alu/IdealRMT-DecisionTrees
1
0
Fork 0
mirror of https://github.com/ltcptgeneral/IdealRMT-DecisionTrees.git synced 2025-09-07 07:47:24 +00:00
Code Issues Packages Projects Releases Wiki Activity

temp fix for issue with metadata

Browse Source
This commit is contained in:
Arthur Lu
2025-06-12 06:10:39 +00:00
parent 3b2d6b3186
commit 8301998da3
1 changed files with 16 additions and 57 deletions
Download Patch File Download Diff File Expand all files Collapse all files

73
CompressedTreeParser.ipynb
View File

@@ -37,36 +37,16 @@
"execution_count": 3, "execution_count": 3,
"id": "12ad454d", "id": "12ad454d",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"name": "stdout",
"output_type": "stream",
"text": [
"here1\n",
" protocl src dst classfication\n",
"0 6 40234 5228 other\n",
"1 6 40234 5228 other\n",
"2 6 443 46330 Dropcam\n",
"3 6 3063 443 other\n",
"4 1 0 0 Netatmo Camera\n",
"... ... ... ... ...\n",
"2419339 6 443 47940 Dropcam\n",
"2419340 6 47940 443 other\n",
"2419341 6 443 47940 Dropcam\n",
"2419342 0 0 0 iHome PowerPlug\n",
"2419343 0 0 0 other\n",
"\n",
"[2419344 rows x 4 columns]\n",
"{8, 20}\n",
"{13}\n",
"[6, 40234, 5228]\n",
"other\n"
]
}
],
"source": [ "source": [
"predict_Yt = []\n", "predict_Yt = []\n",
"index=0\n", "index=0\n",
"original_tree = open('tree.json', 'r')\n",
"original_tree = json.load(original_tree)\n",
"path_to_class = {}\n",
"for i in range(len(original_tree[\"paths\"])):\n",
" path = original_tree[\"paths\"][i]\n",
" path_to_class[path[\"id\"]] = path[\"classification\"]\n",
"with open('compressed_tree.json', 'r') as file:\n", "with open('compressed_tree.json', 'r') as file:\n",
" data = json.load(file)\n", " data = json.load(file)\n",
" classes = data[\"classes\"]\n", " classes = data[\"classes\"]\n",
@@ -105,28 +85,11 @@
"\n", "\n",
" #predict_Yt.append(list(result))\n", " #predict_Yt.append(list(result))\n",
" #print(result)\n", " #print(result)\n",
" if len(result) == 1:\n", " assert len(paths) == 1\n",
" prediction = list(result)[0]\n", " path = list(paths)[0]\n",
" pred_class = classes[prediction]\n", " pred = path_to_class[path]\n",
" predict_Yt.append(pred_class)\n", " pred_class = classes[pred]\n",
" elif len(paths) == 1:\n", " predict_Yt.append(pred_class)\n",
" print(\"here1\")\n",
" print(pd.read_csv('data.csv'))\n",
" print(result)\n",
" print(paths)\n",
" print(x)\n",
" print(Y[index])\n",
" break\n",
" predict_Yt.append(None)\n",
" else:\n",
" print(\"here2\")\n",
" print(pd.read_csv('data.csv'))\n",
" print(result)\n",
" print(paths)\n",
" print(x)\n",
" print(Y[index])\n",
" break\n",
" predict_Yt.append(None)\n",
" \n", " \n",
" index += 1" " index += 1"
] ]
@@ -138,14 +101,10 @@
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"ename": "IndexError", "name": "stdout",
"evalue": "list index out of range", "output_type": "stream",
"output_type": "error", "text": [
"traceback": [ "0.8451332670948943\n"
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mIndexError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[4]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m correct = \u001b[32m0\u001b[39m\n\u001b[32m 2\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(Y)):\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m prediction = \u001b[43mpredict_Yt\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\n\u001b[32m 4\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m prediction != \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m Y[i] == prediction:\n\u001b[32m 5\u001b[39m correct += \u001b[32m1\u001b[39m\n",
"\u001b[31mIndexError\u001b[39m: list index out of range"
] ]
} }
], ],