comparative_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from Bio.SeqUtils import Seq\n",
    "from time import time\n",
    "\n",
    "from utils import generate_random_sequence, plot_times\n",
    "\n",
    "from effidict import LRUDBDict, LRUDict, DBDict\n",
    "\n",
    "from collections import OrderedDict, namedtuple\n",
    "\n",
    "from shutil import rmtree\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Oligo = namedtuple(\n",
    "    \"Oligo\",\n",
    "    [\n",
    "        \"sequence\",\n",
    "        \"chromosome\",\n",
    "        \"start\",\n",
    "        \"end\",\n",
    "        \"strand\",\n",
    "        \"length\",\n",
    "        \"additional_information_fasta\",\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "MAX_IN_MEMORY = 1000\n",
    "BATCH_SIZE = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Original implementation\n",
    "def create_oligo_dict(n_regions, n_oligos):\n",
    "    t = time()\n",
    "    oligo_dict = {}\n",
    "    for region in range(1, n_regions + 1):\n",
    "        region_key = f'region_{region}'\n",
    "        oligo_dict[region_key] = {}\n",
    "\n",
    "        for oligo in range(1, n_oligos + 1):\n",
    "            oligo_key = f'{region_key}::oligo_{oligo}'\n",
    "            sequence = generate_random_sequence(25)\n",
    "\n",
    "            oligo_dict[region_key][oligo_key] = {\n",
    "                'sequence': Seq(sequence),\n",
    "                'chromosome': 1,\n",
    "                'start': [33],\n",
    "                'end': [33],\n",
    "                'strand': '-',\n",
    "                'length': 25,\n",
    "                'additional_information_fasta': ['vfdcsxz']\n",
    "            }\n",
    "    exec_time = time() - t\n",
    "\n",
    "    return oligo_dict, exec_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# With pickle\n",
    "def create_oligo_lru_dict(n_regions, n_oligos, max_in_memory=MAX_IN_MEMORY):\n",
    "    t = time()\n",
    "    oligo_dict = LRUDict(max_in_memory=max_in_memory)\n",
    "\n",
    "    for region in range(1, n_regions + 1):\n",
    "        region_key = f'region_{region}'\n",
    "        oligo_dict[region_key] = {}\n",
    "\n",
    "        for oligo in range(1, n_oligos + 1):\n",
    "            oligo_key = f'{region_key}::oligo_{oligo}'\n",
    "            sequence = generate_random_sequence(25)\n",
    "\n",
    "            oligo_dict[region_key][oligo_key] = {\n",
    "                'sequence': Seq(sequence),\n",
    "                'chromosome': 1,\n",
    "                'start': (33,),\n",
    "                'end': (33,),\n",
    "                'strand': '-',\n",
    "                'length': 25,\n",
    "                'additional_information_fasta': ['vfdcsxz']\n",
    "            }\n",
    "    exec_time = time() - t\n",
    "\n",
    "    return oligo_dict, exec_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# With Sqlite3 and memory\n",
    "def create_oligo_lru_db_dict(n_regions, n_oligos, max_in_memory=MAX_IN_MEMORY, batch_size=BATCH_SIZE, storage_path=\"cache.db\"):\n",
    "    t = time()\n",
    "    oligo_dict = LRUDBDict(max_in_memory=max_in_memory, storage_path=storage_path, batch_size=batch_size)\n",
    "\n",
    "    for region in range(1, n_regions + 1):\n",
    "        region_key = f'region_{region}'\n",
    "        oligo_dict[region_key] = {}\n",
    "\n",
    "        for oligo in range(1, n_oligos + 1):\n",
    "            oligo_key = f'{region_key}::oligo_{oligo}'\n",
    "            sequence = generate_random_sequence(25)\n",
    "\n",
    "            oligo_dict[region_key][oligo_key] = {\n",
    "                'sequence': sequence,\n",
    "                'chromosome': 1,\n",
    "                'start': (33,),\n",
    "                'end': (33,),\n",
    "                'strand': '-',\n",
    "                'length': 25,\n",
    "                'additional_information_fasta': ['vfdcsxz']\n",
    "            }\n",
    "    exec_time = time() - t\n",
    "\n",
    "    return oligo_dict, exec_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# With Sqlite3 only\n",
    "def create_oligo_db_dict(n_regions, n_oligos, storage_path=\"cache.db\"):\n",
    "    t = time()\n",
    "    oligo_dict = DBDict(storage_path=storage_path)\n",
    "\n",
    "    for region in range(1, n_regions + 1):\n",
    "        region_key = f'region_{region}'\n",
    "        oligo_dict[region_key] = {}\n",
    "\n",
    "        for oligo in range(1, n_oligos + 1):\n",
    "            oligo_key = f'{region_key}::oligo_{oligo}'\n",
    "            sequence = generate_random_sequence(25)\n",
    "            oligo_dict[region_key][oligo_key] = {\n",
    "                'sequence': Seq(sequence),\n",
    "                'chromosome': 1,\n",
