\n", + "image\t5\t-\tweight\t0.0\n", + "image\t6\t-\tweight\t0.0\n", + "image\t7\t-\tweight\t0.0\n", + "image\t8\t-\tweight\t0.0\n", + "image\t9\t-\tweight\t0.0\n", + "Showing feature-weight latent semantic\n", + "Latent semantic no. 0\n", + "feature\t0\t-\tweight\t0.07577182824380883\n", + "feature\t9\t-\tweight\t0.07573677778472039\n", + "feature\t3\t-\tweight\t0.07557126037394385\n", + "feature\t6\t-\tweight\t0.0753781982199277\n", + "feature\t7\t-\tweight\t0.0001951950616295707\n", + "feature\t4\t-\tweight\t0.00017549416192999285\n", + "feature\t1\t-\tweight\t0.00012194961415249631\n", + "feature\t2\t-\tweight\t4.287652912002155e-06\n", + "feature\t8\t-\tweight\t-2.314815116961173e-06\n", + "feature\t5\t-\tweight\t-5.197417109414247e-06\n", + "Latent semantic no. 1\n", + "feature\t9\t-\tweight\t0.09115512060365509\n", + "feature\t0\t-\tweight\t0.09113096158559393\n", + "feature\t3\t-\tweight\t0.09044159243667037\n", + "feature\t6\t-\tweight\t0.09018059343754826\n", + "feature\t7\t-\tweight\t0.0021130646053269977\n", + "feature\t4\t-\tweight\t0.002030234496532767\n", + "feature\t1\t-\tweight\t0.002009281088063933\n", + "feature\t5\t-\tweight\t-0.0006594073704548895\n", + "feature\t2\t-\tweight\t-0.0006852646011520126\n", + "feature\t8\t-\tweight\t-0.0007029010314333884\n", + "Latent semantic no. 2\n", + "feature\t2\t-\tweight\t0.0017202269882332225\n", + "feature\t5\t-\tweight\t0.0012782693995776035\n", + "feature\t8\t-\tweight\t0.0011056757480729573\n", + "feature\t4\t-\tweight\t-0.008186797881888055\n", + "feature\t1\t-\tweight\t-0.008219352401356154\n", + "feature\t7\t-\tweight\t-0.0082326697588083\n", + "feature\t6\t-\tweight\t-0.052967486759999564\n", + "feature\t0\t-\tweight\t-0.053269849112101635\n", + "feature\t3\t-\tweight\t-0.053314794168011104\n", + "feature\t9\t-\tweight\t-0.054790922702571875\n", + "Latent semantic no. 3\n", + "feature\t8\t-\tweight\t0.0012457435033955949\n", + "feature\t5\t-\tweight\t0.0011302318267326496\n", + "feature\t2\t-\tweight\t0.0008915556276299796\n", + "feature\t1\t-\tweight\t-0.0067143893348969585\n", + "feature\t7\t-\tweight\t-0.006979411165292033\n", + "feature\t4\t-\tweight\t-0.007115085250947199\n", + "feature\t9\t-\tweight\t-0.04627063193585373\n", + "feature\t0\t-\tweight\t-0.04669584380231813\n", + "feature\t3\t-\tweight\t-0.05358789010258499\n", + "feature\t6\t-\tweight\t-0.05372707309592606\n", + "Latent semantic no. 4\n", + "feature\t9\t-\tweight\t0.06636728404803105\n", + "feature\t0\t-\tweight\t0.06602164959781295\n", + "feature\t6\t-\tweight\t0.0658680657426211\n", + "feature\t3\t-\tweight\t0.0658246292439607\n", + "feature\t7\t-\tweight\t0.0024698135167617423\n", + "feature\t1\t-\tweight\t0.0024642047114514965\n", + "feature\t4\t-\tweight\t0.002441605516788918\n", + "feature\t2\t-\tweight\t3.412235539925601e-05\n", + "feature\t8\t-\tweight\t3.0388889414075837e-05\n", + "feature\t5\t-\tweight\t-3.5221294849889635e-05\n", + "Latent semantic no. 5\n", + "feature\t2\t-\tweight\t-0.0004406092931454461\n", + "feature\t8\t-\tweight\t-0.0004885796160427777\n", + "feature\t5\t-\tweight\t-0.00061257112416781\n", + "feature\t1\t-\tweight\t-0.007155350004314086\n", + "feature\t4\t-\tweight\t-0.007165462449854097\n", + "feature\t7\t-\tweight\t-0.007444856783482605\n", + "feature\t6\t-\tweight\t-0.06042702105743578\n", + "feature\t3\t-\tweight\t-0.06082664033553194\n", + "feature\t0\t-\tweight\t-0.061400505754596324\n", + "feature\t9\t-\tweight\t-0.06196505846576572\n", + "Latent semantic no. 6\n", + "feature\t8\t-\tweight\t0.001098350913411075\n", + "feature\t2\t-\tweight\t0.0010016779555276794\n", + "feature\t5\t-\tweight\t0.0005821006414327626\n", + "feature\t4\t-\tweight\t-0.005900355271379414\n", + "feature\t1\t-\tweight\t-0.00598284109579637\n", + "feature\t7\t-\tweight\t-0.0061876362657868585\n", + "feature\t0\t-\tweight\t-0.06848064266828655\n", + "feature\t3\t-\tweight\t-0.06851566211382039\n", + "feature\t6\t-\tweight\t-0.0686464268816822\n", + "feature\t9\t-\tweight\t-0.0692640377395642\n", + "Latent semantic no. 