ABLkit/examples/hed/hed_example.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
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   "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.basic_model import BasicModel\n",
    "from abl.models.wabl_models import WABLBasicModel\n",
    "\n",
    "from models.nn import SymbolNet\n",
    "from datasets.get_hed import get_hed, split_equation\n",
    "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 = HED_prolog_KB(pseudo_label_list=[1, 0, '+', '='], pl_file='./datasets/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()"
   ]
  }
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