Update notebook

.gitmodules

@@ -1,3 +1,6 @@
 [submodule "qlib-git"]
 	path = .latent-data/qlib
 	url = git@github.com:microsoft/qlib.git
+[submodule ".latent-data/qlib"]
+	path = .latent-data/qlib
+	url = git@github.com:microsoft/qlib.git

README.md

@@ -113,7 +113,7 @@ If you find that this project helps your research, please consider citing the re
   note    = {\mbox{doi}:\url{10.1109/TPAMI.2021.3054824}}
 }
 @inproceedings{dong2020nasbench201,
-  title     = {NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search},
+  title     = {{NAS-Bench-201}: Extending the Scope of Reproducible Neural Architecture Search},
   author    = {Dong, Xuanyi and Yang, Yi},
   booktitle = {International Conference on Learning Representations (ICLR)},
   url       = {https://openreview.net/forum?id=HJxyZkBKDr},

README_CN.md

@@ -107,7 +107,7 @@ Some methods use knowledge distillation (KD), which require pre-trained models.
   note    = {\mbox{doi}:\url{10.1109/TPAMI.2021.3054824}}
 }
 @inproceedings{dong2020nasbench201,
-  title     = {NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search},
+  title     = {{NAS-Bench-201}: Extending the Scope of Reproducible Neural Architecture Search},
   author    = {Dong, Xuanyi and Yang, Yi},
   booktitle = {International Conference on Learning Representations (ICLR)},
   url       = {https://openreview.net/forum?id=HJxyZkBKDr},

notebooks/NATS-Bench/issue-96.ipynb

@@ -0,0 +1,91 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2021-03-01 12:28:12] Try to use the default NATS-Bench (topology) path from fast_mode=True and path=None.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from nats_bench import create\n",
+    "import numpy as np\n",
+    "\n",
+    "def get_correlation(A, B):\n",
+    "    return float(np.corrcoef(A, B)[0,1])\n",
+    "\n",
+    "# Create the API for tologoy search space\n",
+    "api = create(None, 'tss', fast_mode=True, verbose=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "There are 15625 architectures on the topology search space\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('There are {:} architectures on the topology search space'.format(len(api)))\n",
+    "accuracies_12, accuracies_200 = [], []\n",
+    "for i, arch in enumerate(api):\n",
+    "    info_a = api.get_more_info(i, dataset='cifar10-valid', hp='12', is_random=False)\n",
+    "    accuracies_12.append(info_a['valid-accuracy'])\n",
+    "\n",
+    "    info_b = api.get_more_info(i, dataset='cifar10-valid', hp='200', is_random=False)\n",
+    "    accuracies_200.append(info_b['test-accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CIFAR-10] The correlation between 12-epoch validation accuracy and 200-epoch test accuracy is: 91.18%\n"
+     ]
+    }
+   ],
+   "source": [
+    "correlation = get_correlation(accuracies_12, accuracies_200)\n",
+    "print('[CIFAR-10] The correlation between 12-epoch validation accuracy and 200-epoch test accuracy is: {:.2f}%'.format(correlation * 100))"
+   ]
+  }
+ ],
+ "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": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}