7\n", + "feature\t5\t-\tweight\t0.0017254022262197148\n", + "feature\t8\t-\tweight\t0.001702794313355738\n", + "feature\t2\t-\tweight\t0.0003159487432469688\n", + "feature\t1\t-\tweight\t-0.0020164365697178\n", + "feature\t4\t-\tweight\t-0.0027621541654151115\n", + "feature\t7\t-\tweight\t-0.002853145862649653\n", + "feature\t0\t-\tweight\t-0.06195980356747699\n", + "feature\t9\t-\tweight\t-0.06199428708500771\n", + "feature\t6\t-\tweight\t-0.06368714708105448\n", + "feature\t3\t-\tweight\t-0.06385832819997592\n", + "Latent semantic no. 8\n", + "feature\t5\t-\tweight\t0.000579681022086685\n", + "feature\t8\t-\tweight\t0.00023776162317446615\n", + "feature\t2\t-\tweight\t-8.903068121134156e-05\n", + "feature\t1\t-\tweight\t-0.003628410033754683\n", + "feature\t4\t-\tweight\t-0.004042182279091933\n", + "feature\t7\t-\tweight\t-0.004079610197989652\n", + "feature\t0\t-\tweight\t-0.07078512499474116\n", + "feature\t9\t-\tweight\t-0.07090128668558571\n", + "feature\t6\t-\tweight\t-0.07284859530849022\n", + "feature\t3\t-\tweight\t-0.07317257962730919\n", + "Latent semantic no. 9\n", + "feature\t5\t-\tweight\t8.414336905221735e-05\n", + "feature\t8\t-\tweight\t5.6427744439484355e-05\n", + "feature\t2\t-\tweight\t-0.00016083066069906443\n", + "feature\t1\t-\tweight\t-0.0028491737945443647\n", + "feature\t4\t-\tweight\t-0.003057268122115886\n", + "feature\t7\t-\tweight\t-0.003069397622655647\n", + "feature\t0\t-\tweight\t-0.06533602471133236\n", + "feature\t3\t-\tweight\t-0.06579569084919401\n", + "feature\t6\t-\tweight\t-0.06579792129352352\n", + "feature\t9\t-\tweight\t-0.06583901427338712\n", + "Showing label-weight latent semantic\n", + "Latent semantic no. 0\n", + "label\t3\t-\tweight\t1.0\n", + "label\t0\t-\tweight\t0.0\n", + "label\t1\t-\tweight\t0.0\n", + "label\t2\t-\tweight\t0.0\n", + "label\t4\t-\tweight\t0.0\n", + "label\t5\t-\tweight\t0.0\n", + "label\t6\t-\tweight\t0.0\n", + "label\t7\t-\tweight\t0.0\n", + "label\t8\t-\tweight\t0.0\n", + "label\t9\t-\tweight\t0.0\n", + "Latent semantic no. 1\n", + "label\t5\t-\tweight\t0.9999999999999999\n", + "label\t0\t-\tweight\t0.0\n", + "label\t1\t-\tweight\t0.0\n", + "label\t2\t-\tweight\t0.0\n", + "label\t3\t-\tweight\t0.0\n", + "label\t4\t-\tweight\t0.0\n", + "label\t6\t-\tweight\t0.0\n", + "label\t7\t-\tweight\t0.0\n", + "label\t8\t-\tweight\t0.0\n", + "label\t9\t-\tweight\t0.0\n", + "Latent semantic no. 2\n", + "label\t1\t-\tweight\t0.9999999999999998\n", + "label\t0\t-\tweight\t0.0\n", + "label\t2\t-\tweight\t0.0\n", + "label\t3\t-\tweight\t0.0\n", + "label\t4\t-\tweight\t0.0\n", + "label\t5\t-\tweight\t0.0\n", + "label\t6\t-\tweight\t0.0\n", + "label\t7\t-\tweight\t0.0\n", + "label\t8\t-\tweight\t0.0\n", + "label\t9\t-\tweight\t0.0\n", + "Latent semantic no. 3\n", + "label\t0\t-\tweight\t0.9999999999999994\n", + "label\t1\t-\tweight\t0.0\n", + "label\t2\t-\tweight\t0.0\n", + "label\t3\t-\tweight\t0.0\n", + "label\t4\t-\tweight\t0.0\n", + "label\t5\t-\tweight\t0.0\n", + "label\t6\t-\tweight\t0.0\n", + "label\t7\t-\tweight\t0.0\n", + "label\t8\t-\tweight\t0.0\n", + "label\t9\t-\tweight\t0.0\n", + "Latent semantic no. 4\n", + "label\t94\t-\tweight\t1.0000000000000007\n", + "label\t0\t-\tweight\t0.0\n", + "label\t1\t-\tweight\t0.0\n", + "label\t2\t-\tweight\t0.0\n", + "label\t3\t-\tweight\t0.0\n", + "label\t4\t-\tweight\t0.0\n", + "label\t5\t-\tweight\t0.0\n", + "label\t6\t-\tweight\t0.0\n", + "label\t7\t-\tweight\t0.0\n", + "label\t8\t-\tweight\t0.0\n", + "Latent semantic no. 5\n", + "label\t55\t-\tweight\t1.0\n", + "label\t0\t-\tweight\t0.0\n", + "label\t1\t-\tweight\t0.0\n", + "label\t2\t-\tweight\t0.0\n", + "label\t3\t-\tweight\t0.0\n", + "label\t4\t-\tweight\t0.0\n", + "label\t5\t-\tweight\t0.0\n", + "label\t6\t-\tweight\t0.0\n", + "label\t7\t-\tweight\t0.0\n", + "label\t8\t-\tweight\t0.0\n", + "Latent semantic no. 6\n", + "label\t12\t-\tweight\t0.9999999999999998\n", + "label\t0\t-\tweight\t0.0\n", + "label\t1\t-\tweight\t0.0\n", + "label\t2\t-\tweight\t0.0\n", + "label\t3\t-\tweight\t0.0\n", + "label\t4\t-\tweight\t0.0\n", + "label\t5\t-\tweight\t0.0\n", + "label\t6\t-\tweight\t0.0\n", + "label\t7\t-\tweight\t0.0\n", + "label\t8\t-\tweight\t0.0\n", + "Latent semantic no. 7\n", + "label\t63\t-\tweight\t0.9999999999999997\n", + "label\t0\t-\tweight\t0.0\n", + "label\t1\t-\tweight\t0.0\n", + "label\t2\t-\tweight\t0.0\n", + "label\t3\t-\tweight\t0.0\n", + "label\t4\t-\tweight\t0.0\n", + "label\t5\t-\tweight\t0.0\n", + "label\t6\t-\tweight\t0.0\n", + "label\t7\t-\tweight\t0.0\n", + "label\t8\t-\tweight\t0.0\n", + "Latent semantic no. 8\n", + "label\t46\t-\tweight\t1.0\n", + "label\t0\t-\tweight\t0.0\n", + "label\t1\t-\tweight\t0.0\n", + "label\t2\t-\tweight\t0.0\n", + "label\t3\t-\tweight\t0.0\n", + "label\t4\t-\tweight\t0.0\n", + "label\t5\t-\tweight\t0.0\n", + "label\t6\t-\tweight\t0.0\n", + "label\t7\t-\tweight\t0.0\n", + "label\t8\t-\tweight\t0.0\n", + "Latent semantic no. 9\n", + "label\t93\t-\tweight\t1.0000000000000002\n", + "label\t0\t-\tweight\t0.0\n", + "label\t1\t-\tweight\t0.0\n", + "label\t2\t-\tweight\t0.0\n", + "label\t3\t-\tweight\t0.0\n", + "label\t4\t-\tweight\t0.0\n", + "label\t5\t-\tweight\t0.0\n", + "label\t6\t-\tweight\t0.0\n", + "label\t7\t-\tweight\t0.0\n", + "label\t8\t-\tweight\t0.0\n" + ] + } + ], "source": [ "selected_feature_model = valid_feature_models[\n", " str(input(\"Enter feature model - one of \" + str(list(valid_feature_models.keys()))))\n", @@ -59,7 +401,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.5" + "version": "3.11.4" } }, "nbformat": 4, diff --git a/Phase 2/task_9.ipynb b/Phase 2/task_9.ipynb index 5c99624..c57a926 100644 --- a/Phase 2/task_9.ipynb +++ b/Phase 2/task_9.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 197, + "execution_count": 207, "metadata": {}, "outputs": [ { @@ -21,7 +21,7 @@ }, { "cell_type": "code", - "execution_count": 198, + "execution_count": 208, "metadata": {}, "outputs": [], "source": [ @@ -35,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 199, + "execution_count": 209, "metadata": {}, "outputs": [], "source": [ @@ -45,14 +45,14 @@ }, { "cell_type": "code", - "execution_count": 200, + "execution_count": 210, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "image_sim-cm_fd-kmeans-10-semantics.json loaded\n" + "cm_fd-cp-10-semantics.json loaded\n" ] } ], @@ -109,7 +109,7 @@ }, { "cell_type": "code", - "execution_count": 201, + "execution_count": 211, "metadata": {}, "outputs": [], "source": [ @@ -200,7 +200,37 @@ }, { "cell_type": "code", - "execution_count": 202, + "execution_count": 233, + "metadata": {}, + "outputs": [], + "source": [ + "def extract_similarities_ls2(data, label):\n", + "\n", + " LS = np.array(data[\"label-semantic\"])\n", + " S = np.array(data[\"semantics-core\"])\n", + "\n", + " if len(S.shape) == 1:\n", + " S = np.diag(S)\n", + "\n", + " comparison_vector = LS[label]\n", + " comparison_feature_space = np.matmul(LS, S)\n", + "\n", + " distances = []\n", + "\n", + " n = len(comparison_feature_space)\n", + " for i in range(n):\n", + " if i != label:\n", + " distances.append({\"label\": i, \"distance\": math.dist(comparison_vector, comparison_feature_space[i])})\n", + " \n", + " distances = sorted(distances, key=lambda x: x[\"distance\"], reverse=False)[:knum]\n", + "\n", + " for x in distances:\n", + " print(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 234, "metadata": {}, "outputs": [], "source": [ @@ -238,23 +268,23 @@ }, { "cell_type": "code", - "execution_count": 203, + "execution_count": 235, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'image_id': 1596, 'label': 13, 'distance': 10.699607616770502}\n", - "{'image_id': 81, 'label': 0, 'distance': 11.42726536242745}\n", - "{'image_id': 3045, 'label': 57, 'distance': 12.5398964971548}\n", - "{'image_id': 311, 'label': 1, 'distance': 13.106117374912184}\n", - "{'image_id': 2671, 'label': 47, 'distance': 14.239608716065096}\n", - "{'image_id': 1923, 'label': 23, 'distance': 15.409297843450119}\n", - "{'image_id': 3471, 'label': 74, 'distance': 15.417780769047727}\n", - "{'image_id': 4108, 'label': 94, 'distance': 17.628035952336866}\n", - "{'image_id': 1547, 'label': 12, 'distance': 19.28128511589925}\n", - "{'image_id': 3115, 'label': 59, 'distance': 19.762521112658867}\n" + "{'label': 2, 'distance': 