File size: 1,499 Bytes
91ed952 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 1, 3, 3, 1, 2, 2, 2, 0, 0, 1, 1, 0, 0, 2, 1, 2, 2, 1, 1, 2, 3,\n",
" 1, 0, 0, 0, 2, 2, 0, 0, 0, 2, 2, 0, 4, 1, 0, 0, 2, 0, 0, 0, 0, 3,\n",
" 0, 0, 2, 0, 0, 1, 0, 1, 2, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 3, 1, 0,\n",
" 1, 2, 2, 1, 2, 2, 1, 0, 1])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset = \"banking77\"\n",
"T = 3\n",
"seed = 0\n",
"data = pd.read_csv(f\"../data/{dataset}/all.csv\", index_col=0, header=0)\n",
"train_lst = np.loadtxt(f\"../lists/{dataset}/size={int(2**T)}/seed={seed}/0.0-1.0.txt\")\n",
"labels = data.loc[train_lst,\"label\"]\n",
"np.bincount(labels)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "llmcal",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|