include_class_info.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. include class info in the json result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataclasses import dataclass,field\n",
    "from typing import Set, Optional\n",
    "\n",
    "from dataclasses_json import dataclass_json,global_config\n",
    "\n",
    "\n",
    "@dataclass_json\n",
    "@dataclass\n",
    "class Animal:\n",
    "    id: int = 0\n",
    "    health: int = 100\n",
    "\n",
    "\n",
    "@dataclass_json\n",
    "@dataclass\n",
    "class Cat(Animal):\n",
    "    age: int = 1\n",
    "\n",
    "@dataclass_json\n",
    "@dataclass\n",
    "class Dog(Animal):\n",
    "    age: int = 1\n",
    "\n",
    "@dataclass_json\n",
    "@dataclass\n",
    "class PetCat(Cat):\n",
    "    name: str = ''\n",
    "\n",
    "@dataclass_json\n",
    "@dataclass\n",
    "class Person:\n",
    "    name:str = 'zyx'\n",
    "    animals: list[Animal] = field(default_factory=lambda:[])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'zyx',\n",
       " 'animals': [{'id': 0, 'health': 100},\n",
       "  {'id': 0, 'health': 100, 'age': 1},\n",
       "  {'id': 0, 'health': 100, 'age': 1, 'name': ''}]}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p1=Person(animals=[Animal(),Cat(),PetCat()])\n",
    "p1.to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'zyx',\n",
       " 'animals': [{'id': 0, 'health': 100},\n",
       "  {'id': 0, 'health': 100},\n",
       "  {'id': 0, 'health': 100}]}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p2 = Person.from_dict(p1.to_dict())\n",
    "p2.to_dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "some fields are missing!\n",
    "\n",
    "to solve this, we need to include class info into the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'__module__': '__main__',\n",
       " '__name__': 'Person',\n",
       " 'name': 'zyx',\n",
       " 'animals': [{'__module__': '__main__',\n",
       "   '__name__': 'Animal',\n",
       "   'id': 0,\n",
       "   'health': 100},\n",
       "  {'__module__': '__main__',\n",
       "   '__name__': 'Cat',\n",
       "   'id': 0,\n",
       "   'health': 100,\n",
       "   'age': 1},\n",
       "  {'__module__': '__main__',\n",
       "   '__name__': 'PetCat',\n",
       "   'id': 0,\n",
       "   'health': 100,\n",
       "   'age': 1,\n",
       "   'name': ''}]}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "global_config.include_class_info=True\n",
    "p1.to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'zyx',\n",
       " 'animals': [{'id': 0, 'health': 100},\n",
       "  {'id': 0, 'health': 100, 'age': 1},\n",
       "  {'id': 0, 'health': 100, 'age': 1, 'name': ''}]}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p2 = Person.from_dict(p1.to_dict())\n",
    "global_config.include_class_info=False\n",
    "p2.to_dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "now the fields are all restored!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. use cache to save time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "if i have thousands of objects to jsonize, the code will waste much time on get dataclass info, which will not change however in the process of jsonization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wed Feb 28 21:25:23 2024    profile_stats\n",
      "\n",
      "         11802740 function calls (10302726 primitive calls) in 6.541 seconds\n",
      "\n",
      "   Ordered by: cumulative time\n",
      "\n",
      "   ncalls  tottime  percall  cumtime  percall filename:lineno(function)\n",
      "        2    0.000    0.000    6.541    3.270 c:\\Users\\ZYX\\miniconda3\\envs\\dsj\\Lib\\site-packages\\IPython\\core\\interactiveshell.py:3541(run_code)\n",
      "        2    0.000    0.000    6.541    3.270 {built-in method builtins.exec}\n",
      "        1    0.000    0.000    6.541    6.541 C:\\Users\\ZYX\\AppData\\Local\\Temp\\ipykernel_21292\\461415860.py:1(<module>)\n",
