comparison.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d854588c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import algorithms\n",
    "import treespace\n",
    "import logging \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "from utils import calculate_l, generate_requests"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cdfa0d5",
   "metadata": {},
   "source": [
    "## Plotting Requests Distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f518f14",
   "metadata": {},
   "source": [
    "### Level-wise zipf distribution\n",
    "In this distribution, the nodes in the same tree level have the same probability of being requested, and this probability is based on a zipf distribution for the level of the nodes from bottom to top of the tree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "67bf730b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 2**10\n",
    "size = 1000000\n",
    "perm = None\n",
    "reqs = np.array(generate_requests(n = 10, perm=perm, a=4, size=size, dist=\"zipf\", rand_type=\"level\"))\n",
    "plt.figure(figsize=(15,8))\n",
    "plt.hist(reqs, bins=np.arange(0.5,n,1), density=True)\n",
    "plt.grid()\n",
    "#plt.title(\"Distribution of Requested Indices\")\n",
    "plt.xlabel(\"Index\", fontsize=30)\n",
    "plt.ylabel(\"Probability\", fontsize=30)\n",
    "ax = plt.gca()\n",
    "ax.tick_params(axis='both', which='major', labelsize=30)\n",
    "ax.tick_params(axis='both', which='minor', labelsize=16)\n",
    "ax.yaxis.labelpad = 30\n",
    "ax.xaxis.labelpad = 30\n",
    "plt.savefig(\"results/request_distribution_zipf_no_perm.png\", dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61f17975",
   "metadata": {},
   "source": [
    "### Index-wise exponential distribution\n",
    "In this distribution, the nodes have a geometric probability based on their node index from bottom to top. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5084ac86",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 2**10\n",
    "size = 1000000\n",
    "perm = None\n",
    "reqs = np.array(generate_requests(n = n, perm=perm, a=10/n, size=size, dist=\"geo\", rand_type=\"index\"))\n",
    "plt.figure(figsize=(15,8))\n",
    "plt.hist(reqs, bins=np.arange(0.5,n,1), density=True)\n",
    "plt.grid()\n",
    "plt.xlabel(\"Index\", fontsize=30)\n",
    "plt.ylabel(\"Probability\", fontsize=30)\n",
    "ax = plt.gca()\n",
    "ax.tick_params(axis='both', which='major', labelsize=30)\n",
    "ax.tick_params(axis='both', which='minor', labelsize=16)\n",
    "ax.yaxis.labelpad = 30\n",
    "ax.xaxis.labelpad = 30\n",
    "plt.savefig(\"results/request_distribution_geo_no_perm.png\", dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de37e822",
   "metadata": {},
   "source": [
    "## Running the algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5336af7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# setting the log level\n",
    "# set the log level to log all the requests\n",
    "# set the log level to debug for a more detailed description of each operation\n",
    "logging.basicConfig(filename='logs.txt', format='%(levelname)s:%(message)s', level=logging.WARNING)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b86ebab",
   "metadata": {},
   "source": [
    "We first need to run the algorithm for different values of cache size `k`, given an specific number of `n`, `repeats`, and `a` which could be easily tweaked.\n",
    "\n",
    "In order to generate the results for the zipf distribution, the parameters should be specified as follows:\n",
    "- `n = 10`\n",
    "- `a = 4`\n",
    "- `dist = \"zipf\"`\n",
    "- `perm = None`\n",
    "- `rand_type = \"level`\n",
    "\n",
    "In order to generate the results for the exponential distribution, the parameters should be specified as follows:\n",
    "- `n = 10`\n",
    "- `a = 10/2**10`\n",
    "- `dist = \"geo\"`\n",
    "- `perm = None`\n",
    "- `rand_type = \"index\"`\n",
    "\n",
    "You can automatically choose these values by setting the `EXPERIMENT` parameter to `zipf` or `geo`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1edde8c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# choose the EXPERIMENT between 'zipf' and 'geo'\n",
    "EXPERIMENT = \"geo\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4998a594",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-- repeat =0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/508 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 508/508 [01:58<00:00,  4.30it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-- repeat =1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 508/508 [01:48<00:00,  4.68it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-- repeat =2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 508/508 [01:39<00:00,  5.11it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-- repeat =3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 508/508 [02:16<00:00,  3.73it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-- repeat =4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 508/508 [02:11<00:00,  3.86it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-- repeat =5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 508/508 [02:12<00:00,  3.82it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-- repeat =6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 508/508 [03:11<00:00,  2.66it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-- repeat =7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 508/508 [02:39<00:00,  3.18it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-- repeat =8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 508/508 [02:30<00:00,  3.37it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-- repeat =9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 508/508 [02:06<00:00,  4.01it/s]\n"
