{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ab5cd7d1-1a57-46fc-8282-dae0a6cc2944",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import random\n",
    "import pandas as pd\n",
    "import itertools\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3d1ad0b9-f6a8-4e98-84aa-6e02e4279954",
   "metadata": {},
   "outputs": [],
   "source": [
    "SEED = 42\n",
    "np.random.seed(SEED)\n",
    "random.seed(SEED)\n",
    "\n",
    "ZIPF_CONSTANT = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5a27d416-8f98-4814-af9e-6c6bef95f4ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "def eta_star(db_object_count, c_f, cache_sz, c_delta, lambda_vals):\n",
    "    num = (db_object_count * c_f - cache_sz * c_delta)\n",
    "    denom = np.sum(1.0/lambda_vals)\n",
    "    if denom == 0:\n",
    "        print(\"sum(1.0/lambda_vals) == 0\")\n",
    "        print(db_object_count, c_f, cache_sz, c_delta, lambda_vals)\n",
    "    return max(0, num/denom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6276a9ce-f839-4fe6-90f2-2195cf065fc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def h_i_star(c_f, eta, lambda_vals, c_delta):\n",
    "    optimized_hitrate = (c_f - (eta/lambda_vals)) / c_delta\n",
    "    return optimized_hitrate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dcd31a8c-6864-4b9a-8bb3-998f0c32baf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_index_of_furthest_hitrate_from_boundary(hitrates):\n",
    "    lower_bound_violation =  hitrates[(hitrates < 0)]\n",
    "    upper_bound_violation = hitrates[(hitrates > 1)]\n",
    "    smallest_delta = np.abs(np.min(lower_bound_violation))\n",
    "    biggest_delta = np.max(upper_bound_violation) - 1\n",
    "    if smallest_delta > biggest_delta:\n",
    "        print(smallest_delta)\n",
    "        index = np.where(hitrates == np.min(local_hitrates))[0][0]\n",
    "        return index\n",
    "    else:\n",
    "        \n",
    "        index = np.where(hitrates == np.max(local_hitrates))[0][0]\n",
    "        return index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9d774304-ae68-43b3-a76a-e970c06c5236",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_index_of_furthest_hitrate_from_boundary(hitrates):\n",
    "    outside_bounds = (hitrates < 0) | (hitrates > 1)\n",
    "    distances = np.where(outside_bounds, np.maximum(np.abs(hitrates - 0), np.abs(hitrates - 1)), -np.inf)\n",
    "    index = np.argmax(distances)\n",
    "    return index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0e21c26f-058a-4e56-a5ad-1c47bf28656c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def optimize_hitrates(db_object_count, cache_size, c_f, c_delta, lambda_vals):\n",
    "    optimized_hitrates = np.zeros(db_object_count)\n",
    "    current_db_object_count = db_object_count\n",
    "    current_cache_size = cache_size\n",
    "    \n",
    "    differenc_set = np.arange(db_object_count)\n",
    "    fix_i = []\n",
    "    while True:\n",
    "        if current_db_object_count == 0:\n",
    "            if current_cache_size > 0:\n",
    "                # print(\"Re-optimize objects with optimal hitrate of 0.\")\n",
    "                differenc_set = np.where(optimized_hitrates == 0)[0]\n",
    "                fix_i = np.setdiff1d(np.arange(db_object_count), differenc_set).tolist()\n",
    "                current_db_object_count = len(differenc_set)\n",
    "                continue\n",
    "            else:\n",
    "                # print(\"Stop optimization.\")\n",
    "                optimized_hitrates[differenc_set] = 0\n",
    "                break\n",
    "        \n",
    "        eta = eta_star(current_db_object_count, c_f, current_cache_size, c_delta, lambda_vals[differenc_set])\n",
    "        optimized_hitrates[differenc_set] = h_i_star(c_f, eta, lambda_vals[differenc_set], c_delta)\n",
    "\n",
    "        if eta < 0:\n",
    "            # print(\"eta was negative.\")\n",
    "            current_cache_size = current_db_object_count * c_f / c_delta  # Adjust cache size for next iteration\n",
    "            continue\n",
    "        \n",