      "        1    0.004    0.004    6.541    6.541 d:\\workspace\\dataclasses-json\\dataclasses_json\\api.py:26(to_json)\n",
      "        1    0.000    0.000    6.488    6.488 d:\\workspace\\dataclasses-json\\dataclasses_json\\api.py:72(to_dict)\n",
      " 300003/1    0.772    0.000    6.488    6.488 d:\\workspace\\dataclasses-json\\dataclasses_json\\core.py:429(_asdict)\n",
      "   100001    0.057    0.000    6.462    0.000 d:\\workspace\\dataclasses-json\\dataclasses_json\\core.py:459(<genexpr>)\n",
      "700007/300003    0.361    0.000    3.900    0.000 d:\\workspace\\dataclasses-json\\dataclasses_json\\utils.py:14(wrapper)\n",
      "   200002    1.658    0.000    3.450    0.000 d:\\workspace\\dataclasses-json\\dataclasses_json\\core.py:60(_user_overrides_or_exts)\n",
      "   500005    0.156    0.000    1.349    0.000 d:\\workspace\\dataclasses-json\\dataclasses_json\\core.py:36(_cached_fields)\n",
      "   500005    0.808    0.000    1.192    0.000 c:\\Users\\ZYX\\miniconda3\\envs\\dsj\\Lib\\dataclasses.py:1269(fields)\n",
      "1500321/700313    0.349    0.000    1.065    0.000 {built-in method builtins.isinstance}\n",
      "   400004    0.167    0.000    0.827    0.000 c:\\Users\\ZYX\\miniconda3\\envs\\dsj\\Lib\\typing.py:1175(__instancecheck__)\n",
      "   400004    0.202    0.000    0.660    0.000 c:\\Users\\ZYX\\miniconda3\\envs\\dsj\\Lib\\typing.py:1446(__subclasscheck__)\n",
      "   400004    0.148    0.000    0.348    0.000 {built-in method builtins.issubclass}\n",
      "   300003    0.160    0.000    0.266    0.000 c:\\Users\\ZYX\\miniconda3\\envs\\dsj\\Lib\\dataclasses.py:1292(is_dataclass)\n",
      "   100001    0.071    0.000    0.227    0.000 d:\\workspace\\dataclasses-json\\dataclasses_json\\utils.py:203(_handle_undefined_parameters_safe)\n",
      "   400004    0.113    0.000    0.215    0.000 <string>:1(<lambda>)\n",
      "  1500015    0.205    0.000    0.205    0.000 c:\\Users\\ZYX\\miniconda3\\envs\\dsj\\Lib\\dataclasses.py:1284(<genexpr>)\n",
      "   400004    0.091    0.000    0.199    0.000 <frozen abc>:121(__subclasscheck__)\n",
      "   800008    0.197    0.000    0.197    0.000 {method 'update' of 'dict' objects}\n",
      "   100001    0.142    0.000    0.164    0.000 d:\\workspace\\dataclasses-json\\dataclasses_json\\core.py:109(_encode_overrides)\n",
      "   100001    0.131    0.000    0.131    0.000 d:\\workspace\\dataclasses-json\\dataclasses_json\\utils.py:189(_undefined_parameter_action_safe)\n",
      "   400004    0.116    0.000    0.116    0.000 {method 'get' of 'mappingproxy' objects}\n",
      "   700011    0.115    0.000    0.115    0.000 {built-in method builtins.getattr}\n",
      "   400004    0.108    0.000    0.108    0.000 {built-in method _abc._abc_subclasscheck}\n",
      "   500307    0.108    0.000    0.108    0.000 {method 'values' of 'dict' objects}\n",
      "   400004    0.102    0.000    0.102    0.000 {built-in method __new__ of type object at 0x00007FFC09F2B9B0}\n",
      "   200002    0.051    0.000    0.051    0.000 {method 'append' of 'list' objects}\n",
      "        1    0.000    0.000    0.048    0.048 c:\\Users\\ZYX\\miniconda3\\envs\\dsj\\Lib\\json\\__init__.py:183(dumps)\n",
      "        1    0.000    0.000    0.048    0.048 c:\\Users\\ZYX\\miniconda3\\envs\\dsj\\Lib\\json\\encoder.py:183(encode)\n",
      "        1    0.048    0.048    0.048    0.048 c:\\Users\\ZYX\\miniconda3\\envs\\dsj\\Lib\\json\\encoder.py:205(iterencode)\n",
      "   300003    0.046    0.000    0.046    0.000 {built-in method builtins.hasattr}\n",
      "   100001    0.025    0.000    0.025    0.000 {method 'lower' of 'str' objects}\n",
      "   100001    0.023    0.000    0.023    0.000 {method 'items' of 'dict' objects}\n",
      "      302    0.002    0.000    0.004    0.000 c:\\Users\\ZYX\\miniconda3\\envs\\dsj\\Lib\\site-packages\\ipykernel\\ipkernel.py:770(_clean_thread_parent_frames)\n",