0.9999999999999999}\n", + "{'label': 4, 'distance': 0.9999999999999999}\n", + "{'label': 6, 'distance': 0.9999999999999999}\n", + "{'label': 7, 'distance': 0.9999999999999999}\n", + "{'label': 8, 'distance': 0.9999999999999999}\n", + "{'label': 9, 'distance': 0.9999999999999999}\n", + "{'label': 10, 'distance': 0.9999999999999999}\n", + "{'label': 11, 'distance': 0.9999999999999999}\n", + "{'label': 13, 'distance': 0.9999999999999999}\n", + "{'label': 14, 'distance': 0.9999999999999999}\n" ] } ], @@ -268,6 +298,10 @@ " case \"label_sim\":\n", "\n", " extract_similarities_ls3(selected_dim_reduction_method, data, label)\n", + "\n", + " case \"cp\":\n", + "\n", + " extract_similarities_ls2(data, label)\n", " " ] }, From 680fae35cd3fc49ee54e4ab009cb576053ea4dee Mon Sep 17 00:00:00 2001 From: pranavbrkr Date: Fri, 13 Oct 2023 23:24:48 -0700 Subject: [PATCH 09/10] Task 7 --- Phase 2/task_7.ipynb | 305 +++++++++++++++++++++++++++++++++++++++++++ Phase 2/task_9.ipynb | 23 +--- 2 files changed, 309 insertions(+), 19 deletions(-) create mode 100644 Phase 2/task_7.ipynb diff --git a/Phase 2/task_7.ipynb b/Phase 2/task_7.ipynb new file mode 100644 index 0000000..f782863 --- /dev/null +++ b/Phase 2/task_7.ipynb @@ -0,0 +1,305 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import os\n", + "import numpy as np\n", + "from utils import *\n", + "import math\n", + "import heapq\n", + "import random" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "fd_collection = getCollection(\"team_5_mwdb_phase_2\", \"fd_collection\")\n", + "all_images = fd_collection.find()" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "image_sim-cm_fd-kmeans-10-semantics.json loaded\n" + ] + } + ], + "source": [ + "selected_latent_space = valid_latent_spaces[\n", + " str(input(\"Enter latent space - one of \" + str(list(valid_latent_spaces.keys()))))\n", + "]\n", + "\n", + "selected_feature_model = valid_feature_models[\n", + " str(input(\"Enter feature model - one of \" + str(list(valid_feature_models.keys()))))\n", + "]\n", + "\n", + "k = int(input(\"Enter value of k: \"))\n", + "if k < 1:\n", + " raise ValueError(\"k should be a positive integer\")\n", + "\n", + "selected_dim_reduction_method = str(\n", + " input(\n", + " \"Enter dimensionality reduction method - one of \"\n", + " + str(list(valid_dim_reduction_methods.keys()))\n", + " )\n", + ")\n", + "\n", + "image_id = int(input(\"Enter image ID: \"))\n", + "if image_id < 0 and image_id > 8676 and image_id % 2 != 0:\n", + " raise ValueError(\"image id should be even number between 0 and 8676\")\n", + "\n", + "knum = int(input(\"Enter value of knum: \"))\n", + "if knum < 1:\n", + " raise ValueError(\"knum should be a positive integer\")\n", + "\n", + "match selected_latent_space:\n", + " case \"\":\n", + " if os.path.exists(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"):\n", + " data = json.load(open(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"))\n", + " print(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json loaded\")\n", + " else:\n", + " print(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json does not exist\")\n", + " case \"cp\":\n", + " if os.path.exists(f\"{selected_feature_model}-cp-{k}-semantics.json\"):\n", + " data = json.load(open(f\"{selected_feature_model}-cp-{k}-semantics.json\"))\n", + " print(f\"{selected_feature_model}-cp-{k}-semantics.json loaded\")\n", + " else: \n", + " print(f\"{selected_feature_model}-cp-{k}-semantics.json does not exist\")\n", + " case _:\n", + " if os.path.exists(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"):\n", + " data = json.load(open(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"))\n", + " print(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json loaded\")\n", + " else:\n", + " print(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json does not exist\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "def extract_similarities_ls1_ls4(latent_space, dim_reduction, selected_feature_model, data, image_id):\n", + "\n", + " image_fd = np.array(all_images[int(image_id / 2)][selected_feature_model]).flatten()\n", + "\n", + " match