{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "sys.path.append(\"../\")\n",
    "\n",
    "import torch.nn as nn\n",
    "import torch\n",
    "\n",
    "from abl.abducer.abducer_base import HED_Abducer\n",
    "from abl.abducer.kb import HED_prolog_KB\n",
    "\n",
    "from abl.utils.plog import logger\n",
    "from abl.models.nn import SymbolNet\n",
    "from abl.models.basic_model import BasicModel\n",
    "from abl.models.wabl_models import WABLBasicModel\n",
    "\n",
    "from datasets.hed.get_hed import get_hed, split_equation\n",
    "from abl import framework_hed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize logger\n",
    "recorder = logger()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logic Part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR: /home/gaoeh/ABL-Package/examples/datasets/hed/learn_add.pl:67:9: Syntax error: Operator expected\n"
     ]
    }
   ],
   "source": [
    "# Initialize knowledge base and abducer\n",
    "kb = HED_prolog_KB(pseudo_label_list=[1, 0, '+', '='], pl_file='./datasets/hed/learn_add.pl')\n",
    "abducer = HED_Abducer(kb)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Machine Learning Part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize necessary component for machine learning part\n",
    "cls = SymbolNet(\n",
    "    num_classes=len(kb.pseudo_label_list),\n",
    "    image_size=(28, 28, 1),\n",
    ")\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.RMSprop(cls.parameters(), lr=0.001, weight_decay=1e-6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pretrain NN classifier\n",
    "framework_hed.hed_pretrain(kb, cls, recorder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize BasicModel\n",
    "# The function of BasicModel is to wrap NN models into the form of an sklearn estimator\n",
    "base_model = BasicModel(\n",
    "    cls,\n",
    "    criterion,\n",
    "    optimizer,\n",
    "    device,\n",
    "    save_interval=1,\n",
    "    save_dir=recorder.save_dir,\n",
    "    batch_size=32,\n",
    "    num_epochs=1,\n",
    "    recorder=recorder,\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use WABL model to join two parts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = WABLBasicModel(base_model, kb.pseudo_label_list)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_train_data = get_hed(train=True)\n",
    "train_data, val_data = split_equation(total_train_data, 3, 1)\n",
    "test_data = get_hed(train=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train and save"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model, mapping = framework_hed.train_with_rule(model, abducer, train_data, val_data, select_num=10, min_len=5, max_len=8)\n",
    "framework_hed.hed_test(model, abducer, mapping, train_data, test_data, min_len=5, max_len=8)\n",
    "\n",
    "recorder.dump()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ABL",
   "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": "3.8.16"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}