{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load model checkpoints to get data\n",
    "CHECKPOINTS_DIR = 'data'\n",
    "CHECKPOINTS_PREFIX = 'epoch'\n",
    "\n",
    "rows = []\n",
    "\n",
    "epoch = 67\n",
    "while True:\n",
    "    ckp_path = os.path.join(CHECKPOINTS_DIR, CHECKPOINTS_PREFIX + str(epoch))\n",
    "    print(ckp_path)\n",
    "    try:\n",
    "        current_state = torch.load(ckp_path, map_location=torch.device(\"cpu\"))\n",
    "    except FileNotFoundError:\n",
    "        break\n",
    "    \n",
    "    rows.append({\"epoch\": epoch, \"train_loss\": current_state[\"train_loss\"].item(), \"test_loss\": current_state[\"test_loss\"], \"avg_cer\": current_state[\"avg_cer\"], \"avg_wer\": current_state[\"avg_wer\"]})\n",
    "    \n",
    "    epoch += 1\n",
    "\n",
    "\n",
    "# create dataframe, then csv from dataframe\n",
    "df = pd.DataFrame(rows)\n",
    "df.to_csv(\"data/losses.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "csv_path = \"losses.csv\"\n",
    "\n",
    "# load csv\n",
    "df = pd.read_csv(csv_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot train_loss and test_loss\n",
    "# do not use colors, distinguis by line style. use solid for train_loss and dashed for test_loss\n",
    "plt.plot(df['epoch'], df['train_loss'], label='train_loss', linestyle='solid', color='black')\n",
    "\n",
    "# create zip with epoch and test_loss for all epochs\n",
    "# filter out all test_loss with value 0.0\n",
    "# plot test_loss\n",
    "epoch_loss = zip(df['epoch'], df['test_loss'])\n",
    "epoch_loss = list(filter(lambda x: x[1] != 0.0, epoch_loss))\n",
    "plt.plot([x[0] for x in epoch_loss], [x[1] for x in epoch_loss], label='test_loss', linestyle='dashed', color='black')\n",
    "\n",
    "# add markers for test_loss\n",
    "for x, y in epoch_loss:\n",
    "    plt.plot(x, y, marker='o', markersize=3, color='black')\n",
    "\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('loss')\n",
    "plt.legend()\n",
    "\n",
    "# add ticks every 5 epochs\n",
    "plt.xticks(range(0, 70, 5))\n",
    "\n",
    "# set y limits to 0\n",
    "plt.ylim(bottom=0)\n",
    "# reduce margins\n",
    "plt.tight_layout()\n",
    "# increase resolution\n",
    "plt.savefig('train_test_loss.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "epoch_cer = zip(df['epoch'], df['cer'])\n",
    "epoch_cer = list(filter(lambda x: x[1] != 0.0, epoch_cer))\n",
    "plt.plot([x[0] for x in epoch_cer], [x[1] for x in epoch_cer], label='cer', linestyle='solid', color='black')\n",
    "\n",
    "# add markers for cer\n",
    "for x, y in epoch_cer:\n",
    "    plt.plot(x, y, marker='o', markersize=3, color='black')\n",
    "    \n",
    "epoch_wer = zip(df['epoch'], df['wer'])\n",
    "epoch_wer = list(filter(lambda x: x[1] != 0.0, epoch_wer))\n",
    "plt.plot([x[0] for x in epoch_wer], [x[1] for x in epoch_wer], label='wer', linestyle='dashed', color='black')\n",
    "\n",
    "# add markers for wer\n",
    "for x, y in epoch_wer:\n",
    "    plt.plot(x, y, marker='o', markersize=3, color='black')\n",
    "    \n",
    "# set y limits to 0 and 1\n",
    "plt.ylim(bottom=0, top=1)\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('error rate')\n",
    "plt.legend()\n",
    "# reduce margins\n",
    "plt.tight_layout()\n",
    "\n",
    "# add ticks every 5 epochs\n",
    "plt.xticks(range(0, 70, 5))\n",
    "\n",
    "# add ticks every 0.1   \n",
    "plt.yticks([x/10 for x in range(0, 11, 1)])\n",
    "\n",
    "# increase resolution\n",
    "plt.savefig('cer_wer.png', dpi=300)"
   ]
  }
 ],
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