src/training_analyses.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'matplotlib'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m/home/jani/ITU/ANLP/z/KDS_AdvancedNLP_FinalProject/src/training_analyses.ipynb Cell 1\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell:/home/jani/ITU/ANLP/z/KDS_AdvancedNLP_FinalProject/src/training_analyses.ipynb#W0sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpyplot\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mplt\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/jani/ITU/ANLP/z/KDS_AdvancedNLP_FinalProject/src/training_analyses.ipynb#W0sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mdatetime\u001b[39;00m \u001b[39mimport\u001b[39;00m datetime\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path = \"../LUMI/logs/train/5088823.output\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cell 3: Read and parse data\n",
    "try:\n",
    "    with open(file_path, 'r') as file:\n",
    "        # Read the content of the file\n",
    "        file_content = file.readlines()\n",
    "\n",
    "    time_list = []\n",
    "    loss_list = []\n",
    "    eval_loss_list = []\n",
    "\n",
    "    for line in file_content:\n",
    "        try:\n",
    "            entry = eval(line)  # Evaluate the string as a Python expression\n",
    "            if 'epoch' in entry:\n",
    "                # Assuming 'epoch' values are already in seconds\n",
    "                epoch_time = entry['epoch']\n",
    "                time_list.append(epoch_time)\n",
    "                loss_list.append(entry.get('loss', 0))\n",
    "                eval_loss_list.append(entry.get('eval_loss', 0))\n",
    "        except (SyntaxError, ValueError):\n",
    "            # Ignore lines that are not valid Python dictionaries\n",
    "            pass\n",
    "\n",
    "except FileNotFoundError:\n",
    "    print(f\"The file '{file_path}' does not exist.\")\n",
    "except Exception as e:\n",
    "    print(f\"An error occurred: {e}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epochs: (0.15, 0.3, 0.45, 0.6, 0.75, 0.9, 1.05, 1.2, 1.35, 1.5, 1.65, 1.8, 1.95, 2.1, 2.25, 2.4, 2.55, 2.7, 2.85, 3.0)\n",
      "Values: (0.2136755883693695, 0.12843108177185059, 0.11791524291038513, 0.11196029931306839, 0.10807397216558456, 0.10495071113109589, 0.10386848449707031, 0.10224119573831558, 0.10033360123634338, 0.09930505603551865, 0.09806004166603088, 0.09683993458747864, 0.09608425199985504, 0.0971342921257019, 0.09673095494508743, 0.09630133956670761, 0.0958235114812851, 0.09551159292459488, 0.0953667014837265, 0.09521330893039703)\n"
     ]
    }
   ],
   "source": [
    "result = [(value, eval_loss_list[i]) for i, value in enumerate(time_list) if eval_loss_list[i] != 0]\n",
    "epochs, evals = zip(*result)\n",
    "\n",
    "print(\"Epochs:\", epochs)\n",
    "print(\"Values:\", evals)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#loss_list = loss_list[:7]\n",
    "#time_list = time_list[:7]\n",
    "#eval_loss_list = eval_loss_list[:7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))  # Adjust the figure size as needed\n",
    "\n",
    "plt.plot(time_list, loss_list, label='Training Loss')\n",
    "plt.plot(epochs, evals, label='Eval Loss')\n",
    "\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('100K')\n",
    "plt.legend()\n",
    "\n",
    "# Rotate x-axis text\n",
    "plt.xticks(rotation=45, ha='right')  # Adjust the rotation angle and alignment as needed\n",
    "\n",
    "plt.tight_layout()  # Ensures that labels are not cut off\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming you have 'time_list', 'loss_list', 'epochs', and 'evals' defined somewhere in your code\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=(12, 6))\n",
    "\n",
    "# Plot the training loss on the left y-axis\n",
    "ax1.plot(time_list, loss_list, label='Training Loss', color='blue')\n",
    "ax1.set_xlabel('Epoch')\n",
    "ax1.set_ylabel('Training Loss', color='blue')\n",
    "ax1.tick_params('y', colors='blue')\n",
    "\n",
    "# Create a twin Axes sharing the xaxis\n",
    "ax2 = ax1.twinx()\n",
    "\n",
    "# Plot the eval loss on the right y-axis\n",
    "ax2.plot(epochs, evals, label='Eval Loss', color='red')\n",
    "ax2.set_ylabel('Eval Loss', color='red')\n",
    "ax2.tick_params('y', colors='red')\n",
    "\n",
    "# Combine legends from both axes\n",
    "lines, labels = ax1.get_legend_handles_labels()\n",
    "lines2, labels2 = ax2.get_legend_handles_labels()\n",
    "ax2.legend(lines + lines2, labels + labels2, loc='upper left')\n",
    "\n",
    "plt.title('100K')\n",
    "\n",
    "# Rotate x-axis text\n",
    "plt.xticks(rotation=45, ha='right')  # Adjust the rotation angle and alignment as needed\n",
    "\n",
    "plt.tight_layout()  # Ensures that labels are not cut off\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))  # Adjust the figure size as needed\n",
    "\n",
    "plt.plot(epochs, evals, label='Eval Loss',color='red')\n",
    "\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('100K')\n",
    "plt.legend()\n",