dim_reduction:\n", + "\n", + " case 'svd':\n", + " U = np.array(data[\"image-semantic\"])\n", + " S = np.array(data[\"semantics-core\"])\n", + " if len(S.shape) == 1:\n", + " S = np.diag(S)\n", + " V = np.transpose(np.array(data[\"semantic-feature\"]))\n", + " \n", + " comparison_feature_space = np.matmul(U, S)\n", + "\n", + " if latent_space == \"image_sim\":\n", + " comparison_vector = comparison_feature_space[int(image_id / 2)]\n", + " else:\n", + " comparison_vector = np.matmul(np.matmul(image_fd, V), S)\n", + " \n", + " case \"nmf\":\n", + " H = np.array(data['semantic-feature'])\n", + " comparison_feature_space = np.array(data['image-semantic'])\n", + "\n", + " if latent_space == \"image_sim\":\n", + " comparison_vector = comparison_feature_space[int(image_id / 2)]\n", + " else:\n", + " comparison_vector = np.matmul(image_fd, np.transpose(H))\n", + "\n", + " case \"kmeans\":\n", + " comparison_vector = []\n", + " comparison_feature_space = np.array(data[\"image-semantic\"])\n", + " S = np.array(data[\"semantic-feature\"])\n", + "\n", + " for centroid in S:\n", + " if latent_space == \"image_sim\":\n", + " sim_matrix = np.array(data[\"sim-matrix\"])\n", + " comparison_vector.append(math.dist(sim_matrix[int(image_id / 2)], centroid))\n", + " else:\n", + " comparison_vector.append(math.dist(image_fd, centroid))\n", + "\n", + " n = len(comparison_feature_space)\n", + "\n", + " distances = []\n", + " for i in range(n):\n", + " if (i * 2) != image_id:\n", + " distances.append({\"image_id\": i, \"label\": all_images[i][\"true_label\"], \"distance\": math.dist(comparison_vector, comparison_feature_space[i])})\n", + "\n", + " distances = sorted(distances, key=lambda x: x[\"distance\"], reverse=False)[:knum]\n", + "\n", + " for x in distances:\n", + " print(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "def extract_similarities_ls2(data, image_id):\n", + "\n", + " IS = np.array(data[\"image-semantic\"])\n", + " S = np.array(data[\"semantics-core\"])\n", + "\n", + " if len(S.shape) == 1:\n", + " S = np.diag(S)\n", + "\n", + " comparison_feature_space = np.matmul(IS, S)\n", + " comparison_vector = comparison_feature_space[int(image_id / 2)]\n", + "\n", + " distances = []\n", + "\n", + " n = len(comparison_feature_space)\n", + " for i in range(n):\n", + " if i != (image_id / 2):\n", + " distances.append({\"image_id\": i * 2, \"distance\": math.dist(comparison_vector, comparison_feature_space[i])})\n", + " \n", + " distances = sorted(distances, key=lambda x: x[\"distance\"], reverse=False)[:knum]\n", + "\n", + " for x in distances:\n", + " print(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "def extract_similarities_ls3(dim_reduction, data, image_id):\n", + "\n", + " img_label = all_images[int(image_id / 2)][\"true_label\"]\n", + "\n", + " match dim_reduction:\n", + "\n", + " case 'svd':\n", + " U = np.array(data[\"image-semantic\"])\n", + " S = np.array(data[\"semantics-core\"])\n", + " V = np.transpose(np.array(data[\"semantic-feature\"]))\n", + "\n", + " comparison_feature_space = np.matmul(U, S)\n", + " comparison_vector = comparison_feature_space[img_label]\n", + " \n", + " case \"nmf\":\n", + " comparison_feature_space = np.array(data['image-semantic'])\n", + " comparison_vector = comparison_feature_space[img_label]\n", + "\n", + " case \"kmeans\":\n", + " comparison_feature_space = np.array(data[\"image-semantic\"])\n", + " comparison_vector = comparison_feature_space[img_label]\n", + "\n", + " n = len(comparison_feature_space)\n", + " distance = float('inf')\n", + " most_similar_label = img_label\n", + " for i in range(n):\n", + " if i != img_label:\n", + " temp_distance = math.dist(comparison_vector, comparison_feature_space[i])\n", + " if distance > temp_distance:\n", + " distance = temp_distance\n", + " most_similar_label = i\n", + "\n", + " label_images = [x[\"image_id\"] for x in all_images if x[\"true_label\"] == most_similar_label]\n", + " similar_images = random.sample(label_images, knum)\n", + "\n", + " print(f\"Most similar label to {img_label} is {most_similar_label}\")\n", + " for