demonstration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PSET 1: Bottom-Up Synthesis\n",
    "\n",
    "I follow Algorithm 1 in the BUSTLE paper:\n",
    "\n",
    "> Odena, A. *et al.* BUSTLE: Bottom-Up Program Synthesis Through Learning-Guided Exploration. in *9th International Conference on Learning Representations*; 2021 May 3-7; Austria.\n",
    "\n",
    "First, I import the required libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import itertools\n",
    "\n",
    "# argument parser for command line arguments\n",
    "import argparse\n",
    "\n",
    "# import arithmetic module\n",
    "from arithmetic import *\n",
    "from abstract_syntax_tree import OperatorNode\n",
    "from examples import example_set, check_examples\n",
    "from synthesizer import extract_constants, observationally_equivalent, check_program\n",
    "import config"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, I define variables as proxies for command-line arguments provided to the synthesizer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "domain = \"arithmetic\"\n",
    "examples_key = \"addition\"\n",
    "examples = example_set[examples_key]\n",
    "max_weight = 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I define a function to extract constants from examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize program bank\n",
    "program_bank = extract_constants(examples)\n",
    "program_bank_str = [p.str() for p in program_bank]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, I define the bottom-up synthesis algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(x0 + x1)\n"
     ]
    }
   ],
   "source": [
    "# define operators\n",
    "operators = arithmetic_operators\n",
    "\n",
    "# iterate over each level\n",
    "for weight in range(2, max_weight):\n",
    "\n",
    "    for op in operators:\n",
    "\n",
    "        # get all possible combinations of primitives in program bank\n",
    "        combinations = itertools.combinations(program_bank, op.arity)\n",
    "\n",
    "        # iterate over each combination\n",
    "        for combination in combinations:\n",
    "\n",
    "            # get type signature\n",
    "            type_signature = [p.type for p in combination]\n",
    "\n",
    "            # check if type signature matches operator\n",
    "            if type_signature != op.arg_types:\n",
    "                continue\n",
    "\n",
    "            # check that sum of weights of arguments <= w\n",
    "            if sum([p.weight for p in combination]) > weight:\n",
    "                continue\n",
    "\n",
    "            # create new program\n",
    "            program = OperatorNode(op, combination)\n",
    "\n",
    "            # check if program is in program bank using string representation\n",
    "            if program.str() in program_bank_str:\n",
    "                continue\n",
    "            \n",
    "            # check if program is observationally equivalent to any program in program bank\n",
    "            if any([observationally_equivalent(program, p, examples) for p in program_bank]):\n",
    "                continue\n",
    "\n",
    "            # add program to program bank\n",
    "            program_bank.append(program)\n",
    "            program_bank_str.append(program.str())\n",
    "\n",
    "            # check if program passes all examples\n",
    "            if check_program(program, examples):\n",
    "                # return(program)\n",
    "                print(program.str())"
   ]
  }
 ],
 "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}