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https://github.com/ltcptgeneral/IdealRMT-DecisionTrees.git
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add data processing notebooks
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188
DecisionTree.ipynb
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188
DecisionTree.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": 4,
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"id": "d5618056",
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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\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"
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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": 5,
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"id": "d336971a",
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"metadata": {},
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"outputs": [],
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"source": [
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"# extract argument\n",
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"inputfile = \"data.csv\"\n",
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"outputfile = \"tree\"\n",
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"#testfile = args.t\n",
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"\n",
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"# output the tree\n",
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"def get_lineage(tree, feature_names, file):\n",
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" proto = []\n",
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" src = []\n",
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" dst = []\n",
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" left = tree.tree_.children_left\n",
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" right = tree.tree_.children_right\n",
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" threshold = tree.tree_.threshold\n",
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" features = [feature_names[i] for i in tree.tree_.feature]\n",
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" value = tree.tree_.value\n",
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" le = '<='\n",
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" g = '>'\n",
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" # get ids of child nodes\n",
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" idx = np.argwhere(left == -1)[:, 0]\n",
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" \n",
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" # traverse the tree and get the node information\n",
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" def recurse(left, right, child, lineage=None):\n",
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" if lineage is None:\n",
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" lineage = [child]\n",
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" if child in left:\n",
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" parent = np.where(left == child)[0].item()\n",
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" split = 'l'\n",
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" else:\n",
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" parent = np.where(right == child)[0].item()\n",
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" split = 'r'\n",
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" \n",
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" lineage.append((parent, split, threshold[parent], features[parent]))\n",
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" if parent == 0:\n",
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" lineage.reverse()\n",
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" return lineage\n",
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" else:\n",
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" return recurse(left, right, parent, lineage)\n",
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"\n",
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" for j, child in enumerate(idx):\n",
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" clause = ' when '\n",
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" for node in recurse(left, right, child):\n",
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" if len(str(node)) < 3:\n",
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" continue\n",
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" i = node\n",
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" \n",
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" if i[1] == 'l':\n",
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" sign = le\n",
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" else:\n",
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" sign = g\n",
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" clause = clause + i[3] + sign + str(i[2]) + ' and '\n",
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" \n",
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" # wirte the node information into text file\n",
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" a = list(value[node][0])\n",
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" ind = a.index(max(a))\n",
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" clause = clause[:-4] + ' then ' + str(ind)\n",
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" file.write(clause)\n",
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" file.write(\";\\n\")\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": 8,
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"id": "b96f3403",
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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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"train accuracy: 0.879490682862549\n",
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"test accuracy: 0.879490682862549\n"
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]
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}
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],
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"source": [
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"# Training set X and Y\n",
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"Set1 = pd.read_csv(inputfile)\n",
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"Set = Set1.values.tolist()\n",
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"X = [i[0:3] for i in Set]\n",
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"Y =[i[3] for i in Set]\n",
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"\n",
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"# Test set Xt and Yt\n",
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"Set2 = pd.read_csv(inputfile)\n",
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"Sett = Set2.values.tolist()\n",
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"Xt = [i[0:3] for i in Set]\n",
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"Yt =[i[3] for i in Set]\n",
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"\n",
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"#class_names=['iperf','memcached','ping','sparkglm','sparkkmeans']\n",
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"feature_names=['proto','src','dst']\n",
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"\n",
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"# prepare training and testing set\n",
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"X = np.array(X)\n",
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"Y = np.array(Y)\n",
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"Xt = np.array(Xt)\n",
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"Yt = np.array(Yt)\n",
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"\n",
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"# decision tree fit\n",
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"dt = DecisionTreeClassifier(max_depth = 5)\n",
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"dt.fit(X, Y)\n",
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"Predict_Y = dt.predict(X)\n",
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"print(f\"train accuracy: {accuracy_score(Y, Predict_Y)}\")\n",
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"\n",
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"Predict_Yt = dt.predict(Xt)\n",
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"print(f\"test accuracy: {accuracy_score(Yt, Predict_Yt)}\")\n",
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"\n",
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"# output the tree in a text file, write it\n",
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"threshold = dt.tree_.threshold\n",
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"features = [feature_names[i] for i in dt.tree_.feature]\n",
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"proto = []\n",
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"src = []\n",
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"dst = []\n",
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"for i, fe in enumerate(features):\n",
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" \n",
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" if fe == 'proto':\n",
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" proto.append(threshold[i])\n",
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" elif fe == 'src':\n",
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" if threshold[i] != -2.0:\n",
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" src.append(threshold[i])\n",
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" else:\n",
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" dst.append(threshold[i])\n",
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"proto = [int(i) for i in proto]\n",
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"src = [int(i) for i in src]\n",
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"dst = [int(i) for i in dst]\n",
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"proto.sort()\n",
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"src.sort()\n",
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"dst.sort()\n",
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"tree = open(outputfile,\"w+\")\n",
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"tree.write(\"proto = \")\n",
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"tree.write(str(proto))\n",
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"tree.write(\";\\n\")\n",
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"tree.write(\"src = \")\n",
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"tree.write(str(src))\n",
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"tree.write(\";\\n\")\n",
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"tree.write(\"dst = \")\n",
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"tree.write(str(dst))\n",
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"tree.write(\";\\n\")\n",
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"get_lineage(dt,feature_names,tree)\n",
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"tree.close()"
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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": "switch",
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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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