evaluate.ipynb

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  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "import argparse\n",
    "import pandas as pd\n",
    "import torch\n",
    "\n",
    "from SuperResolution.models import calculate_test_loss, load_model, check_performance\n",
    "\n",
    "\n",
    "from SuperResolution.utils import load_config, copy_config_file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": [
    "def clean_tensor_values(value):\n",
    "    if isinstance(value, torch.Tensor):\n",
    "        return value.item()\n",
    "    return value"
   ]
  },
  {
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      "Config file path: models/x2_lvl6/factor_2.yaml\n",
      "model_epoch_counter_0000000220_data_time_1722479563.pth\n",
      "loaded model from models/x2_lvl6/model_epoch_counter_0000000220_data_time_1722479563.pth\n",
      "dx 0.0625\n",
      "Loading data\n"
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      "Evaluating model\n"
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      "Config file path: models/x1_lvl5/factor_0.yaml\n",
      "model_epoch_counter_0000000680_data_time_1722519225.pth\n",
      "loaded model from models/x1_lvl5/model_epoch_counter_0000000680_data_time_1722519225.pth\n",
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      "Config file path: models/x1_lvl6/factor_0.yaml\n",
      "model_epoch_counter_0000000080_data_time_1722517755.pth\n",
      "loaded model from models/x1_lvl6/model_epoch_counter_0000000080_data_time_1722517755.pth\n",
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      "model_epoch_counter_0000000020_data_time_1722519320.pth\n",
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   ],
   "source": [
    "metrics = []\n",
    "for name in os.listdir(\"models\"):\n",
    "    net, config = load_model(os.path.join(\"models\", name))\n",
    "    metric = calculate_test_loss(net, config, name)\n",
    "    for i in [2, 3, 4, 5, 6]:\n",
    "        # Check performance\n",
    "        L2Hmodelerror, L2Hinterp = check_performance(\n",
    "            net=net,\n",
    "            config=config,\n",
    "            datafolder=f\"/home/thelfer1/scr4_tedwar42/thelfer1/high_end_data_{i}/outputXdata_level{config['res_level']}_step0001.dat\",\n",
    "        )\n",
    "        factor = L2Hinterp / L2Hmodelerror\n",
    "        metric[f\"factor{i}\"] = factor\n",
    "        metric[f\"L2Hinterp{i}\"] = L2Hinterp\n",
    "        metric[f\"L2Hmodelerror{i}\"] = L2Hmodelerror\n",
    "    metrics.append(metric)\n",
    "    df = pd.DataFrame(metrics)\n",
    "    df = df.applymap(clean_tensor_values)\n",
    "    df.to_csv(\"metrics_results.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(metrics)\n",
    "df = df.applymap(clean_tensor_values)\n",
    "df.to_csv(\"metrics_results.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def format_float(val, col_name):\n",
    "    if \"factor\" in col_name:\n",
    "        return f\"{val:.2f}\"\n",
    "    return f\"{val:.2e}\"\n",
    "\n",
    "\n",
    "# Create a custom format function for the LaTeX conversion\n",
    "def custom_latex_format(df):\n",
    "    formatted_latex = df.to_latex(\n",
    "        index=False,\n",
    "        float_format=lambda x: f\"{x:.2e}\",\n",
    "        column_format=\"|\".join([\"l\"] + [\"r\"] * len(df.columns)),\n",
    "    )\n",
    "    for col in df.columns:\n",
    "        if \"factor\" in col:\n",
    "            formatted_latex = formatted_latex.replace(\n",
    "                f\"{df[col][0]:.2e}\", f\"{df[col][0]:.2f}\"\n",
    "            )\n",
    "            formatted_latex = formatted_latex.replace(\n",
    "                f\"{df[col][1]:.2e}\", f\"{df[col][1]:.2f}\"\n",
    "            )\n",
    "    return formatted_latex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{l|r|r|r|r|r|r|r|r}\n",
      "\\toprule\n",
      "config_name & total_loss_val & interp_val & factor2 & factor3 & factor4 & factor5 & factor6 \\\\\n",
      "\\midrule\n",
      "x2_lvl6 & 8.86e-08 & 1.48e-06 & 0.58 & 2.09 & 5.79 & 0.11 & 0.04 \\\\\n",
      "x1_lvl5 & 3.34e-08 & 1.94e-07 & 0.09 & 0.12 & 0.15 & 0.05 & 0.03 \\\\\n",
      "x1_lvl6 & 8.23e-08 & 1.23e-06 & 3.52e-02 & 7.69e-02 & 1.28e-01 & 8.98e-03 & 2.85e-03 \\\\\n",
      "x2_lvl5 & 9.10e-08 & 6.07e-07 & 6.77e-01 & 2.17e+00 & 3.92e+00 & 1.53e-01 & 6.10e-02 \\\\\n",
      "x1_lvl7 & 8.17e-07 & 5.67e-06 & 1.64e-02 & 3.45e-02 & 5.59e-02 & 4.52e-03 & 1.57e-03 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Function to clean tensor values\n",
    "columns_to_drop = [\n",
    "    \"L1Loss_val_interp\",\n",
    "    \"L1Loss_val\",\n",
    "    \"loss_hard_base\",\n",
    "    \"Ham_loss\",\n",
    "    \"Ham_loss_interp\",\n",
    "]\n",
    "for i in [2, 3, 4, 5, 6]:\n",
    "    columns_to_drop.append(f\"L2Hinterp{i}\")\n",
    "    columns_to_drop.append(f\"L2Hmodelerror{i}\")\n",
    "df_cleaned = df.drop(columns=columns_to_drop)\n",
    "latex_output = custom_latex_format(df_cleaned)\n",
    "print(latex_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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