{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils import *\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "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 CP decomposition on the resnet_fd space to get 5 latent semantics (showing only top 10 image-weight pairs for each latent semantic)...\n",
      "(4339, 1000, 101)\n",
      "Showing image-weight latent semantic\n",
      "Latent semantic no. 0\n",
      "image\t16\t-\tweight\t0.09457863290867145\n",
      "image\t33\t-\tweight\t0.09431358165046931\n",
      "image\t8\t-\tweight\t0.09383106409263509\n",
      "image\t31\t-\tweight\t0.0909314099940096\n",
      "image\t77\t-\tweight\t0.08944321537104273\n",
      "image\t60\t-\tweight\t0.08919523673591125\n",
      "image\t117\t-\tweight\t0.08863817716328135\n",
      "image\t190\t-\tweight\t0.08742826964165426\n",
      "image\t61\t-\tweight\t0.08740880468847693\n",
      "image\t4\t-\tweight\t0.08658248985130698\n",
      "Latent semantic no. 1\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. 2\n",
      "image\t901\t-\tweight\t0.06704958761044195\n",
      "image\t821\t-\tweight\t0.06679275183308425\n",
      "image\t560\t-\tweight\t0.0664275386647296\n",
      "image\t617\t-\tweight\t0.0662151403988761\n",
      "image\t797\t-\tweight\t0.06443320486788845\n",
      "image\t899\t-\tweight\t0.06434223110660761\n",
      "image\t892\t-\tweight\t0.06428385401777054\n",
      "image\t893\t-\tweight\t0.064267819006683\n",
      "image\t553\t-\tweight\t0.06396933419584953\n",
      "image\t688\t-\tweight\t0.06322044784443977\n",
      "Latent semantic no. 3\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. 4\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\t0.011984002180022709\n",
      "Latent semantic no. 1\n",
      "feature\t0\t-\tweight\t-0.015847730845197867\n",
      "Latent semantic no. 2\n",
      "feature\t0\t-\tweight\t0.003755764372246337\n",
      "Latent semantic no. 3\n",
      "feature\t0\t-\tweight\t-0.015820605365729715\n",
      "Latent semantic no. 4\n",
      "feature\t0\t-\tweight\t0.006783847616503207\n",
      "Showing label-weight latent semantic\n",
      "Latent semantic no. 0\n",
      "label\t0\t-\tweight\t0.9999999999999998\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. 1\n",
      "label\t1\t-\tweight\t1.0000000000000004\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. 2\n",
      "label\t3\t-\tweight\t1.0000000000000009\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. 3\n",
      "label\t5\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\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\t1.0000000000000004\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",
    "]\n",
    "\n",
    "k = int(input(\"Enter value of k: \"))\n",
    "if k < 1:\n",
    "    raise ValueError(\"k should be a positive integer\")\n",
    "\n",
    "extract_CP_semantics_from_feature_model(\n",
    "    fd_collection,\n",
    "    k,\n",
    "    selected_feature_model,\n",
    "    top_images=10\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
}