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CSE515_MWDB_Project/Phase 2/task_5.ipynb

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{
"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": [
"from utils import *\n",
"warnings.filterwarnings('ignore')\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"fd_collection = getCollection(\"team_5_mwdb_phase_2\", \"fd_collection\")\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"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",
"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",
"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",
"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",
"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",
"Latent semantic no. 4\n",
"Image_ID\t0\t-\tWeight\t0.1851068625752792\n",
"Image_ID\t68\t-\tWeight\t0.18233577289211206\n",
"Image_ID\t70\t-\tWeight\t0.17658848660973384\n",
"Image_ID\t2\t-\tWeight\t0.1740864069632969\n",
"Image_ID\t64\t-\tWeight\t0.1652208125636303\n",
"Image_ID\t144\t-\tWeight\t0.1473307832877541\n",
"Image_ID\t140\t-\tWeight\t0.13555748295430797\n",
"Image_ID\t142\t-\tWeight\t0.12823249250147356\n",
"Image_ID\t86\t-\tWeight\t0.12718092599165637\n",
"Image_ID\t76\t-\tWeight\t0.1252879989162334\n",
"Latent semantic no. 5\n",
"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",
"Latent semantic no. 6\n",
"Image_ID\t114\t-\tWeight\t0.15664448468019831\n",
"Image_ID\t2\t-\tWeight\t0.15491061836983144\n",
"Image_ID\t0\t-\tWeight\t0.1530303208538504\n",
"Image_ID\t6\t-\tWeight\t0.15295162665264536\n",
"Image_ID\t106\t-\tWeight\t0.14505207452002586\n",
"Image_ID\t110\t-\tWeight\t0.14364619871330633\n",
"Image_ID\t104\t-\tWeight\t0.14360445482307752\n",
"Image_ID\t116\t-\tWeight\t0.14309751290704328\n",
"Image_ID\t108\t-\tWeight\t0.14103122187663494\n",
"Image_ID\t112\t-\tWeight\t0.13936814882577545\n",
"Latent semantic no. 7\n",
"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",
"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",
"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"
]
}
],
"source": [
"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_sim_matrix = find_label_label_similarity(fd_collection,selected_feature_model)\n",
"\n",
"extract_latent_semantics(\n",
" fd_collection,\n",
" k,\n",
" selected_feature_model,\n",
" selected_dim_reduction_method,\n",
" sim_matrix=label_sim_matrix,\n",
" top_images=10,\n",
" fn_prefix='label_sim-'\n",
")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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