added

Author: nipunbatra

posts/2024-forecast.ipynb

@@ -539,11 +539,75 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 114,
    "id": "a06840b9",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "from transformers import InformerForPrediction, InformerConfig\n",
+    "\n",
+    "config = InformerConfig(\n",
+    "    input_dim=context_length,\n",
+    "    prediction_length=prediction_length,\n",
+    "    num_heads=4,\n",
+    "    encoder_layers=2,\n",
+    "    decoder_layers=2,\n",
+    "    use_mask=True,\n",
+    "    forecast=True\n",
+    ")\n",
+    "\n",
+    "informer = InformerForPrediction(config)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "id": "5465e218",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "134531"
+      ]
+     },
+     "execution_count": 116,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(p.numel() for p in informer.parameters())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "id": "4f46f6a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "forward() missing 2 required positional arguments: 'past_time_features' and 'past_observed_mask'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[117], line 7\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(n_epochs):\n\u001b[1;32m      6\u001b[0m     optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[0;32m----> 7\u001b[0m     y_pred \u001b[38;5;241m=\u001b[39m \u001b[43minformer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mXs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      8\u001b[0m     loss \u001b[38;5;241m=\u001b[39m criterion(y_pred, ys)\n\u001b[1;32m      9\u001b[0m     loss\u001b[38;5;241m.\u001b[39mbackward()\n",
+      "File \u001b[0;32m~/miniconda3/lib/python3.9/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1499\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1500\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
+      "\u001b[0;31mTypeError\u001b[0m: forward() missing 2 required positional arguments: 'past_time_features' and 'past_observed_mask'"
+     ]
+    }
+   ],
+   "source": [
+    "# training loop\n",
+    "n_epochs = 100\n",
+    "\n",
+    "start_time = time.time()\n",
+    "for epoch in range(n_epochs):\n",
+    "    optimizer.zero_grad()\n",
+    "    y_pred = informer(Xs, past\n"
+   ]
   }
  ],
  "metadata": {