sym scaling

transformer/scaling.ipynb

@@ -681,7 +681,7 @@
     "    evaluator = Evaluator()\n",
     "    agg_metrics, _ = evaluator(iter(tss), iter(forecasts))\n",
     "    agg_metrics[\"trainable_parameters\"] = summarize(estimator.create_lightning_module()).trainable_parameters\n",
-    "    sym_metrics.append(agg_metrics.copy())\\\n",
+    "    sym_metrics.append(agg_metrics.copy())\n",
     "\n",
     "with open(\"elec_sym_metrics.pkl\", \"wb\") as fp:\n",
     "    pickle.dump(sym_metrics, fp)"
@@ -689,7 +689,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "fad41444",
    "metadata": {},
    "outputs": [],
@@ -697,6 +697,131 @@
     "with open(\"elec_sym_metrics.pkl\", \"rb\") as fp:\n",
     "    sym_metrics = pickle.load(fp)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "5f53e2aa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Symmetric Scaling')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(\n",
+    "    [metrics[\"trainable_parameters\"] for metrics in sym_metrics],\n",
+    "    [metrics[\"mean_wQuantileLoss\"] for metrics in sym_metrics],   \n",
+    ")\n",
+    "plt.xlabel(\"trainable parameters\")\n",
+    "plt.ylabel(\"CRPS\")\n",
+    "plt.title(\"Symmetric Scaling\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "4ab76a75",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Symmetric Scaling')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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 truncated
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(\n",
+    "    [metrics[\"trainable_parameters\"] for metrics in sym_metrics],\n",
+    "    [metrics[\"MASE\"] for metrics in sym_metrics],   \n",
+    ")\n",
+    "plt.xlabel(\"trainable parameters\")\n",
+    "plt.ylabel(\"MASE\")\n",
+    "plt.title(\"Symmetric Scaling\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "faa34130",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Symmetric Scaling')"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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 truncated
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(\n",
+    "    [metrics[\"trainable_parameters\"] for metrics in sym_metrics],\n",
+    "    [metrics[\"NRMSE\"] for metrics in sym_metrics],   \n",
+    ")\n",
+    "plt.xlabel(\"trainable parameters\")\n",
+    "plt.ylabel(\"NRMSE\")\n",
+    "plt.title(\"Symmetric Scaling\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "318edfdf",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {