mirror of
https://github.com/ltcptgeneral/IdealRMT-DecisionTrees.git
synced 2025-09-04 14:27:23 +00:00
fix range bug in TreeCompress, update parser to autofail non-singular classifications
This commit is contained in:
@@ -2,7 +2,7 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 17,
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"execution_count": 1,
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"id": "938dec51",
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"metadata": {},
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"outputs": [],
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@@ -17,102 +17,37 @@
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"from matplotlib import pyplot as plt\n",
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"from labels import mac_to_label\n",
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"import json\n",
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"import math\n"
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"import math"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"execution_count": 2,
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"id": "442624c7",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[6, 40234, 5228], [6, 40234, 5228], [6, 443, 46330], [6, 3063, 443], [1, 0, 0], [17, 61725, 53], [6, 5228, 40234], [6, 443, 3063], [0, 0, 0], [0, 0, 0], [6, 40234, 5228], [17, 4500, 45966], [17, 53, 61725], [1, 0, 0], [6, 46330, 443], [6, 443, 46330], [0, 0, 0], [1, 0, 0], [6, 3063, 443], [6, 443, 3063]]\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"Set1 = pd.read_csv('data.csv').values.tolist()\n",
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"X = [i[0:3] for i in Set1]\n",
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"Y =[i[3] for i in Set1]\n",
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"\n",
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"print(X[0:20])\n"
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"Y =[i[3] for i in Set1]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "f18850b1",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"['Amazon Echo', 'Belkin Motion Sensor', 'Belkin Switch', 'Blipcare BP Meter', 'Dropcam', 'HP Printer', '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', 'other']\n"
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]
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}
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],
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"source": [
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"classes = [\n",
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" \"Amazon Echo\",\n",
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" \"Belkin Motion Sensor\",\n",
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" \"Belkin Switch\",\n",
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" \"Blipcare BP Meter\",\n",
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" \"Dropcam\",\n",
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" \"HP Printer\",\n",
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" \"NEST Smoke Sensor\",\n",
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" \"Netatmo Camera\",\n",
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" \"Netatmo Weather station\",\n",
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" \"Pixstart photo frame\",\n",
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" \"Samsung Smart Cam\",\n",
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" \"Smart Things\",\n",
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" \"TP-Link Camera\",\n",
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" \"TP-Link Plug\",\n",
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" \"Triby Speaker\",\n",
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" \"Withings\",\n",
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" \"Withings Scale\",\n",
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" \"other\"\n",
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" ]\n",
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"\n",
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"print(classes)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 3,
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"id": "12ad454d",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[1, 11, 17], [1, 11, 17], [1, 4, 11, 15, 17], [17], [8, 17], [], [1, 11, 17, 15], [11], [8, 17], [8, 17], [1, 11, 17], [], [], [8, 17], [17]]\n",
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"['other', 'other', 'Dropcam', 'other', 'Netatmo Camera', 'other', 'Triby Speaker', 'Smart Things', 'other', 'Belkin Switch', 'other', 'Netatmo Camera', 'Netatmo Camera', 'other', 'other']\n",
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"947072\n",
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"947072\n",
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"The accuracy was: 0.0\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"predict_Yt = []\n",
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"with open('compressed_tree.json', 'r') as file:\n",
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" data = json.load(file)\n",
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" #print(data['layers']['dst'])\n",
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" classes = data[\"classes\"]\n",
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" for x in X:\n",
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" counter = 0\n",
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" class_set = []\n",
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" 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",
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" #print(\"The feature is: \", feature)\n",
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"\n",
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" for node in data['layers'][feature]:\n",
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" #print(\"The node is: \", node)\n",
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" #print(f\"The min is: {node['min']}, the max is {node['max']}\")\n",
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" if node['min'] is None:\n",
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" if x[counter] < node['max']:\n",
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" class_set.append(node['classes'])\n",
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@@ -130,39 +65,42 @@
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" break #is this an issue?\n",
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"\n",
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" counter += 1\n",
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" #print(\"The list of classes is: \", class_set)\n",
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" result = set(class_set[0])\n",
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" for s in class_set[1:]:\n",
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" result.intersection_update(s)\n",
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"\n",
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" #print(\"The result was: \", result)\n",
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" predict_Yt.append(list(result))\n",
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" #print(predict_Yt)\n",
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" #predict_Yt.append(list(result))\n",
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" #print(result)\n",
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" if len(result) == 1:\n",
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" prediction = list(result)[0]\n",
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" pred_class = classes[prediction]\n",
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" predict_Yt.append(pred_class)\n",
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" else:\n",
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" predict_Yt.append(None)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "8b4c56b6",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0.8448217242194891\n"
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]
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}
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],
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"source": [
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"correct = 0\n",
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"for i in range(len(Y)):\n",
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" prediction = predict_Yt[i]\n",
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" if prediction != None and Y[i] == prediction:\n",
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" correct += 1\n",
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"\n",
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" \n",
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"\n",
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"print(predict_Yt[0:15])\n",
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"print(Y[0:15])\n",
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"\n",
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"\n",
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"print(len(predict_Yt))\n",
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"print(len(Y))\n",
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"\n",
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"\n",
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"counter = 0\n",
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"for index in range(len(Y)):\n",
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" for possible_class_index in predict_Yt[index]:\n",
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" if Y[index] == classes[possible_class_index]:\n",
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" counter += 1\n",
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"\n",
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"\n",
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"print(\"The accuracy was: \", counter / len(Y))\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n"
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"print(correct / len(Y))"
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]
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}
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],
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@@ -182,7 +120,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.6"
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"version": "3.12.7"
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}
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},
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"nbformat": 4,
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File diff suppressed because one or more lines are too long
@@ -109,13 +109,13 @@
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"\t\tvalue = condition[\"value\"]\n",
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"\n",
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"\t\t# move the min/max for the corresponding feature in compressed\n",
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"\t\tif operation == \"<=\" and compressed[feature][\"min\"] is None:\n",
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"\t\tif operation == \"<=\" and compressed[feature][\"max\"] is None:\n",
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"\t\t\tcompressed[feature][\"max\"] = value\n",
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"\t\telif operation == \">\" and compressed[feature][\"max\"] is None:\n",
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"\t\telif operation == \">\" and compressed[feature][\"min\"] is None:\n",
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"\t\t\tcompressed[feature][\"min\"] = value\n",
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"\t\telif operation == \"<=\" and value < compressed[feature][\"min\"]:\n",
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"\t\telif operation == \"<=\" and value < compressed[feature][\"max\"]:\n",
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"\t\t\tcompressed[feature][\"max\"] = value\n",
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"\t\telif operation == \">\" and value > compressed[feature][\"max\"]:\n",
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"\t\telif operation == \">\" and value > compressed[feature][\"min\"]:\n",
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"\t\t\tcompressed[feature][\"min\"] = value\n",
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"\n",
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"\tpath[\"compressed\"] = compressed"
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@@ -263,7 +263,7 @@
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"[1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
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"id mapping: \n",
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"[['dst_range', 'dst_meta'], ['src_range', 'src_meta'], ['protocl_range', 'protocl_meta'], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], []]\n",
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"TCAM bits: 3584\n",
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"TCAM bits: 3520\n",
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"RAM bits: 522\n"
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]
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}
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