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CSE515_MWDB_Project/Phase 2/task_5.ipynb
pranavbrkr c135fdc390 task9 init
2023-10-12 16:41:06 -07:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 12,
"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": 13,
"metadata": {},
"outputs": [],
"source": [
"from utils import *\n",
"warnings.filterwarnings('ignore')\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"fd_collection = getCollection(\"team_5_mwdb_phase_2\", \"fd_collection\")\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"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_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\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\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\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",
"Image_ID\t176\t-\tWeight\t0.17418620419170064\n",
"Image_ID\t184\t-\tWeight\t0.16284491366511475\n",
"Image_ID\t178\t-\tWeight\t0.15835141260945226\n",
"Image_ID\t182\t-\tWeight\t0.1563230190106094\n",
"Image_ID\t180\t-\tWeight\t0.14992527858819726\n",
"Image_ID\t170\t-\tWeight\t0.1461798073190985\n",
"Image_ID\t174\t-\tWeight\t0.13541698801645058\n",
"Image_ID\t166\t-\tWeight\t0.12423630035289784\n",
"Image_ID\t172\t-\tWeight\t0.1234361443074221\n",
"Image_ID\t52\t-\tWeight\t0.12074682250121946\n",
"Latent semantic no. 5\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",
"Image_ID\t160\t-\tWeight\t0.2664558477429268\n",
"Image_ID\t86\t-\tWeight\t0.22964178511691158\n",
"Image_ID\t4\t-\tWeight\t0.2027946708731003\n",
"Image_ID\t8\t-\tWeight\t0.17594388183949075\n",
"Image_ID\t96\t-\tWeight\t0.15932731178540344\n",
"Image_ID\t150\t-\tWeight\t0.1557669882841681\n",
"Image_ID\t42\t-\tWeight\t0.15015687757605228\n",
"Image_ID\t70\t-\tWeight\t0.14221366935133106\n",
"Image_ID\t166\t-\tWeight\t0.13822990110337333\n",
"Image_ID\t170\t-\tWeight\t0.136006921209686\n",
"Latent semantic no. 7\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\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\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"
]
}
],
"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": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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