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CSE515_MWDB_Project/Phase 2/task_3.ipynb
pranavbrkr d8490c7c4b start kmeans task 9
2023-10-13 11:27:59 -07:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from utils import *\n",
"warnings.filterwarnings('ignore')\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"fd_collection = getCollection(\"team_5_mwdb_phase_2\", \"fd_collection\")\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"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",
"Note: for K-Means we display distances, in ascending order\n",
"Latent semantic no. 0\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\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\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\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\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\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\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\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\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\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"
]
}
],
"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",
"extract_latent_semantics_from_feature_model(\n",
" fd_collection,\n",
" k,\n",
" selected_feature_model,\n",
" selected_dim_reduction_method,\n",
" top_images=10,\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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