{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "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 AbducerBase\n",
    "from abl.abducer.kb import HWF_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.hwf.get_hwf import get_hwf\n",
    "from abl import framework_hed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize logger\n",
    "recorder = logger()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logic Part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize knowledge base and abducer\n",
    "kb = HWF_KB(GKB_flag=True)\n",
    "abducer = AbducerBase(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(num_classes=len(kb.pseudo_label_list), image_size=(45, 45, 1))\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(cls.parameters(), lr=0.001, betas=(0.9, 0.99))"
   ]
  },
  {
   "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": [
    "# Initialize WABL model\n",
    "# The main function of the WABL model is to serialize data and \n",
    "# provide a unified interface for different machine learning models\n",
    "model = WABLBasicModel(base_model, kb.pseudo_label_list)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get training and testing data\n",
    "train_data = get_hwf(train=True, get_pseudo_label=True)\n",
    "test_data = get_hwf(train=False, get_pseudo_label=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train and save"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train model\n",
    "framework_hed.train(\n",
    "    model, abducer, train_data, test_data, loop_num=15, sample_num=5000, verbose=1\n",
    ")\n",
    "\n",
    "# Save results\n",
    "recorder.dump()"
   ]
  }
 ],
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