Explore-ZeroShotDataset.ipynb

{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "# Explore ZeroShotDataset\n",
    "\n",
    "This notebook is an experimental approach on solving the ARC challenge. See https://arcprize.org/ for more details."
   ],
   "id": "7e378b5f8ac9d1"
  },
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-08-25T14:10:40.747871Z",
     "start_time": "2024-08-25T14:10:36.583724Z"
    }
   },
   "source": [
    "import random\n",
    "\n",
    "from pathlib import Path\n",
    "\n",
    "from arc2024.data import ZeroShotDataset\n",
    "from arc2024.utils import plot"
   ],
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Prepare Dataset",
   "id": "c4156c9dda9040ba"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-25T14:10:41.051099Z",
     "start_time": "2024-08-25T14:10:40.750289Z"
    }
   },
   "cell_type": "code",
   "source": [
    "dataset = ZeroShotDataset(dataset_dir=Path('datasets'))\n",
    "dataset"
   ],
   "id": "183b8a634e649a8c",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<arc2024.data.ZeroShotDataset at 0x70667c4ec110>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Visualize Random Challenge from Training Dataset",
   "id": "254d51fba59ef060"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-25T14:10:41.422091Z",
     "start_time": "2024-08-25T14:10:41.052455Z"
    }
   },
   "cell_type": "code",
   "source": [
    "X, y = random.choice(dataset)\n",
    "\n",
    "plot.input_and_output(X, y)"
   ],
   "id": "3e67e21f8bd2e67e",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1920x1440 with 2 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Visualize specific Challenges from Trainnig Dataset",
   "id": "b7ffbc65402ef661"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# Challenge '9172f3a0': Scale 3x",
   "id": "d0ef205e3349cefb"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-25T14:10:41.636136Z",
     "start_time": "2024-08-25T14:10:41.424063Z"
    }
   },
   "cell_type": "code",
   "source": [
    "X, y = dataset[dataset.challenge_ids.index('9172f3a0')]\n",
    "plot.input_and_output(X, y)"
   ],
   "id": "9f6bf421dbdf55be",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1920x1440 with 2 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Challenge '6150a2bd': Rotate 180°",
   "id": "fc5d530223332a36"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-25T14:14:28.052488Z",
     "start_time": "2024-08-25T14:14:27.873507Z"
    }
   },
   "cell_type": "code",
   "source": [
    "X, y = dataset[dataset.challenge_ids.index('6150a2bd')]\n",
    "plot.input_and_output(X, y) "
   ],
   "id": "9512cb78b855715d",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1920x1440 with 2 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "9c8468d28ce6eccf"
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
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 "nbformat_minor": 5
}