    "                'start': (33,),\n",
    "                'end': (33,),\n",
    "                'strand': '-',\n",
    "                'length': 25,\n",
    "                'additional_information_fasta': ['vfdcsxz']\n",
    "            }\n",
    "    exec_time = time() - t\n",
    "\n",
    "    return oligo_dict, exec_time\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# With namedtuple\n",
    "def create_oligo_dict_named_tuple(n_regions, n_oligos):\n",
    "    t = time()\n",
    "    oligo_dict = {}\n",
    "\n",
    "    for region in range(1, n_regions + 1):\n",
    "        region_key = f'region_{region}'\n",
    "        oligo_dict[region_key] = {}\n",
    "\n",
    "        for oligo in range(1, n_oligos + 1):\n",
    "            oligo_key = f'{region_key}::oligo_{oligo}'\n",
    "            sequence = generate_random_sequence(25)\n",
    "            oligo = Oligo(sequence=Seq(sequence), \n",
    "                  chromosome=1, \n",
    "                  start=(33,), \n",
    "                  end=(33,), \n",
    "                  strand='-', \n",
    "                  length=25, \n",
    "                  additional_information_fasta=['vfdcsxz'])\n",
    "\n",
    "            oligo_dict[region_key][oligo_key] = oligo\n",
    "    exec_time = time() - t\n",
    "\n",
    "    return oligo_dict, exec_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------\n",
      "Now testing with: dict\n",
      "n_regions: 0\n",
      "n_regions: 1000\n",
      "n_regions: 2000\n",
      "n_regions: 3000\n",
      "n_regions: 4000\n",
      "n_regions: 5000\n",
      "n_regions: 6000\n",
      "n_regions: 7000\n",
      "n_regions: 8000\n",
      "n_regions: 9000\n",
      "n_regions: 10000\n",
      "----------------------------------------------------\n",
      "Now testing with: lru_dict\n",
      "n_regions: 0\n",
      "n_regions: 1000\n",
      "n_regions: 2000\n",
      "n_regions: 3000\n",
      "n_regions: 4000\n",
      "n_regions: 5000\n",
      "n_regions: 6000\n",
      "n_regions: 7000\n",
      "n_regions: 8000\n",
      "n_regions: 9000\n",
      "n_regions: 10000\n",
      "----------------------------------------------------\n",
      "Now testing with: lru_db_dict\n",
      "n_regions: 0\n",
      "n_regions: 1000\n",
      "n_regions: 2000\n",
      "n_regions: 3000\n",
      "n_regions: 4000\n",
      "n_regions: 5000\n",
      "n_regions: 6000\n",
      "n_regions: 7000\n",
      "n_regions: 8000\n",
      "n_regions: 9000\n",
      "n_regions: 10000\n",
      "----------------------------------------------------\n",
      "Now testing with: db_dict\n",
      "n_regions: 0\n",
      "n_regions: 1000\n",
      "n_regions: 2000\n",
      "n_regions: 3000\n",
      "n_regions: 4000\n",
      "n_regions: 5000\n",
      "n_regions: 6000\n",
      "n_regions: 7000\n",
      "n_regions: 8000\n",
      "n_regions: 9000\n",
      "n_regions: 10000\n",
      "----------------------------------------------------\n",
      "Now testing with: dict_named_tuple\n",
      "n_regions: 0\n",
      "n_regions: 1000\n",
      "n_regions: 2000\n",
      "n_regions: 3000\n",
      "n_regions: 4000\n",
      "n_regions: 5000\n",
      "n_regions: 6000\n",
      "n_regions: 7000\n",
      "n_regions: 8000\n",
      "n_regions: 9000\n",
      "n_regions: 10000\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_regions_values = range(0, 10001, 1000)\n",
    "n_oligos_per_regions = 1000\n",
    "\n",
    "all_times = []\n",
    "methods = [create_oligo_dict, \n",
    "           create_oligo_lru_dict, \n",
    "           create_oligo_lru_db_dict, \n",
    "           create_oligo_db_dict, \n",
    "           create_oligo_dict_named_tuple]\n",
    "\n",
    "\n",
    "\n",
    "for method in methods:\n",
    "    method_name = method.__name__.split('create_oligo_')[1]\n",
    "    times = []\n",
    "    print('----------------------------------------------------')\n",
    "    print(f\"Now testing with: {method_name}\")\n",
    "    for n_regions in n_regions_values:\n",
    "        print(f\"n_regions: {n_regions}\")\n",
    "        _, exec_time = method(n_regions, n_oligos_per_regions)\n",
    "        times.append(exec_time)\n",
    "        import os\n",
    "\n",
    "        [os.remove(item) for item in os.listdir() if item.startswith(\"cache\") and os.path.isfile(item)]\n",
    "        [rmtree(item) for item in os.listdir() if item.startswith(\"cache\") and os.path.isdir(item)]\n",
    "        del _\n",
    "\n",
    "\n",
    "    all_times.append((times, method_name))\n",
    "\n",
    "\n",
    "\n",
    "plot_times(all_times, n_regions_values)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "oligo_database_env",
   "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": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}