      "      151    0.001    0.000    0.001    0.000 c:\\Users\\ZYX\\miniconda3\\envs\\dsj\\Lib\\threading.py:1533(enumerate)\n",
      "      755    0.001    0.000    0.001    0.000 c:\\Users\\ZYX\\miniconda3\\envs\\dsj\\Lib\\threading.py:1196(ident)\n",
      "      604    0.000    0.000    0.000    0.000 {method 'keys' of 'dict' objects}\n",
      "        2    0.000    0.000    0.000    0.000 c:\\Users\\ZYX\\miniconda3\\envs\\dsj\\Lib\\codeop.py:121(__call__)\n",
      "        2    0.000    0.000    0.000    0.000 {built-in method builtins.compile}\n",
      "        1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}\n",
      "      151    0.000    0.000    0.000    0.000 {method '__exit__' of '_thread.RLock' objects}\n",
      "        2    0.000    0.000    0.000    0.000 c:\\Users\\ZYX\\miniconda3\\envs\\dsj\\Lib\\contextlib.py:299(helper)\n",
      "        2    0.000    0.000    0.000    0.000 c:\\Users\\ZYX\\miniconda3\\envs\\dsj\\Lib\\site-packages\\traitlets\\traitlets.py:676(__get__)\n",
      "        4    0.000    0.000    0.000    0.000 {built-in method builtins.next}\n",
      "        2    0.000    0.000    0.000    0.000 c:\\Users\\ZYX\\miniconda3\\envs\\dsj\\Lib\\contextlib.py:104(__init__)\n",
      "        2    0.000    0.000    0.000    0.000 c:\\Users\\ZYX\\miniconda3\\envs\\dsj\\Lib\\contextlib.py:141(__exit__)\n",
      "        2    0.000    0.000    0.000    0.000 c:\\Users\\ZYX\\miniconda3\\envs\\dsj\\Lib\\contextlib.py:132(__enter__)\n",
      "        2    0.000    0.000    0.000    0.000 c:\\Users\\ZYX\\miniconda3\\envs\\dsj\\Lib\\site-packages\\traitlets\\traitlets.py:629(get)\n",
      "        4    0.000    0.000    0.000    0.000 c:\\Users\\ZYX\\miniconda3\\envs\\dsj\\Lib\\site-packages\\IPython\\core\\compilerop.py:180(extra_flags)\n",
      "        1    0.000    0.000    0.000    0.000 c:\\Users\\ZYX\\miniconda3\\envs\\dsj\\Lib\\json\\encoder.py:105(__init__)\n",
      "        2    0.000    0.000    0.000    0.000 {method 'get' of 'dict' objects}\n",
      "        2    0.000    0.000    0.000    0.000 c:\\Users\\ZYX\\miniconda3\\envs\\dsj\\Lib\\site-packages\\IPython\\core\\interactiveshell.py:3493(compare)\n",
      "        2    0.000    0.000    0.000    0.000 c:\\Users\\ZYX\\miniconda3\\envs\\dsj\\Lib\\site-packages\\IPython\\core\\interactiveshell.py:1277(user_global_ns)\n",
      "        1    0.000    0.000    0.000    0.000 {method 'join' of 'str' objects}\n",
      "        4    0.000    0.000    0.000    0.000 c:\\Users\\ZYX\\miniconda3\\envs\\dsj\\Lib\\typing.py:2132(cast)\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<pstats.Stats at 0x22344ab74a0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import cProfile\n",
    "import pstats\n",
    "global_config.enable_cache=False\n",
    "p3 = Person(animals=[Animal() for _ in range(100000)])\n",
    "\n",
    "# Create a cProfile object\n",
    "pr = cProfile.Profile()\n",
    "\n",
    "# Enable profiling\n",
    "pr.enable()\n",
    "\n",
    "# Run your code\n",
    "result_without_cache = p3.to_json()\n",
    "\n",
    "# Disable profiling\n",
    "pr.disable()\n",
    "\n",
    "# Save the stats to a file\n",
    "pr.dump_stats('profile_stats')\n",
    "\n",
    "# Open the saved stats file in Jupyter Notebook\n",
    "stats = pstats.Stats('profile_stats')\n",
    "stats.sort_stats('cumulative')\n",
    "stats.print_stats()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wed Feb 28 21:26:30 2024    profile_stats\n",
      "\n",
      "         5702755 function calls (4602444 primitive calls) in 2.713 seconds\n",
      "\n",
      "   Ordered by: cumulative time\n",
      "\n",
      "   ncalls  tottime  percall  cumtime  percall filename:lineno(function)\n",
      "   100001    0.041    0.000    2.664    0.000 d:\\workspace\\dataclasses-json\\dataclasses_json\\core.py:459(<genexpr>)\n",
