mirror of
https://github.com/ltcptgeneral/IdealRMT-DecisionTrees.git
synced 2026-10-07 16:28:31 +00:00
First try boundary nudging
This commit is contained in:
Nathan Huey committed
1 parent
afc882a569
commit
fda251f051
5 files changed
+146
-24
No files matched your search
+3
-1
@@ -1,3 +1,5 @@
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data.*
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__pycache__
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*.json
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*.json
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.DS_Store
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.ipynb_checkpoints/
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+131
-11
@@ -17,7 +17,8 @@
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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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"import math\n",
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"import copy"
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]
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},
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{
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@@ -30,9 +31,9 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"dataset size: 4735360\n",
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"train accuracy: 0.879490682862549\n",
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"test accuracy: 0.879490682862549\n"
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"dataset size: 1894144\n",
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"pre-nudge train accuracy: 0.879490682862549\n",
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"pre-nudge test accuracy: 0.879490682862549\n"
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]
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}
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],
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@@ -64,15 +65,126 @@
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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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"print(f\"pre-nudge 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)}\")"
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"print(f\"pre-nudge test accuracy: {accuracy_score(Yt, Predict_Yt)}\")"
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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": "17d2b9de-3e0d-4c52-a3da-aeab436898b4",
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"metadata": {},
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"outputs": [],
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"source": [
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"def nudge_threshold_power_2(threshold: float):\n",
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" threshold = math.floor(threshold)\n",
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" bit_len = threshold.bit_length()\n",
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" if threshold <= 2:\n",
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" nudged_value = threshold\n",
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" elif threshold & (1 << (bit_len - 2)):\n",
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" nudged_value = 1 << (bit_len)\n",
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" else:\n",
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" nudged_value = 1 << (bit_len - 1)\n",
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" return nudged_value"
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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": "fe251c3a-629d-4db7-b0aa-06bd085e8d60",
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"metadata": {},
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"outputs": [],
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"source": [
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"def nudge_threshold_n_significant_bits(threshold: float, n_sig_bits: int):\n",
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" threshold = math.floor(threshold)\n",
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" \n",
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" bit_len = threshold.bit_length()\n",
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" if bit_len - 1 <= n_sig_bits:\n",
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" nudged_value = threshold\n",
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" else:\n",
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" mask = ((1 << n_sig_bits) - 1) << (threshold.bit_length() - n_sig_bits)\n",
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" nudged_value = threshold & mask\n",
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" if threshold & (1 << (bit_len - 1 - n_sig_bits)):\n",
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" nudged_value += (1 << (bit_len - n_sig_bits))\n",
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" \n",
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" return nudged_value"
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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": "83fe5426-60a0-4231-bbd6-08970ca2af8e",
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"metadata": {},
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"outputs": [],
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"source": [
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"def nudge_threshold_max_n_bits(threshold: float, n_bits: int):\n",
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" threshold = math.floor(threshold)\n",
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" if n_bits == 0:\n",
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" return threshold\n",
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" \n",
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" mask = pow(2, 32) - 1 ^ ((1 << n_bits) - 1)\n",
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" nudged_value = threshold & mask\n",
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" if threshold & (1 << (n_bits - 1)):\n",
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" nudged_value += (1 << (n_bits))\n",
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" \n",
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" return nudged_value"
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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": 6,
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"id": "d2dba2fe-1822-48f0-b0af-9dbed09a31a5",
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"metadata": {},
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"outputs": [],
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"source": [
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"def crawl_tree(tree: _tree, node):\n",
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" # postfix traversal on non-leaf nodes\n",
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" flag = 0\n",
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" if tree.children_left[node] != -1:\n",
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" crawl_tree(tree, tree.children_left[node])\n",
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" flag |= 1\n",
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" if tree.children_right[node] != -1:\n",
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" crawl_tree(tree, tree.children_right[node])\n",
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" flag |= 1\n",
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"\n",
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" if flag:\n",
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" # can choose `nudge_threshold_power_2`, `nudge_threshold_n_significant_bits`, or `nudge_threshold_max_n_bits`\n",
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" tree.threshold[node] = nudge_threshold_max_n_bits(tree.threshold[node], 2)\n",
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"\n",
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"original_tree = dt.tree_\n",
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"nudged_tree = copy.deepcopy(original_tree)\n",
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"crawl_tree(nudged_tree, 0)"
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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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"id": "5230f85b-6908-46a9-922e-f9815e633931",
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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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"post-nudge train accuracy: 0.8767189822949047\n",
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"post-nudge test accuracy: 0.8767189822949047\n"
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]
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}
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],
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"source": [
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"dt.tree_ = nudged_tree\n",
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"Predict_Y = dt.predict(X)\n",
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"print(f\"post-nudge 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\"post-nudge test accuracy: {accuracy_score(Yt, Predict_Yt)}\")"
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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": "d336971a",
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"metadata": {},
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"outputs": [],
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@@ -141,7 +253,7 @@
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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": 9,
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"id": "7f36344d",
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"metadata": {},
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"outputs": [],
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@@ -163,13 +275,13 @@
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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": 10,
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"id": "cf8832b9",
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"metadata": {},
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"outputs": [
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{
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"data": {
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truncated
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truncated
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"text/plain": [
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"<Figure size 10000x10000 with 1 Axes>"
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]
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@@ -182,11 +294,19 @@
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"fig = plt.figure(figsize=(100,100))\n",
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"_ = plot_tree(dt, filled=True)"
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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": "ad2a735b-517f-49c6-80cb-a431f2788133",
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"metadata": {},
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"outputs": [],
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"source": []
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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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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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@@ -200,7 +320,7 @@
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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": "3.12.9"
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}
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},
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"nbformat": 4,
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@@ -89,7 +89,7 @@
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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 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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@@ -103,7 +103,7 @@
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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": "3.12.9"
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}
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},
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"nbformat": 4,
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+2
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@@ -241,7 +241,7 @@
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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 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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@@ -255,7 +255,7 @@
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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": "3.12.9"
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}
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},
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"nbformat": 4,
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+8
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@@ -117,8 +117,8 @@
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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: 13312\n",
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"RAM bits: 522\n"
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"TCAM bits: 13184\n",
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"RAM bits: 504\n"
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]
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}
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],
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@@ -263,8 +263,8 @@
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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: 3520\n",
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"RAM bits: 522\n"
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"TCAM bits: 3320\n",
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"RAM bits: 504\n"
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]
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}
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],
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@@ -368,8 +368,8 @@
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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: 2120\n",
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"RAM bits: 522\n"
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"TCAM bits: 2152\n",
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"RAM bits: 504\n"
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]
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}
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],
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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 (ipykernel)",
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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": "3.12.9"
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}
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},
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"nbformat": 4,
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