     ]
    }
   ],
   "source": [
    "results_df = pd.DataFrame(columns = [\"n\", \"k\", \"len\", \"alg\", \"tree_caching\"])\n",
    "\n",
    "# setting the parameters\n",
    "repeats = 10\n",
    "size = 5000\n",
    "n = 10\n",
    "perm = None\n",
    "if EXPERIMENT == \"zipf\":\n",
    "    a = 4\n",
    "    dist = \"zipf\"\n",
    "    rand_type = \"level\"\n",
    "    \n",
    "elif EXPERIMENT == \"geo\":\n",
    "    a = n / (2 ** n)\n",
    "    dist = \"geo\"\n",
    "    rand_type = \"index\"\n",
    "\n",
    "n_dist = n if rand_type == \"level\" else 2**n\n",
    "\n",
    "tree = treespace.BinaryTree(n)\n",
    "tree_caching = algorithms.TreeCaching(tree)\n",
    "for repeat in range(repeats):\n",
    "    print(f\"-- repeat ={repeat}\") \n",
    "    requests_idx = list(generate_requests(n=n_dist, perm=perm, a=a, size=size, dist=dist, rand_type=rand_type))\n",
    "    req_len = len(requests_idx)\n",
    "    requests = list(map(lambda x: tree.nodes[x - 1], requests_idx))\n",
    "    \n",
    "    for k in tqdm(range(4, 2**(n-1), 1)):\n",
    "        alg = algorithms.RandomBucket(tree)\n",
    "        alg_cost = alg.serve_requests(requests, k, calculate_l(n), 1)\n",
    "        tree_c_cost = tree_caching.serve_requests(requests, k, 1)\n",
    "        \n",
    "        # Uncomment the following for printing the costs\n",
    "        # print(\"---- k =\", k)\n",
    "        # print(f\"-------- Request Len = {req_len}\")\n",
    "        # print(f\"-------- Alg Cost = {alg_cost}\")\n",
    "        # print(f\"-------- Tree C Cost = {tree_c_cost}\")\n",
    "        \n",
    "        temp_df = pd.DataFrame([[n,k,req_len,alg_cost,tree_c_cost]], columns=['n','k','len','alg','tree_caching'])\n",
    "        results_df = pd.concat([results_df, temp_df], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b16131b",
   "metadata": {},
   "source": [
    "Calculating the average cost of each algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "34f940cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_df['alg_ratio'] = results_df['alg']/results_df['len']\n",
    "results_df['tc_ratio'] = results_df['tree_caching']/results_df['len']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "49dd13ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_arr = results_df[['n','k','alg_ratio','tc_ratio']].values.copy().astype(\"float\")\n",
    "res_smooth = np.array([[]]).reshape((0,4))\n",
    "for n in range(10,11):\n",
    "    for k in range(4, 2**(n-1), 1):\n",
    "        res = np.mean(results_arr[(results_arr[:,0] == n) & (results_arr[:,1] == k)], axis=0).reshape((1,4))\n",
    "        res_smooth = np.append(res_smooth, res, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b6f8952",
   "metadata": {},
   "source": [
    "## Saving the results\n",
    "The results of different experiments are saved in the directory `data`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4c220753",
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 10\n",
    "data = res_smooth[res_smooth[:, 0]==n][:, [1,2,3]]\n",
    "\n",
    "if EXPERIMENT == \"zipf\":\n",
    "    with open('data/zipf_no_perm_levelwise_data.npy', 'wb') as f:\n",
    "        np.save(f, data)\n",
    "\n",
    "elif EXPERIMENT == \"geo\":\n",
    "    with open('data/geo_no_perm_data.npy', 'wb') as f:\n",
    "        np.save(f, data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe5222db",
   "metadata": {},
   "source": [
    "## Plotting\n",
    "### Loading the data\n",
    "In cases when you already have the data you can start fro here to just load and plot the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6cbd19bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "if EXPERIMENT == \"zipf\":\n",
    "    with open('data/zipf_no_perm_levelwise_data.npy', 'rb') as f:\n",
    "        data = np.load(f)\n",
    "\n",
    "elif EXPERIMENT == \"geo\":\n",
    "    with open('data/geo_no_perm_data.npy', 'rb') as f:\n",
    "        data = np.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14ea7378",
   "metadata": {},
   "source": [
    "### Plotting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "353986dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# n = 10\n",
    "n = 10\n",
    "plt.figure(figsize=(18,6))\n",
    "plt.plot(data[data[:,0]>20,0], data[data[:,0]>20,1], 'r', linewidth=4)\n",
    "plt.plot(data[data[:,0]>20,0], data[data[:,0]>20,2], 'b', linewidth=4)\n",
    "plt.xlabel(f\"Cache Size ($k$)\", fontsize=25)\n",
    "plt.ylabel(\"Cost/(Sequence Length)\", fontsize=25)\n",
    "plt.grid()\n",
    "ax = plt.gca()\n",
    "ax.tick_params(axis='both', which='major', labelsize=25)\n",
    "ax.tick_params(axis='both', which='minor', labelsize=16)\n",
    "ax.yaxis.labelpad = 30\n",
    "ax.xaxis.labelpad = 30\n",
    "plt.legend([\"BucketingBypass\", \"TreeCaching\"], fontsize=25)\n",
    "\n",
    "if EXPERIMENT == \"zipf\":\n",
    "    filename = f\"results/cost_cache_n={n}_zipf_no_perm_levelwise.png\"\n",
    "elif EXPERIMENT == \"geo\":\n",
    "    filename = f\"results/cost_cache_n={n}_geo_no_perm.png\"\n",
    "\n",
    "plt.savefig(filename, format='png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}