Reduce n_epochs in CI for msr_banzhaf_digits.ipynb

aai-institute · Jan 12, 2025 · f4201a2 · f4201a2
1 parent 504a639
commit f4201a2
Showing 1 changed file with 26 additions and 78 deletions.
diff --git a/notebooks/msr_banzhaf_digits.ipynb b/notebooks/msr_banzhaf_digits.ipynb
@@ -43,12 +43,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "tags": [
-     "hide"
-    ]
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
@@ -70,7 +66,6 @@
     "\n",
     "is_CI = os.environ.get(\"CI\")\n",
     "random_state = 24\n",
-    "n_jobs = 16\n",
     "random.seed(random_state)"
    ]
   },
@@ -123,53 +118,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "hide"
-    ]
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "# In CI we only use a subset of the training set\n",
+    "# Reduce computation time for CI\n",
     "training_data = list(training_data)\n",
     "if is_CI:\n",
     "    training_data[0] = training_data[0][:10]\n",
     "    training_data[1] = training_data[1][:10]\n",
     "    max_checks = 1\n",
+    "    n_jobs = 2\n",
+    "    n_epochs = 1\n",
     "else:\n",
     "    training_data[0] = training_data[0][:200]\n",
     "    training_data[1] = training_data[1][:200]\n",
-    "    max_checks = 1000"
+    "    max_checks = 1000\n",
+    "    n_jobs = 16\n",
+    "    n_epochs = 40"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "tags": [
-     "hide-input",
-     "invertible-output"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 400x400 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Visualize some of the data\n",
     "fig, axes = plt.subplots(2, 2, figsize=(4, 4))\n",
@@ -219,28 +192,15 @@
     "from support.banzhaf import TorchCNNModel\n",
     "\n",
     "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "model = TorchCNNModel(lr=0.001, epochs=40, batch_size=32, device=device)\n",
+    "model = TorchCNNModel(lr=0.001, epochs=n_epochs, batch_size=32, device=device)\n",
     "model.fit(x=training_data[0], y=training_data[1])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "tags": [
-     "hide-input"
-    ]
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train Accuracy: 0.705\n",
-      "Test Accuracy: 0.630\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "print(f\"Train Accuracy: {model.score(x=training_data[0], y=training_data[1]):.3f}\")\n",
     "print(f\"Test Accuracy: {model.score(x=test_data[0], y=test_data[1]):.3f}\")"
@@ -550,21 +510,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "tags": [
-     "hide-output"
-    ]
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|█████████▉| 99.9/100 [00:59<00:00,  1.69%/s]              \n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "anomalous_dataset = Dataset(\n",
     "    x_train=x_train_anomalous,\n",
@@ -574,7 +522,7 @@
     ")\n",
     "\n",
     "anomalous_utility = Utility(\n",
-    "    model=TorchCNNModel(),\n",
+    "    model=TorchCNNModel(lr=0.001, epochs=n_epochs, batch_size=32, device=device),\n",
     "    data=anomalous_dataset,\n",
     "    scorer=Scorer(\"accuracy\", default=0.0, range=(0, 1)),\n",
     "    cache_backend=MemcachedCacheBackend(MemcachedClientConfig()),\n",
@@ -722,12 +670,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "utility = Utility(\n",
-    "    model=TorchCNNModel(),\n",
+    "    model=TorchCNNModel(lr=0.001, epochs=n_epochs, batch_size=32, device=device),\n",
     "    data=dataset,\n",
     "    scorer=Scorer(\"accuracy\", default=0.0, range=(0, 1)),\n",
     "    cache_backend=MemcachedCacheBackend(MemcachedClientConfig()),\n",
@@ -969,7 +917,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -983,15 +931,15 @@
     "    )\n",
     "else:\n",
     "    utility = Utility(\n",
-    "        model=TorchCNNModel(),\n",
+    "        model=TorchCNNModel(lr=0.001, epochs=n_epochs, batch_size=32, device=device),\n",
     "        data=dataset,\n",
     "        scorer=Scorer(\"accuracy\", default=0.0, range=(0, 1)),\n",
     "    )"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [