8 Commits

Author SHA1 Message Date
51f920e2ba upload example tree based on 10-01 data 2025-06-20 03:18:09 +00:00
1136bd93ea update readme 2025-06-14 03:10:48 +00:00
2ad40946d1 update treetormt 2025-06-12 19:53:46 +00:00
50075b1acc Merge remote-tracking branch 'origin/decision-tree-nudge-boundaries' 2025-06-12 19:52:02 +00:00
Jai Parera
1585399c7d Fixed loop ordering and path_to_class in JSON 2025-06-11 23:37:33 -07:00
8301998da3 temp fix for issue with metadata 2025-06-12 06:10:39 +00:00
3b2d6b3186 fix eval bug in parser 2025-06-12 05:37:48 +00:00
Nathan Huey
fda251f051 First try boundary nudging 2025-06-11 16:55:23 -07:00
12 changed files with 3015 additions and 64 deletions

6
.gitignore vendored
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@@ -1,5 +1,5 @@
data.*
__pycache__
*.json
data/*
data/*
.DS_Store
.ipynb_checkpoints/

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@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 138,
"id": "938dec51",
"metadata": {},
"outputs": [],
@@ -22,66 +22,90 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 139,
"id": "442624c7",
"metadata": {},
"outputs": [],
"source": [
"Set1 = pd.read_csv('data.csv').values.tolist()\n",
"Set1 = pd.read_csv('data/combined/data.csv').values.tolist()\n",
"X = [i[0:3] for i in Set1]\n",
"Y =[i[3] for i in Set1]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 142,
"id": "12ad454d",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'0': 20, '1': 20, '2': 9, '3': 20, '4': 0, '5': 13, '6': 20, '7': 0, '8': 12, '9': 4, '10': 20, '11': 4, '12': 1, '13': 16, '14': 20, '15': 2, '16': 20, '17': 0, '18': 20, '19': 20, '20': 20, '21': 20, '22': 20, '23': 1, '24': 2, '25': 20, '26': 13, '27': 11, '28': 20, '29': 20}\n"
]
}
],
"source": [
"predict_Yt = []\n",
"index=0\n",
"\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",
" 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",
" paths_set = []\n",
" features = [\"protocol\", \"src\", \"dst\"]\n",
" for feature in features:\n",
" if feature in data[\"layers\"]:\n",
" for node in data['layers'][feature]:\n",
" if node['min'] is None:\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",
" 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",
" break #is this an issue?\n",
" else:\n",
" continue\n",
" elif node['min'] < x[counter] and x[counter] < node['max']:\n",
" class_set.append(node['classes'])\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",
" if len(result) == 1:\n",
" prediction = list(result)[0]\n",
" pred_class = classes[prediction]\n",
" predict_Yt.append(pred_class)\n",
" else:\n",
" predict_Yt.append(None)"
" if len(paths) != 1:\n",
" print(paths)\n",
" print(x)\n",
" print(result)\n",
" assert len(paths) == 1\n",
" path = list(paths)[0]\n",
" pred = data[\"path_to_class\"][str(path)]\n",
" pred_class = classes[pred]\n",
" predict_Yt.append(pred_class)\n",
" \n",
" index += 1"
]
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 143,
"id": "8b4c56b6",
"metadata": {},
"outputs": [
@@ -89,7 +113,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
"0.8448217242194891\n"
"0.8410252791654538\n"
]
}
],

File diff suppressed because one or more lines are too long

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@@ -89,7 +89,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "switch",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -103,7 +103,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.7"
"version": "3.12.9"
}
},
"nbformat": 4,

View File

@@ -20,4 +20,12 @@ Run `extract_all_datasets.py` which will extract the data from each file in `dat
## Training
Run `DecisionTree.ipynb`, the tree should be output in `tree`
Run `DecisionTree.ipynb`, the tree should be output in `tree.json`
## Compression
Run `TreeCompress.ipynb`, the tree should be output in `compressed_tree.json`
## RMT
Run `TreeToRMT.ipynb`, it will report the TCAM and SRAM usage of the compressed tree

