mirror of
https://github.com/ltcptgeneral/IdealRMT-DecisionTrees.git
synced 2025-09-04 14:27:23 +00:00
129 lines
3.6 KiB
Plaintext
129 lines
3.6 KiB
Plaintext
{
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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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"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"
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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": 2,
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"id": "442624c7",
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"metadata": {},
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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]"
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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": 3,
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"id": "12ad454d",
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"metadata": {},
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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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" 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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" for node in data['layers'][feature]:\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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" 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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" #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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"print(correct / len(Y))"
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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": "3.12.7"
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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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