{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "443692c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "77be6d6c",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "           0\n",
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       "3     affirm\n",
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       "..       ...\n",
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       "588     null\n",
       "589      ack\n",
       "590    hello\n",
       "\n",
       "[591 rows x 1 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expected = pd.read_csv('evaluate.tsv', sep='\\t', header=None)\n",
    "expected = expected.fillna('null')\n",
    "expected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b77d4102",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "           0\n",
       "0      hello\n",
       "1       help\n",
       "2    request\n",
       "3     affirm\n",
       "4    request\n",
       "..       ...\n",
       "586     null\n",
       "587  request\n",
       "588     null\n",
       "589      ack\n",
       "590    hello\n",
       "\n",
       "[591 rows x 1 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted = pd.read_csv('predicted.tsv', sep='\\t', header=None)\n",
    "predicted = predicted.fillna('null')\n",
    "predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "79ecc70a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(expected, predicted))"
   ]
  }
 ],
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