focal_loss_visual.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a3851134-a315-4399-81d1-690958263aae",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "ced01c21-7bb4-46ec-a48a-14d6d04d4d4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ce_loss(y, y_hat):\n",
    "    def f(y, y_hat):\n",
    "        return -y * np.log(y_hat)\n",
    "    return f(1-y, 1-y_hat) + f(y, y_hat)\n",
    "\n",
    "\n",
    "def focal_loss(y, y_hat, alpha=0.25, gamma=2, reduce=None):\n",
    "    p = y_hat\n",
    "    targets = y\n",
    "    ce_loss_val = ce_loss(y, y_hat)\n",
    "    p_t = p * targets + (1 - p) * (1 - targets)\n",
    "    loss = ce_loss_val * ((1 - p_t) ** gamma)\n",
    "    # loss = ce_loss_val * (np.sqrt(1-p_t))\n",
    "\n",
    "    if alpha >= 0:\n",
    "        alpha_t = alpha * targets + (1 - alpha) * (1 - targets)\n",
    "        loss = alpha_t * loss\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7eca4a4b-f282-4206-ab3f-a0fb1f255760",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = np.array([0] * 5 + [1] * 5)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "6c29e5c7-635f-444a-9838-1a46e62eb85e",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat = np.array(\n",
    "    [\n",
    "        0.512, 0.534, 0.3, 0.1, 0.05,\n",
    "        0.492, 0.487, 0.7, 0.6, 0.9\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "6c90b7cb-aec0-4f68-9a43-097012bc2c53",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = pd.DataFrame(\n",
    "    {\n",
    "        'ce_loss': ce_loss(y, y_hat),\n",
    "        'focal_loss': focal_loss(y, y_hat)\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "8a416cd2-f580-47b2-9564-7f4a08f41ce1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "b384d552-9d69-49bf-afb5-d63acdc55066",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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u29bV1VURERGF2mdOnn32WVksFsXExMjPzy/Lei8vLz3//PN2Ff1vv/1WPXr0UOXKleXp6amIiAg98cQTSklJsdt24MCB8vX1VVxcnDp37ixfX19VrFhR8+fPl3T5ryTt27dXmTJlVKVKFbu/+EvSW2+9JYvFou+++06jRo1S+fLlFRgYqGHDhiktLU3nzp1T//79FRQUpKCgII0bNy7LrSivvPKKWrdurbJly8rb21tNmzbVqlWr8vVeXe23337Tb7/9pqFDh8rN7Z+LLx999FEZY647zkcffaT09HQ9+uijtmUWi0XDhw/XX3/9pS1bttiWr169Wp