mirror of
https://github.com/ltcptgeneral/IdealRMT-DecisionTrees.git
synced 2025-09-04 14:27:23 +00:00
144 lines
4.3 KiB
Plaintext
144 lines
4.3 KiB
Plaintext
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 1,
|
|
"id": "938dec51",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import numpy as np\n",
|
|
"import pandas as pd\n",
|
|
"import argparse\n",
|
|
"from sklearn.tree import DecisionTreeClassifier, plot_tree, _tree\n",
|
|
"from sklearn.metrics import accuracy_score\n",
|
|
"from sklearn.tree import export_graphviz\n",
|
|
"import pydotplus\n",
|
|
"from matplotlib import pyplot as plt\n",
|
|
"from labels import mac_to_label\n",
|
|
"import json\n",
|
|
"import math"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 2,
|
|
"id": "442624c7",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"Set1 = pd.read_csv('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,
|
|
"id": "12ad454d",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"predict_Yt = []\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",
|
|
" 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",
|
|
" 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",
|
|
"\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",
|
|
" assert len(paths) == 1\n",
|
|
" path = list(paths)[0]\n",
|
|
" pred = path_to_class[path]\n",
|
|
" pred_class = classes[pred]\n",
|
|
" predict_Yt.append(pred_class)\n",
|
|
" \n",
|
|
" index += 1"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"id": "8b4c56b6",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"0.8451332670948943\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"correct = 0\n",
|
|
"for i in range(len(Y)):\n",
|
|
" prediction = predict_Yt[i]\n",
|
|
" if prediction != None and Y[i] == prediction:\n",
|
|
" correct += 1\n",
|
|
"\n",
|
|
"print(correct / len(Y))"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.12.7"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|