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@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 73,
"id": "ec310f34",
"metadata": {},
"outputs": [],
@@ -14,7 +14,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 74,
"id": "5b54797e",
"metadata": {},
"outputs": [],
@@ -28,7 +28,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 75,
"id": "a38fdb8a",
"metadata": {},
"outputs": [],
@@ -60,7 +60,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 76,
"id": "2fd4f738",
"metadata": {},
"outputs": [],
@@ -83,7 +83,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 77,
"id": "98cde024",
"metadata": {},
"outputs": [],
@@ -123,7 +123,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 78,
"id": "b6fbadbf",
"metadata": {},
"outputs": [],
@@ -183,7 +183,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 79,
"id": "0a767971",
"metadata": {},
"outputs": [],
@@ -213,16 +213,22 @@
"\tcompressed_layers[feature_name].append({\"min\": lower, \"max\": upper, \"paths\": paths, \"classes\": classes})\n",
"\t#print(\"=\"*40)\n",
"\n",
"path_to_class = {}\n",
"for i in range(len(tree[\"paths\"])):\n",
" path = tree[\"paths\"][i]\n",
" path_to_class[path[\"id\"]] = path[\"classification\"]\n",
"\n",
"compressed_tree = {\n",
"\t\"paths\": path_ids,\n",
"\t\"classes\": path_classes,\n",
"\t\"layers\": compressed_layers,\n",
" \"path_to_class\": path_to_class,\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 80,
"id": "561b0bc1",
"metadata": {},
"outputs": [],
@@ -241,7 +247,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "switch",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -255,7 +261,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.7"
"version": "3.12.9"
}
},
"nbformat": 4,

View File

@@ -117,8 +117,8 @@
"[1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"id mapping: \n",
"[['dst_range', 'dst_meta'], ['src_range', 'src_meta'], ['protocl_range', 'protocl_meta'], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], []]\n",
"TCAM bits: 13312\n",
"RAM bits: 522\n"
"TCAM bits: 13184\n",
"RAM bits: 504\n"
]
}
],
@@ -263,8 +263,8 @@
"[1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"id mapping: \n",
"[['dst_range', 'dst_meta'], ['src_range', 'src_meta'], ['protocl_range', 'protocl_meta'], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], []]\n",
"TCAM bits: 3520\n",
"RAM bits: 522\n"
"TCAM bits: 3320\n",
"RAM bits: 504\n"
]
}
],
@@ -274,6 +274,14 @@
"print(f\"RAM bits: {ram_bits}\")"
]
},
{
"cell_type": "markdown",
"id": "2504b1ba",
"metadata": {},
"source": [
"# Priority Aware Prefix Expansion"
]
},
{
"cell_type": "code",
"execution_count": 9,
@@ -368,8 +376,8 @@
"[1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"id mapping: \n",
"[['dst_range', 'dst_meta'], ['src_range', 'src_meta'], ['protocl_range', 'protocl_meta'], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], []]\n",
"TCAM bits: 2120\n",
"RAM bits: 522\n"
"TCAM bits: 2152\n",
"RAM bits: 504\n"
]
}
],
@@ -382,7 +390,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "switch",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},

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@@ -0,0 +1,560 @@
{
"paths": [
0,
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9,
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],
"classes": [
"Amazon Echo",
"Belkin Motion Sensor",
"Belkin Switch",
"Dropcam",
"HP Printer",
"LiFX Bulb",
"NEST Smoke Sensor",
"Netatmo Camera",
"Netatmo Weather station",
"Pixstart photo frame",
"Samsung Smart Cam",
"Smart Things",
"TP-Link Camera",
"TP-Link Plug",
"Triby Speaker",
"Withings",
"Withings Scale",
"Withings sleep sensor",
"iHome PowerPlug",
"other"
],
"layers": {
"dst": [
{
"min": null,
"max": 2136,
"paths": [
0,
1,
2,
3,
4,
5,
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],
"classes": [
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4
]
},
{
"min": 2136,
"max": 2224,
"paths": [
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],
"classes": [
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]
},
{
"min": 2224,
"max": 5016,
"paths": [
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],
"classes": [
1,
19
]
},
{
"min": 5016,
"max": 25848,
"paths": [
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],
"classes": [
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]
},
{
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"paths": [
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],
"classes": [
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]
},
{
"min": 47936,
"max": 47944,
"paths": [
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],
"classes": [
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]
},
{
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"max": 49152,
"paths": [
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],
"classes": [
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]
},
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"paths": [
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"classes": [
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]
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"paths": [
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"classes": [
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"src": [
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"max": 64,
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},
{
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"18": 2,
"19": 15,
"20": 17,
"21": 19,
"22": 19
}
}

734
example/naive_rmt.json Normal file
View File

@@ -0,0 +1,734 @@
[
{
"id": "dst_range",
"step": 0,
"match": "ternary",
"entries": 68,
"key_size": 16,
"ranges": [
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"000010000100****",
"0000100001010***",
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]
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"00001000011*****",
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"0000100010110000"
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{
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700
example/priority_aware.json Normal file
View File

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744
example/tree.json Normal file
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]
}

View File

@@ -0,0 +1,47 @@
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