    "        if len((optimized_hitrates[differenc_set])[((optimized_hitrates[differenc_set]) < 0) | ((optimized_hitrates[differenc_set])> 1)]) == 0:\n",
    "            # print(\"All values optimized.\")\n",
    "            break\n",
    "        \n",
    "        max_outbound_index = get_index_of_furthest_hitrate_from_boundary(optimized_hitrates)\n",
    "        fix_i.append(max_outbound_index)\n",
    "        differenc_set = np.setdiff1d(np.arange(db_object_count), fix_i)\n",
    "    \n",
    "        old_hitrate = optimized_hitrates[max_outbound_index]\n",
    "        optimized_hitrates[max_outbound_index] = (1 if optimized_hitrates[max_outbound_index] > 1 else 0)\n",
    "        \n",
    "        current_db_object_count -= 1\n",
    "        current_cache_size -= optimized_hitrates[max_outbound_index]\n",
    "    return optimized_hitrates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b6bf3329-3a63-4807-ab8b-8a54f824f47e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def objective_function(optimized_hitrates, c_f, c_delta, lambda_vals):\n",
    "    return np.sum(lambda_vals*(1-optimized_hitrates)*c_f+0.5*np.power(optimized_hitrates,2)*c_delta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7a998837-72b8-4039-95a5-ca8d9c8e65ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform grid search\n",
    "def grid_search(db_object_counts, cache_sizes, c_f_values, c_delta_values):\n",
    "    best_objective = float('inf')\n",
    "    best_params = None\n",
    "\n",
    "    # Iterate through all combinations of parameters\n",
    "    for db_object_count, cache_size, c_f, c_delta in tqdm(itertools.product(db_object_counts, cache_sizes, c_f_values, c_delta_values), total=len(db_object_counts) * len(cache_sizes) * len(c_f_values) * len(c_delta_values), desc=\"Grid Search Progress\"):\n",
    "        if db_object_count < cache_size:\n",
    "            continue\n",
    "        lambda_vals = np.array([np.random.zipf(ZIPF_CONSTANT) for i in np.arange(1, db_object_count + 1,1)])\n",
    "        # print(db_object_count, cache_size, c_f, c_delta)\n",
    "        # Call the optimization function\n",
    "        optimized_hitrates = optimize_hitrates(db_object_count, cache_size, c_f, c_delta, lambda_vals)\n",
    "\n",
    "        # Compute the objective function\n",
    "        objective = objective_function(optimized_hitrates, c_f, c_delta, lambda_vals)\n",
    "        \n",
    "        # Track the best (minimum) objective and corresponding parameters\n",
    "        if objective < best_objective:\n",
    "            best_objective = objective\n",
    "            best_params = (db_object_count, cache_size, c_f, c_delta)\n",
    "\n",
    "    return best_objective, best_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a271b52d-1f24-4670-ae3f-af5dd9096a2f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Grid Search Progress: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 64152/64152 [12:27<00:00, 85.87it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 12min 16s, sys: 11.5 s, total: 12min 28s\n",
      "Wall time: 12min 27s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Define the grid search space\n",
    "test_ratios = np.array([0.1, 0.2, 0.5, 0.7, 1, 1.5, 2, 5, 10])\n",
    "db_object_count_values = np.round(np.array([10, 15, 30, 100, 200, 500]))\n",
    "cache_size_values = np.unique(np.round(np.array([db_object_count_values * i for i in test_ratios]).flatten()))\n",
    "c_f_values = np.array([0.1, 0.2, 0.5, 0.7, 1, 1.5, 2, 5, 10])\n",
    "c_delta_values = np.unique(np.array([c_f_values * i for i in test_ratios]).flatten())\n",
    "\n",
    "best_objective, best_params = grid_search(db_object_count_values, cache_size_values, c_f_values, c_delta_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b2f625d0-ebe0-4a5d-92ff-7de03942ef51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.05000000000000002, (10, 10.0, 1.5, 0.010000000000000002))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_objective, best_params "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86a23d02-6f14-4d4d-ad8a-39084ea69151",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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