mirror of
https://github.com/ltcptgeneral/IdealRMT-DecisionTrees.git
synced 2025-09-04 14:27:23 +00:00
Implemented working compressed tree parser to get classification accuracy
This commit is contained in:
krishpatel
190
CompressedTreeParser.ipynb
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190
CompressedTreeParser.ipynb
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{
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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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"id": "938dec51",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"import argparse\n",
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"from sklearn.tree import DecisionTreeClassifier, plot_tree, _tree\n",
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"from sklearn.metrics import accuracy_score\n",
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"from sklearn.tree import export_graphviz\n",
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"import pydotplus\n",
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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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]
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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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"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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"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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]
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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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"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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"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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" 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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" break #is this an issue?\n",
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" else:\n",
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" continue\n",
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" elif node['max'] is None:\n",
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" if node['min'] < x[counter]:\n",
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" class_set.append(node['classes'])\n",
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" break #is this an issue?\n",
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" else:\n",
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" continue\n",
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" elif node['min'] < x[counter] and x[counter] < node['max']:\n",
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" class_set.append(node['classes'])\n",
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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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"\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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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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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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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@@ -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": 1,
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"execution_count": 9,
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"id": "ec310f34",
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"metadata": {},
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"outputs": [],
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@@ -14,7 +14,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 10,
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"id": "5b54797e",
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"metadata": {},
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"outputs": [],
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@@ -28,7 +28,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 11,
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"id": "a38fdb8a",
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"metadata": {},
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"outputs": [],
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@@ -38,14 +38,14 @@
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"i = 0\n",
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"\n",
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"path_ids = set()\n",
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"path_classes = tree[\"classes\"]\n",
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"path_classes = set()\n",
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"\n",
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"# for each path in the tree\n",
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"for path in paths:\n",
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"\t# assign a path id \n",
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"\tpath[\"id\"] = i\n",
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"\tpath_ids.add(i)\n",
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"\t#path_classes.add(path[\"classification\"])\n",
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"\tpath_classes.add(path[\"classification\"])\n",
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"\ti += 1\t\n",
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"\t# for each condition\n",
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"\tconditions = path[\"conditions\"]\n",
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@@ -60,7 +60,7 @@
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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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"execution_count": 12,
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"id": "2fd4f738",
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"metadata": {},
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"outputs": [],
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@@ -83,7 +83,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 13,
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"id": "98cde024",
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"metadata": {},
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"outputs": [],
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@@ -123,7 +123,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 14,
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"id": "b6fbadbf",
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"metadata": {},
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"outputs": [],
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@@ -171,19 +171,16 @@
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"\t\t# for each bucket which encompases the condition's range, add this path's id to the sets \n",
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"\t\ti = 0\n",
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"\t\tfor bp in breakpoints[feature_name]:\n",
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"\t\t\tif is_in_range(bp, lower, upper):\n",
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"\t\t\tin_range = is_in_range(bp, lower, upper)\n",
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"\t\t\tif in_range:\n",
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"\t\t\t\tbuckets_id[feature_name][i].add(ID)\n",
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"\t\t\t\tbuckets_class[feature_name][i].add(Class)\n",
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"\t\t\ti += 1\n",
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"\n",
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"\t\tif is_in_range(bp+1, lower, upper):\n",
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"\t\t\tbuckets_id[feature_name][i].add(ID)\n",
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"\t\t\tbuckets_class[feature_name][i].add(Class)"
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"\t\t\ti += 1"
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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": 7,
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"execution_count": 15,
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"id": "0a767971",
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"metadata": {},
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"outputs": [],
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@@ -201,13 +198,13 @@
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"\tfor i in range(1, len(buckets_id[feature_name]) - 1):\n",
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"\t\tlower = breakpoints[feature_name][i-1]\n",
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"\t\tupper = breakpoints[feature_name][i]\n",
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"\t\tpaths = buckets_id[feature_name][i]\n",
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"\t\tmembers = buckets_id[feature_name][i]\n",
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"\t\tclasses = buckets_class[feature_name][i]\n",
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"\t\t#print(f\"{feature_name} = [{lower}, {upper}]: {buckets[feature_name][i]}\")\n",
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"\t\tcompressed_layers[feature_name].append({\"min\": lower, \"max\": upper, \"paths\": paths, \"classes\": classes})\n",
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"\tlower = breakpoints[feature_name][len(breakpoints[feature_name]) - 1]\n",
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"\tupper = None\n",
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"\tpaths = buckets_id[feature_name][len(buckets_id[feature_name]) - 1]\n",
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"\tmembers = buckets_id[feature_name][len(buckets_id[feature_name]) - 1]\n",
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"\tclasses = buckets_class[feature_name][len(buckets_class[feature_name]) - 1]\n",
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"\t#print(f\"{feature_name} = [{lower}, {upper}]: {members}\")\n",
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"\tcompressed_layers[feature_name].append({\"min\": lower, \"max\": upper, \"paths\": paths, \"classes\": classes})\n",
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@@ -222,7 +219,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 16,
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"id": "561b0bc1",
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"metadata": {},
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"outputs": [],
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@@ -241,7 +238,7 @@
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],
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"metadata": {
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"kernelspec": {
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"display_name": "switch",
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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@@ -255,7 +252,7 @@
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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": "3.12.7"
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"version": "3.10.6"
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}
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},
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"nbformat": 4,
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@@ -382,7 +382,7 @@
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],
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"metadata": {
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"kernelspec": {
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"display_name": "switch",
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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@@ -396,7 +396,7 @@
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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": "3.12.7"
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"version": "3.10.6"
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}
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},
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"nbformat": 4,
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