Systemy_dialogowe/evaluate/evaluate.ipynb

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2022-04-26 18:33:20 +02:00
{
"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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" 0\n",
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},
"execution_count": 34,
"metadata": {},
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}
],
"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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"</div>"
],
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" 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": {},
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],
"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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