{
 "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": []
  }
 ],
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    "version": 3
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   "file_extension": ".py",
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