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fix eval bug in parser

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Arthur Lu
2025-06-12 05:37:48 +00:00
parent 24fc2ed6f7
commit 3b2d6b3186
1 changed files with 64 additions and 8 deletions
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72
CompressedTreeParser.ipynb
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@@ -37,37 +37,71 @@
"execution_count": 3,
"id": "12ad454d",
"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": [
"predict_Yt = []\n",
"index=0\n",
"with open('compressed_tree.json', 'r') as file:\n",
" data = json.load(file)\n",
" classes = data[\"classes\"]\n",
" for x in X:\n",
" counter = 0\n",
" class_set = []\n",
" paths_set = []\n",
" for feature in reversed(data['layers']): #Have to reverse this list due to structure of the data.csv file and how it aligns with the compressed tree layers\n",
" for node in data['layers'][feature]:\n",
" if node['min'] is None:\n",
" if x[counter] < node['max']:\n",
" if x[counter] <= node['max']:\n",
" class_set.append(node['classes'])\n",
" paths_set.append(node[\"paths\"])\n",
" break #is this an issue?\n",
" else:\n",
" continue\n",
" elif node['max'] is None:\n",
" if node['min'] < x[counter]:\n",
" class_set.append(node['classes'])\n",
" paths_set.append(node[\"paths\"])\n",
" break #is this an issue?\n",
" else:\n",
" continue\n",
" elif node['min'] < x[counter] and x[counter] < node['max']:\n",
" elif node['min'] < x[counter] and x[counter] <= node['max']:\n",
" class_set.append(node['classes'])\n",
" paths_set.append(node[\"paths\"])\n",
" break #is this an issue?\n",
"\n",
" counter += 1\n",
" result = set(class_set[0])\n",
" paths = set(paths_set[0])\n",
" for s in class_set[1:]:\n",
" result.intersection_update(s)\n",
" for s in paths_set[1:]:\n",
" paths.intersection_update(s)\n",
"\n",
" #predict_Yt.append(list(result))\n",
" #print(result)\n",
@@ -75,8 +109,26 @@
" prediction = list(result)[0]\n",
" pred_class = classes[prediction]\n",
" predict_Yt.append(pred_class)\n",
" elif len(paths) == 1:\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",
" predict_Yt.append(None)"
" 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",
" index += 1"
]
},
{
@@ -86,10 +138,14 @@
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.8448217242194891\n"
"ename": "IndexError",
"evalue": "list index out of range",
"output_type": "error",
"traceback": [
"\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"
]
}
],