    "\n",
    "# Rotate x-axis text\n",
    "plt.xticks(rotation=45, ha='right')  # Adjust the rotation angle and alignment as needed\n",
    "\n",
    "plt.tight_layout()  # Ensures that labels are not cut off\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path = \"../LUMI/logs/train/5088840.output\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cell 3: Read and parse data\n",
    "try:\n",
    "    with open(file_path, 'r') as file:\n",
    "        # Read the content of the file\n",
    "        file_content = file.readlines()\n",
    "\n",
    "    time_list = []\n",
    "    loss_list = []\n",
    "    eval_loss_list = []\n",
    "\n",
    "    for line in file_content:\n",
    "        try:\n",
    "            entry = eval(line)  # Evaluate the string as a Python expression\n",
    "            if 'epoch' in entry:\n",
    "                # Assuming 'epoch' values are already in seconds\n",
    "                epoch_time = entry['epoch']\n",
    "                time_list.append(epoch_time)\n",
    "                loss_list.append(entry.get('loss', 0))\n",
    "                eval_loss_list.append(entry.get('eval_loss', 0))\n",
    "        except (SyntaxError, ValueError):\n",
    "            # Ignore lines that are not valid Python dictionaries\n",
    "            pass\n",
    "\n",
    "except FileNotFoundError:\n",
    "    print(f\"The file '{file_path}' does not exist.\")\n",
    "except Exception as e:\n",
    "    print(f\"An error occurred: {e}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epochs: (0.11, 0.21, 0.32, 0.43, 0.54, 0.64, 0.75, 0.86, 0.96, 1.07, 1.18, 1.29, 1.39, 1.5, 1.61, 1.71, 1.82, 1.93, 2.04, 2.14, 2.25, 2.36, 2.46, 2.57, 2.68, 2.79, 2.89, 3.0)\n",
      "Values: (0.10708379000425339, 0.09541499614715576, 0.08930520713329315, 0.0846906527876854, 0.08152671158313751, 0.07885902374982834, 0.07662069797515869, 0.07468736171722412, 0.0730019062757492, 0.07204516232013702, 0.0709744468331337, 0.0698758214712143, 0.06905165314674377, 0.06793466955423355, 0.06732290983200073, 0.06632260233163834, 0.06552444398403168, 0.06482412666082382, 0.06518623977899551, 0.06452923268079758, 0.06426353752613068, 0.0638389065861702, 0.06345319002866745, 0.06303569674491882, 0.06280268728733063, 0.06255515664815903, 0.06239980459213257, 0.06232231482863426)\n"
     ]
    }
   ],
   "source": [
    "result = [(value, eval_loss_list[i]) for i, value in enumerate(time_list) if eval_loss_list[i] != 0]\n",
    "epochs, evals = zip(*result)\n",
    "\n",
    "print(\"Epochs:\", epochs)\n",
    "print(\"Values:\", evals)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#loss_list = loss_list[:12]\n",
    "#time_list = time_list[:12]\n",
    "#eval_loss_list = eval_loss_list[:12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))  # Adjust the figure size as needed\n",
    "\n",
    "plt.plot(time_list, loss_list, label='Training Loss')\n",
    "plt.plot(time_list, eval_loss_list, label='Eval Loss')\n",
    "\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('700K')\n",
    "plt.legend()\n",
    "\n",
    "# Rotate x-axis text\n",
    "plt.xticks(rotation=45, ha='right')  # Adjust the rotation angle and alignment as needed\n",
    "\n",
    "plt.tight_layout()  # Ensures that labels are not cut off\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming you have 'time_list', 'loss_list', 'epochs', and 'evals' defined somewhere in your code\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=(12, 6))\n",
    "\n",
    "# Plot the training loss on the left y-axis\n",
    "ax1.plot(time_list, loss_list, label='Training Loss', color='blue')\n",
    "ax1.set_xlabel('Epoch')\n",
    "ax1.set_ylabel('Training Loss', color='blue')\n",
    "ax1.tick_params('y', colors='blue')\n",
    "\n",
    "# Create a twin Axes sharing the xaxis\n",
    "ax2 = ax1.twinx()\n",
    "\n",
    "# Plot the eval loss on the right y-axis\n",
    "ax2.plot(epochs, evals, label='Eval Loss', color='red')\n",
    "ax2.set_ylabel('Eval Loss', color='red')\n",
    "ax2.tick_params('y', colors='red')\n",
    "\n",
    "# Combine legends from both axes\n",
    "lines, labels = ax1.get_legend_handles_labels()\n",
    "lines2, labels2 = ax2.get_legend_handles_labels()\n",
    "ax2.legend(lines + lines2, labels + labels2, loc='upper left')\n",
    "\n",
    "plt.title('100K')\n",
    "\n",
    "# Rotate x-axis text\n",
    "plt.xticks(rotation=45, ha='right')  # Adjust the rotation angle and alignment as needed\n",
    "\n",
    "plt.tight_layout()  # Ensures that labels are not cut off\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))  # Adjust the figure size as needed\n",
    "\n",
    "plt.plot(epochs, evals, label='Eval Loss', color='red')\n",
    "\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('100K')\n",
    "plt.legend()\n",
    "\n",
    "# Rotate x-axis text\n",
    "plt.xticks(rotation=45, ha='right')  # Adjust the rotation angle and alignment as needed\n",
    "\n",
    "plt.tight_layout()  # Ensures that labels are not cut off\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}