{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import torch\n",
    "from torch import nn\n",
    "from torch.utils.data import DataLoader\n",
    "from torchmetrics import MeanSquaredError\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "from expt1 import X, y, collate_fn, device\n",
    "from expt3 import model\n",
    "\n",
    "pd.options.plotting.backend = \"plotly\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.eval()\n",
    "\n",
    "checkpoints = tuple(Path(\"checkpoints\").glob(\"*.ckpt\"))\n",
    "\n",
    "test = DataLoader(\n",
    "    list(zip(X.values(), y.values())),\n",
    "    collate_fn=collate_fn,\n",
    "    shuffle=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cf4c681d9d5b4bb5aa1aef4f544bf2ef",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Evaluating checkpoints:   0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "462ba8b04a7b4e25a108bf31b9d358b0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Evaluating samples:   0%|          | 0/81 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluate all checkpoints from expt3 on all samples\n",
    "sample_df = pd.DataFrame(columns=[\"sample\"], data=X.keys()).set_index(\"sample\")\n",
    "overall_df = []\n",
    "with torch.no_grad():\n",
    "    for checkpoint in tqdm(checkpoints, desc=\"Evaluating checkpoints\"):\n",
    "        model.load_state_dict(torch.load(checkpoint)[\"state_dict\"])\n",
    "        y_ground = []\n",
    "        y_eval = []\n",
    "        for sample_X0, sample_X1, sample_y in tqdm(\n",
    "            test, desc=\"Evaluating samples\", leave=False\n",
    "        ):\n",
    "            y_ground.append(sample_y.to(\"cpu\"))\n",
    "            y_eval.append(\n",
    "                model(sample_X0, sample_X1).to(\"cpu\")\n",
    "            )  # Move to CPU because otherwise will run out of VRAM\n",
    "            torch.cuda.empty_cache()  # empty cache to ensure maximum VRAM available\n",
    "        y_eval, y_ground = torch.stack(y_eval), torch.stack(y_ground)\n",
    "        sample_df[checkpoint.name] = [\n",
    "            nn.MeanSquaredError()(ye, yg).item() for ye, yg in zip(y_eval, y_ground)\n",
    "        ]\n",
    "        overall_df.append(\n",
    "            (\n",
    "                model.__name__,\n",
    "                checkpoint.name,\n",
    "                MeanSquaredError()(y_eval, y_ground).item(),\n",
    "            )\n",
    "        )\n",
    "    overall_df = pd.DataFrame(columns=[\"model\", \"checkpoint\", \"loss\"], data=overall_df)\n",
    "    torch.cuda.empty_cache()\n",
    "\n",
    "overall_df.to_csv(\"expt3_overall.csv\")\n",
    "sample_df.to_csv(\"expt3_sample.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# checkpoint = \"checkpoints/checkpoint-epoch=12-val_loss=0.00.ckpt\"\n",
    "# model.load_state_dict(torch.load(checkpoint)[\"state_dict\"])\n",
    "y_ground = []\n",
    "y_eval = []\n",
    "with torch.no_grad():\n",
    "    for sample_X0, sample_X1, sample_y in tqdm(\n",
    "        test, desc=\"Evaluating samples\", leave=False\n",
    "    ):\n",
    "        y_ground.append(sample_y.to(\"cpu\"))\n",
    "        y_eval.append(\n",
    "            model(sample_X0, sample_X1).to(\"cpu\")\n",
    "        )  # Move to CPU because otherwise will run out of VRAM\n",
    "        torch.cuda.empty_cache()  # empty cache to ensure maximum VRAM available\n",
    "    y_eval, y_ground = torch.stack(y_eval), torch.stack(y_ground)\n",
    "    sample_accuracy = [\n",
    "        MeanSquaredError()(ye, yg).item() for ye, yg in zip(y_eval, y_ground)\n",
    "    ]\n",
    "    overall_accuracy = MeanSquaredError()(y_eval, y_ground).item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame()\n",
    "df[\"sample\"] = [\"Overall\"] + list(str(i) for i in range(len(sample_accuracy)))\n",
    "df[\"mean_squared_error\"] = [overall_accuracy] + sample_accuracy\n",
    "df[\"y_ground_Ni\"] = [None] + [y[0][0].item() for y in y_ground]\n",
    "df[\"y_ground_Ti\"] = [None] + [y[0][1].item() for y in y_ground]\n",
    "df[\"y_eval_Ni\"] = [None] + [y[0][0].item() for y in y_eval]\n",
    "df[\"y_eval_Ti\"] = [None] + [y[0][1].item() for y in y_eval]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
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           "bgcolor": "#E5ECF6",
           "caxis": {
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            "ticks": ""
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          },
          "title": {
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          "xaxis": {
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           "ticks": "",
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          "yaxis": {
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      }
     },
     "metadata": {},
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   ],
   "source": [
    "df.plot(\"sample\", \"mean_squared_error\", kind=\"bar\", title=\"Error by Sample\")"
   ]
  }
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