img in similar_images:\n", + " print(img)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'image_id': 2457, 'label': 39, 'distance': 5.400083378408386}\n", + "{'image_id': 2629, 'label': 46, 'distance': 6.360136822031199}\n", + "{'image_id': 1916, 'label': 23, 'distance': 8.279651870400942}\n", + "{'image_id': 1975, 'label': 24, 'distance': 9.305370097143731}\n", + "{'image_id': 3287, 'label': 65, 'distance': 9.696792665660324}\n", + "{'image_id': 292, 'label': 1, 'distance': 10.198675122162054}\n", + "{'image_id': 3965, 'label': 90, 'distance': 11.544874878013612}\n", + "{'image_id': 4018, 'label': 92, 'distance': 12.064116415014514}\n", + "{'image_id': 4037, 'label': 92, 'distance': 13.383239007317492}\n", + "{'image_id': 4307, 'label': 99, 'distance': 14.448284626506538}\n" + ] + } + ], + "source": [ + "match selected_latent_space:\n", + "\n", + " case \"\" | \"image_sim\":\n", + " \n", + " extract_similarities_ls1_ls4(selected_latent_space, selected_dim_reduction_method, selected_feature_model, data, image_id)\n", + "\n", + " case \"label_sim\":\n", + "\n", + " extract_similarities_ls3(selected_dim_reduction_method, data, image_id)\n", + "\n", + " case \"cp\":\n", + "\n", + " extract_similarities_ls2(data, image_id)\n" + ] + } + ], + "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": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Phase 2/task_9.ipynb b/Phase 2/task_9.ipynb index c57a926..fe438e2 100644 --- a/Phase 2/task_9.ipynb +++ b/Phase 2/task_9.ipynb @@ -40,7 +40,7 @@ "outputs": [], "source": [ "fd_collection = getCollection(\"team_5_mwdb_phase_2\", \"fd_collection\")\n", - "all_images = fd_collection.find()\n" + "all_images = fd_collection.find()" ] }, { @@ -78,7 +78,7 @@ "\n", "label = int(input(\"Enter label: \"))\n", "if label < 0 and label > 100:\n", - " raise ValueError(\"k should be between 0 and 100\")\n", + " raise ValueError(\"label should be between 0 and 100\")\n", "\n", "knum = int(input(\"Enter value of knum: \"))\n", "if knum < 1:\n", @@ -212,8 +212,8 @@ " if len(S.shape) == 1:\n", " S = np.diag(S)\n", "\n", - " comparison_vector = LS[label]\n", " comparison_feature_space = np.matmul(LS, S)\n", + " comparison_vector = comparison_feature_space[label]\n", "\n", " distances = []\n", "\n", @@ -301,23 +301,8 @@ "\n", " case \"cp\":\n", "\n", - " extract_similarities_ls2(data, label)\n", - " " + " extract_similarities_ls2(data, label)\n" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From 6adc8bcf70eca85d38cf7ecb8fc948ce39841722 Mon Sep 17 00:00:00 2001 From: pranavbrkr Date: Sat, 14 Oct 2023 05:59:55 -0700 Subject: [PATCH 10/10] task 8 --- Phase 2/task_8.ipynb | 342 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 342 insertions(+) create mode 100644 Phase 2/task_8.ipynb diff --git a/Phase 2/task_8.ipynb b/Phase 2/task_8.ipynb new file mode 100644 index 0000000..da19b0b --- /dev/null +++ b/Phase 2/task_8.ipynb @@ -0,0 +1,342 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import os\n", + "import numpy as np\n", + "from utils import *\n", + "import math\n", + "import heapq\n", + "import random" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [], + "source": [ + "fd_collection = getCollection(\"team_5_mwdb_phase_2\", \"fd_collection\")\n", + "all_images = fd_collection.find()" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "image_sim-cm_fd-kmeans-10-semantics.json loaded\n" + ] + } + ], + "source": [ + "selected_latent_space = valid_latent_spaces[\n", + " str(input(\"Enter latent space - one of \" + str(list(valid_latent_spaces.keys()))))\n", + "]\n", + "\n", + "selected_feature_model = valid_feature_models[\n", + " str(input(\"Enter feature model - one of \" + str(list(valid_feature_models.keys()))))\n", + "]\n", + "\n", + "k = int(input(\"Enter value of k: \"))\n", + "if k < 1:\n", + " raise ValueError(\"k should be a positive integer\")\n", + "\n", + "selected_dim_reduction_method = str(\n", + " input(\n", + " \"Enter dimensionality reduction method - one of \"\n", + " + str(list(valid_dim_reduction_methods.keys()))\n", + " )\n", + ")\n", + "\n", + "image_id = int(input(\"Enter image ID: \"))\n", + "if image_id < 0 and image_id > 8676 and image_id % 2 != 0:\n", + " raise ValueError(\"image id should be even number between 0 and 8676\")\n", + "\n", + "img_label = all_images[int(image_id / 2)][\"true_label\"]\n", + "\n", + "knum = int(input(\"Enter value of knum: \"))\n", + "if knum < 1:\n", + " raise ValueError(\"knum should be a positive integer\")\n", + "\n", + "match selected_latent_space:\n", + " case \"\":\n", + " if os.path.exists(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"):\n", + " data = json.load(open(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"))\n", + " print(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json loaded\")\n", + " else:\n", + " print(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json does not exist\")\n", + " case \"cp\":\n", + " if os.path.exists(f\"{selected_feature_model}-cp-{k}-semantics.json\"):\n", + " data = json.load(open(f\"{selected_feature_model}-cp-{k}-semantics.json\"))\n", + " print(f\"{selected_feature_model}-cp-{k}-semantics.json loaded\")\n", + " else: \n", + " print(f\"{selected_feature_model}-cp-{k}-semantics.json does not exist\")\n", + " case _:\n", + " if os.path.exists(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"):\n", + " data = json.load(open(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"))\n", + " print(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json loaded\")\n", + " else:\n", + " print(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json does not exist\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def extract_similarities_ls1_ls4(latent_space, dim_reduction, selected_feature_model, data, image_id):\n", + "\n", + " image_fd = np.array(all_images[int(image_id / 2)][selected_feature_model]).flatten()\n", + "\n", + " match dim_reduction:\n", + "\n", + " case 'svd':\n", + " U = np.array(data[\"image-semantic\"])\n", + " S = np.array(data[\"semantics-core\"])\n", + " if len(S.shape) == 1:\n", + " S = np.diag(S)\n", + " V = np.transpose(np.array(data[\"semantic-feature\"]))\n", + " \n", + " comparison_feature_space = np.matmul(U, S)\n", + "\n", + " if latent_space == \"image_sim\":\n", + " comparison_vector = comparison_feature_space[int(image_id / 2)]\n", + " else:\n", + " comparison_vector = np.matmul(np.matmul(image_fd, V), S)\n", + " \n", + " case \"nmf\":\n", + " H = np.array(data['semantic-feature'])\n", + " comparison_feature_space = np.array(data['image-semantic'])\n", + "\n", + " if latent_space == \"image_sim\":\n", + " comparison_vector = comparison_feature_space[int(image_id / 2)]\n", + " else:\n", + " comparison_vector = np.matmul(image_fd, np.transpose(H))\n", + "\n", + " case \"kmeans\":\n", + " comparison_vector = []\n", + " comparison_feature_space = np.array(data[\"image-semantic\"])\n", + " S = np.array(data[\"semantic-feature\"])\n", + "\n", + " for centroid in S:\n", + " if latent_space == \"image_sim\":\n", + " sim_matrix = np.array(data[\"sim-matrix\"])\n", + " comparison_vector.append(math.dist(sim_matrix[int(image_id / 2)], centroid))\n", + " else:\n", + " comparison_vector.append(math.dist(image_fd, centroid))\n", + "\n", + " n = len(comparison_feature_space)\n", + "\n", + " distances = []\n", + " for i in range(n):\n", + " if (i * 2) != image_id:\n", + " distances.append({\"image_id\": i, \"label\": all_images[i][\"true_label\"], \"distance\": math.dist(comparison_vector, comparison_feature_space[i])})\n", + "\n", + " distances = sorted(distances, key=lambda x: x[\"distance\"], reverse=False)\n", + "\n", + " similar_labels = []\n", + " unique_labels = set()\n", + "\n", + " for img in distances:\n", + " if img['label'] not in unique_labels:\n", + " similar_labels.append(img)\n", + " unique_labels.add(img[\"label\"])\n", + "\n", + " if len(similar_labels) == knum:\n", + " break\n", + "\n", + "\n", + " for x in similar_labels:\n", + " print(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [], + "source": [ + "def extract_similarities_ls2(data, image_id):\n", + "\n", + " IS = np.array(data[\"image-semantic\"])\n", + " S = np.array(data[\"semantics-core\"])\n", + "\n", + " if