      "1500355/700052    0.493    0.000    1.168    0.000 {built-in method builtins.isinstance}\n",
      "400006/400004    0.158    0.000    0.783    0.000 c:\\Users\\ZYX\\miniconda3\\envs\\dsj\\Lib\\typing.py:1175(__instancecheck__)\n",
      "400006/400005    0.191    0.000    0.625    0.000 c:\\Users\\ZYX\\miniconda3\\envs\\dsj\\Lib\\typing.py:1446(__subclasscheck__)\n",
      "400006/400005    0.141    0.000    0.327    0.000 {built-in method builtins.issubclass}\n",
      "   300003    0.147    0.000    0.241    0.000 c:\\Users\\ZYX\\miniconda3\\envs\\dsj\\Lib\\dataclasses.py:1292(is_dataclass)\n",
      "   100001    0.066    0.000    0.208    0.000 d:\\workspace\\dataclasses-json\\dataclasses_json\\utils.py:203(_handle_undefined_parameters_safe)\n",
      "400006/400005    0.085    0.000    0.186    0.000 <frozen abc>:121(__subclasscheck__)\n",
      "   100001    0.130    0.000    0.153    0.000 d:\\workspace\\dataclasses-json\\dataclasses_json\\core.py:109(_encode_overrides)\n",
      "   300003    0.129    0.000    0.129    0.000 d:\\workspace\\dataclasses-json\\dataclasses_json\\utils.py:14(wrapper)\n",
      "   100001    0.118    0.000    0.118    0.000 d:\\workspace\\dataclasses-json\\dataclasses_json\\utils.py:189(_undefined_parameter_action_safe)\n",
      "   400006    0.098    0.000    0.098    0.000 {built-in method _abc._abc_subclasscheck}\n",
      "   200002    0.048    0.000    0.048    0.000 {method 'append' of 'list' objects}\n",
      "        1    0.000    0.000    0.048    0.048 c:\\Users\\ZYX\\miniconda3\\envs\\dsj\\Lib\\json\\__init__.py:183(dumps)\n",
      "        1    0.000    0.000    0.047    0.047 c:\\Users\\ZYX\\miniconda3\\envs\\dsj\\Lib\\json\\encoder.py:183(encode)\n",
      "        1    0.047    0.047    0.047    0.047 c:\\Users\\ZYX\\miniconda3\\envs\\dsj\\Lib\\json\\encoder.py:205(iterencode)\n",
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      "   300006    0.043    0.000    0.043    0.000 {built-in method builtins.hasattr}\n",
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      "      286    0.002    0.000    0.004    0.000 c:\\Users\\ZYX\\miniconda3\\envs\\dsj\\Lib\\site-packages\\ipykernel\\ipkernel.py:770(_clean_thread_parent_frames)\n",
      "      143    0.001    0.000    0.001    0.000 c:\\Users\\ZYX\\miniconda3\\envs\\dsj\\Lib\\threading.py:1533(enumerate)\n",
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      " 300001/0    0.684    0.000    0.000          d:\\workspace\\dataclasses-json\\dataclasses_json\\core.py:429(_asdict)\n",
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      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<pstats.Stats at 0x22344a734d0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import cProfile\n",
    "import pstats\n",
    "global_config.enable_cache=True\n",
    "p3 = Person(animals=[Animal() for _ in range(100000)])\n",
    "\n",
    "# Create a cProfile object\n",
    "pr = cProfile.Profile()\n",
    "\n",
    "# Enable profiling\n",
    "pr.enable()\n",
    "\n",
    "# Run your code\n",
    "result_with_cache = p3.to_json()\n",
    "\n",
    "# Disable profiling\n",
    "pr.disable()\n",
    "\n",
    "# Save the stats to a file\n",
    "pr.dump_stats('profile_stats')\n",
    "\n",
    "# Open the saved stats file in Jupyter Notebook\n",
    "stats = pstats.Stats('profile_stats')\n",
    "stats.sort_stats('cumulative')\n",
    "stats.print_stats()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The improvement in program speed is huge, from 6.6s to 2.5s in my laptop\n",
    "\n",
    "now let's check if the results are the same"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_with_cache==result_without_cache"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
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   "pygments_lexer": "ipython3",
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