demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook shows a simple demo of how to use `iPrompt`. It follows a simple sklearn-style interface to yield an explanation given a dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from iprompt import Explainer\n",
    "import datasets\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load some data\n",
    "Here, we load some training/validation data from the rotten-tomatoes movie dataset. To make things fast, we restrict our training and testing datasets to only 300 examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using custom data configuration default\n",
      "Reusing dataset rotten_tomatoes (/home/chansingh/.cache/huggingface/datasets/rotten_tomatoes/default/1.0.0/40d411e45a6ce3484deed7cc15b82a53dad9a72aafd9f86f8f227134bec5ca46)\n"
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       "model_id": "45324fcc21fc436ba71a1ee4c6a04739",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using custom data configuration default\n",
      "Reusing dataset rotten_tomatoes (/home/chansingh/.cache/huggingface/datasets/rotten_tomatoes/default/1.0.0/40d411e45a6ce3484deed7cc15b82a53dad9a72aafd9f86f8f227134bec5ca46)\n"
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       "model_id": "1863402143024ba0a0701cbb65d015eb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dset = datasets.load_dataset('rotten_tomatoes')['train']\n",
    "dset = dset.select(np.random.choice(len(dset), size=300, replace=False))\n",
    "\n",
    "dset_val = datasets.load_dataset('rotten_tomatoes')['validation']\n",
    "dset_val = dset_val.select(np.random.choice(len(dset_val), size=300, replace=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple call to iPrompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp = Explainer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    ".explain(\n",
    "    dset,\n",
    "    ...\n",
    ")"
   ]
  }
 ],
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  "kernelspec": {
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   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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