len(S.shape) == 1:\n", + " S = np.diag(S)\n", + "\n", + " comparison_feature_space = np.matmul(IS, S)\n", + " comparison_vector = comparison_feature_space[int(image_id / 2)]\n", + "\n", + " distances = []\n", + "\n", + " n = len(comparison_feature_space)\n", + " for i in range(n):\n", + " if i != (image_id / 2):\n", + " distances.append({\"image_id\": i * 2, \"label\": all_images[i][\"true_label\"], \"distance\": math.dist(comparison_vector, comparison_feature_space[i])})\n", + " \n", + " distances = sorted(distances, key=lambda x: x[\"distance\"], reverse=False)\n", + "\n", + " similar_labels = []\n", + " unique_labels = set()\n", + "\n", + " for img in distances:\n", + " if img[\"label\"] not in unique_labels and img[\"label\"] != img_label:\n", + " similar_labels.append(img)\n", + " unique_labels.add(img[\"label\"])\n", + "\n", + " if len(similar_labels) == knum:\n", + " break\n", + "\n", + "\n", + " for x in similar_labels:\n", + " print(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": {}, + "outputs": [], + "source": [ + "def extract_similarities_ls3(dim_reduction, data, image_id):\n", + "\n", + " img_label = all_images[int(image_id / 2)][\"true_label\"]\n", + "\n", + " match dim_reduction:\n", + "\n", + " case 'svd':\n", + " U = np.array(data[\"image-semantic\"])\n", + " S = np.array(data[\"semantics-core\"])\n", + " V = np.transpose(np.array(data[\"semantic-feature\"]))\n", + "\n", + " comparison_feature_space = np.matmul(U, S)\n", + " comparison_vector = comparison_feature_space[img_label]\n", + " \n", + " case \"nmf\":\n", + " comparison_feature_space = np.array(data['image-semantic'])\n", + " comparison_vector = comparison_feature_space[img_label]\n", + "\n", + " case \"kmeans\":\n", + " comparison_feature_space = np.array(data[\"image-semantic\"])\n", + " comparison_vector = comparison_feature_space[img_label]\n", + "\n", + " n = len(comparison_feature_space)\n", + " distance = float('inf')\n", + " most_similar_label = img_label\n", + " distances = []\n", + " for i in range(n):\n", + " if i != img_label:\n", + " distances.append({\"label\": i, \"distance\": math.dist(comparison_vector, comparison_feature_space[i])})\n", + " # temp_distance = math.dist(comparison_vector, comparison_feature_space[i])\n", + " # if distance > temp_distance:\n", + " # distance = temp_distance\n", + " # most_similar_label = i\n", + "\n", + " # label_images = [x[\"image_id\"] for x in all_images if x[\"true_label\"] == most_similar_label]\n", + " # similar_images = random.sample(label_images, knum)\n", + "\n", + " # print(f\"Most similar label to {img_label} is {most_similar_label}\")\n", + " distances = sorted(distances, key=lambda x: x[\"distance\"], reverse=False)[:knum]\n", + "\n", + " for img in distances:\n", + " print(img)" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'image_id': 2457, 'label': 39, 'distance': 5.400083378408386}\n", + "{'image_id': 2629, 'label': 46, 'distance': 6.360136822031199}\n", + "{'image_id': 1916, 'label': 23, 'distance': 8.279651870400942}\n", + "{'image_id': 1975, 'label': 24, 'distance': 9.305370097143731}\n", + "{'image_id': 3287, 'label': 65, 'distance': 9.696792665660324}\n", + "{'image_id': 292, 'label': 1, 'distance': 10.198675122162054}\n", + "{'image_id': 3965, 'label': 90, 'distance': 11.544874878013612}\n", + "{'image_id': 4018, 'label': 92, 'distance': 12.064116415014514}\n", + "{'image_id': 4307, 'label': 99, 'distance': 14.448284626506538}\n", + "{'image_id': 2329, 'label': 35, 'distance': 14.742475318290913}\n" + ] + } + ], + "source": [ + "match selected_latent_space:\n", + "\n", + " case \"\" | \"image_sim\":\n", + " \n", + " extract_similarities_ls1_ls4(selected_latent_space, selected_dim_reduction_method, selected_feature_model, data, image_id)\n", + "\n", + " case \"label_sim\":\n", + "\n", + " extract_similarities_ls3(selected_dim_reduction_method, data, image_id)\n", + "\n", + " case \"cp\":\n", + "\n", + " extract_similarities_ls2(data, image_id)\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": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}