07d1blypVty6KionTTTTfpvffeK9I+SxL5RD6RT4WroPskt9v7+PiofPnyhX6cVK1aVcnJyUpLS7tu26JGPpFP5FP+nDt3TkuXLtXQoUNVrVo1paWlKTU1Ndfbf/nll/rll180depUeXt7Kzk5WRkZGdm2nT17tlq0aKH/+7//k9VqVVJSUoH7vFpqaqpWr16tdu3aqVKlSrn+PgoLWUQWkUX5FxERcc0r1jMV9NhbtWqVmjdvrubNm9uW1a5dW3fccYfd710bNmzQ33//bfe7nCSNGDFCSUlJ+vTTT4u0z1zJU/mrGNSuXdsMHjzYGGPMpk2bslT5Mumqqv7UqVONJNOgQQPTpUsXM2/ePPPggw8aSaZfv35Ztm3UqJEJDw83zz//vJkzZ46pXr268fHxMadPn7a1e//9902jRo3MlClTzOLFi83TTz9tgoKCTJUqVUxSUpKtXUZGhomPj8/VKy0tzbZdVFSUqVOnTpbv7auvvjKSzMcff5yr9+zs2bPm1KlT5qeffjIPPfSQkWQWL16cpV16erqJj483R48eNV988YWpXbu28fPzM3///XeWthcuXDDx8fFm//79Zvbs2cbV1dX06dMn2/FPnz5tTp48abZv3266dOliJJkvv/wyV3O/UlJSknFzczNRUVF52u6xxx4z99xzj5k+fbp5/fXXzeDBg42rq6vp3r27XbsBAwYYLy8vU7duXfPII4+Y+fPnm9atWxtJZunSpaZChQpm7Nix5rXXXjP16tUzrq6udn+pW7p0qZFkGjdubO6++24zf/58069fPyPJjBs3zrRt29b06dPHLFiwwHTu3NlIMsuWLbObQ6VKlcyjjz5q5s2bZ2bPnm1atGhhJJlPPvnErt25c+dydTwlJibatlm+fLmRZLZu3ZrlPapUqZK57777rvk+Pvzww6ZMmTLGarXaLd+/f7+RZF599VVjzOWrriSZf//731n6ePDBB01wcHCR9lmSyCfyiXzKXz5lyst+zk5qaqqJj483cXFxZs2aNSYsLMxUqVLF7qrUTOfPnzfx8fEmNjbWTJw40UgyTz/9dJZ2VqvVxMfHm+PHj5tNmzaZ1q1bG1dX12yvak1OTjbx8fHm4MGD5q233jJlypQxrVu3ztXcixr5RD6RT/nLp//973+2fX///fcbV1dXY7FYTOvWrc2uXbuu+z4++eSTRpJZv369adq0qZFkPDw8TK9eveyOkfPnzxuLxWJGjBhhJk6caHx9fY0kU61aNbNy5cp89ZmdNWvWGEkmOjr6unMvCmQRWUQWFexcKdO1rtAqyLGXkZFhPD09zfDhw7OsmzRpkpFku7ruhRdeMJLMyZMn7dqlpqYaFxcXM2bMmCLrM7ccqqCVeYnjunXrjDGXTzIrVapkRo8enaVtTiHYtWtXu3aPPvqokWT27Nljt62Hh4fZv3+/bdmePXuMJPPaa6/ZlmV3ie+WLVuMJPPf//7XtuzgwYNGUq5eGzZssG1Xr1490759+yxj/Prrr0aSWbRoUc5v1hVq1apl69/X19dMmjTJdqlidnPPfNWqVctuPlcaNmyYrZ2Li4vp3r17jpf/eXp62tqWLVvWVqTIq8x98Pjjj2dZ9/fff9v98KemptrWZbefZsyYYSwWi93tawMGDDCSzPTp023Lzp49a7y9vY3FYjErVqywLd+7d2+WYywzBDt06GBXoGnVqpWxWCzmkUcesS27dOmSqVSpUpYQunquaWlppn79+lmOg3bt2uXqeBowYIBtm5dfftlIMnFxcVnej+bNm5t//etfWZZfqVOnTqZ69epZliclJRlJZsKECcYYY7Zv357lZyDT2LFjjSRz8eLFIuuzpJBPl5FP5FN+8ilTXvZzdt5991277Zs1a2Z++umnbNt26NDB1s7Dw8MMGzbMpKSkZGl3/Phxuz4rVaqU5ZfLTDNmzLBre8cdd2SbucWNfLqMfCKf8pNPs2fPtu2DFi1amJiYGLNgwQITGhpqgoKCzLFjx7K8T1fq2rWrbfu+ffuaVatWmcmTJxs3NzfTunVr2/e8c+dOW7vQ0FCzYMECExMTY1q0aGEsFov5/PPP89xndu6//37j6elpzp49e815FwWy6DKyiCwqyLlSpmsVtApy7MXHxxtJ5rnnnsuybv78+UaS2bt3rzHGmBEjRhhXV9ds+ylfvrzp3bt3kfWZWw71ofAxMTEKDQ3V7bffLunyrUm9evXS8uXLNWvWrFx9cNyIESPsvn7ssce0YMECffbZZ2rYsKFteVRUlGrUqGH7umHDhvL397f7FH5vb2/b/6enpyshIUE1a9ZUYGCgdu7cqX79+kmSwsLCcv1EgSs/3C8lJUWenp5Z2nh5ednW58bSpUuVkJCgP//8U0uXLlVKSooyMjKyXK5Yt25drVu3TklJSdq8ebO++uqrHJ8u9fjjj6t79+46duyY3nvvPWVkZOR4S8Xnn3+uixcvKjY2VsuXL8/x8unrSUhIkCT5+vpmWVe9enWdP3/e9vX777+v7t27S7LfT0lJSUpJSVHr1q1ljNGuXbvsbmGTpIcfftj2/4GBgapVq5b279+vnj172pbXqlVLgYGB2T6VYfDgwbJYLLavW7ZsqS1btmjw4MG2Za6urmrWrJl+/PFHu22vnOvZs2eVkZGhW265JctT/2bNmqWzZ89mGftqV35YY+bxktMxlfn+5iS3x+P1xrmyr6Los6SQT5eRT+RTfvIpU172c3Zuv/12rVu3TufOndP69eu1Z8+eHPfpzJkz9eSTT+rIkSNatmyZ0tLSsn3ia3BwsNatW6eLFy9q165dWrNmTY5zeuCBB9SsWTPFx8frk08+0cmTJ3P9s1CUyKfLyCfyKT/5lLkvLRaL1q9fb3sfb775ZrVq1Urz58/XCy+8kGNfmds3b95cy5cvlyTdf//98vHx0cSJE7V+/XpFRUXZ2v3999/64YcfbE8A7Nq1q6pVq6YXXnjB9tCK3PZ5tYSEBH366ae65557FBgYeN33obCRRZeRRWRRQc6VcqMgx15uf+/K/G9OD+by8vLK8+9yeekztxymoJWRkaEVK1bo9ttv18GDB23LW7ZsqVmzZmn9+vW5elJHZGSk3dc1atSQi4tLlnulr/7BkKSgoCC7Ay8lJUUzZszQ0qVLdfToUbt7aK/8gfTy8sr2H5Xr8fb2zvYe/cz7xK/8gbmWVq1a2f6/d+/eqlOnjqTL9/heyd/f3zbPbt266Z133lG3bt20c+dOu3CWLt/vWrt2bUlS//79ddddd6lLly7aunWrXQBIsv2j1bFjR3Xr1k3169eXr6+vRo4cmav5Z8q8zzq7YM78LKY9e/boqaeeslsXFxenKVOm6OOPP84SHFfuJ+nyvipfvrzdsoCAAFWqVCnL9xUQEJBtEF197AQEBEi6fM/z9bb/5JNP9MILL2j37t12+/7qsZs2bZpl3OvJPF5yOqaudzzl9ni83jhXty3sPksC+fQP8ol8yk8+ZcrLfs5OaGioQkNDJUndu3fX9OnTdeedd2rfvn0KCwuza9u4cWPb/z/44INq0qSJ7YlAV/Lw8LDNqXPnzrrjjjvUpk0bhYSEqHPnznZtq1SpoipVqki6XNwaOnSooqKi9Pvvv5dYRpFP/yCfyKeCnD916dLF7hfxf/3rX6pWrZo2b96cq+0feOABu+V9+vTRxIkTtXnzZkVFRdnaVatWzVbMki7/8t+lSxctX75cly5dkpubW677vNrq1at18eJF9e3bN7fffqEhi/5BFpFFBTlXyo2CHHt5/V0up0Lolb9fFkWfueUwBa2vv/5ax48f14oVK7RixYos62NiYvL16NmrD65MOf2F4Mqge+yxx7R06VI9/vjjatWqlQICAmSxWNS7d29ZrVZbu4yMDMXHx+dqPsHBwbaKZHh4uI4ePZqlzfHjxyXlr2IbFBSk9u3bKyYmJksIXu2+++5Tv379tGLFiuv+ItG9e3cNGzZMf/zxh2rVqpVjuxo1aujmm29WTExMnkOwZs2acnNz0y+//JJlXbt27STJ7sPOpcvv/Z133qkzZ85o/Pjxql27tsqUKaOjR49q4MCBdvtJynm/5+Z4yE8fV27/7bffqmvXrrr11lu1YMEChYeHy93dXUuXLs3yoYVnzpzJ1YcMe3t720I4PDxc0uXj5+pAPn78uFq0aHHNvsLDw7VhwwYZY+x+bq4+Hq8c52rHjx9XcHCwrTpfFH2WBPLpH+QT+ZSffMpJXvZzdrp3765nnnlGH330kYYNG5ZjOw8PD3Xt2lUzZ85USkrKNU+WWrdurfDwcMXExGQpaGU3fnR0tDZt2qQOHTrkef6FgXz6B/lEPuUnnzKPl8xi+ZVCQkKue5VFTtuHhIRIkm37642Tnp6upKQkBQQE5LrPq8XExCggIOC62VUUyKJ/kEVkUWGeK2WnIMde5u9VOf3edeX24eHhysjI0KlTp2z5I0lpaWn6+++/be2Kos/ccpiCVkxMjEJCQmxPKbjSmjVr9MEHH2jRokXXrdjt27dP1apVs329f/9+Wa1W2xMb8mLVqlUaMGCAZs2aZVt28eLFLE99OHLkiN2Y17Jhwwbddtttki7/9XjDhg1KSEiQv7+/rc3WrVtt6/MjJSUlSzU7O6mpqbJarblqm3npX27b5uXpMJnKlCmj2267Td98842OHj2qihUrXnebn3/+WX/88YeWLVum/v3725bn9rLh4rR69Wp5eXnpiy++sCvOLF26NEvb++67T9988811+xwwYIDeeustSf8cLzt27LArXh07dkx//fWX3VMwstO4cWO98cYbio2NVd26dW3Lrz4eK1asqPLly2vHjh1Z+ti2bZvdcVsUfZYE8ol8Ip/+kZ98ykle9nN28rrvjTFKTEy87s/qxYsXC/3YKyrkE/lEPv0jP/mUeSVFdr8cHjt2zHaVS06aNm2q6OjoLNsfO3ZMkmxXk1SoUEFhYWE5juPl5WW7wiW3fV7p+PHj2rBhgwYOHFgifwQki8gisugfhXmulJ2CHHsuLi5q0KBBtr93bd26VdWrV7dl0ZW/X95zzz22djt27JDVarWtL4o+c8shClopKSlas2aNevToYbuX9koVKlTQu+++q48//li9evW6Zl/z58+3q/6/9tprki5fQplXrq6uWaq6r732WpbH5ub3vuvu3bvrlVde0eLFi22XXqampmrp0qVq2bKl3VU2cXFxSk5OtvtH9eqqpiQdOnRI69evV7NmzWzLzp07pzJlysjd3d2u7RtvvCFJdm2z6zM9PV3//e9/5e3tbStKXLp0SYmJiQoKCrJru23bNv3888/q06dPrt6Pq02ZMkW33XabHnzwQf3vf//Lcg/21fsjs5J+5XJjjObOnZuv8YuSq6urLBaL3fFz6NAhffjhh1na5ue+63r16ql27dpavHixhg0bZntvFi5cKIvFYvezdf78eR0/flzh4eG2vwp069ZNTzzxhBYsWKB58+ZJuvxeLlq0SBUrVlTr1q1t299///1atmyZjhw5YjtO169frz/++ENPPPGErV1R9FncyCfyKRP5dFl+8ikv+zk5OVlxcXEqV66cypUrJ0k6ffq0ypYtm+Uv9bk9Ts6dO6fVq1crIiLCti4pKUkWi0U+Pj52bVevXq2zZ8/a9RkfH5/tL5BLliyRxWJRkyZNrvNuFA3yiXzKRD5dlp98qlWrlho1aqSPPvpIp0+ftuXOl19+qSNHjuixxx6ztc3p/Gn06NFaunSpBg4caPvco8zj5M4777Rt36tXL82dO1fr1q2zLT99+rQ++ugjtW/f3rZtXvrMtGLFClmt1hK53ZAsIosykUWXFfVnaBX02OvevbsmTJigHTt22I6f33//XV9//bXdLaHt27dXcHCwFi5caFd8WrhwoXx8fNSpU6ci7TM3HKKg9fHHHysxMVFdu3bNdv2//vUvlS9fXjExMdcNwYMHD6pr1666++67tWXLFi1fvlx9+vTJ160MnTt31ttvv62AgADVrVtXW7Zs0VdffaWyZcvatcvvfdctW7ZUjx49NHHiRJ06dUo1a9bUsmXLdOjQIS1ZssSubf/+/fXNN9/Y/bA3aNBAd9xxhxo3bqygoCDt27dPS5YsUXp6umbOnGlrt3HjRo0aNUrdu3dXZGSk0tLS9O2332rNmjVq1qyZHnzwQVvbYcOGKSEhQbfeeqsqVqyoEydOKCYmRnv37tWsWbNsoXThwgVFRESoV69eqlevnsqUKaOff/5ZS5cuVUBAgCZPnmw3/8xqfXaXfV7plltu0bx58/TYY48pMjJSffv2Ve3atZWWlqY//vhDMTEx8vDwsH1WSu3atVWjRg099dRTOnr0qPz9/W2/jDiaTp06afbs2br77rvVp08fnTp1SvPnz1fNmjX1008/2bXN733XL7/8srp27aq77rpLvXv31i+//KJ58+bp4Ycftt2PL0kffPCBBg0aZDtRkqRKlSrp8ccf18svv6z09HQ1b95cH374ob799lvFxMTYXYb79NNP6/3339ftt9+u0aNH68KFC3r55ZfVoEEDDRo0yNauKPosbuQT+ZSJfLosP/mUl/28bds23X777Zo6daqmTZsmSVq+fLkWLVqke++9V9WrV1diYqK++OILrVu3Tl26dFH79u1t23fs2FGVKlVSy5YtFRISori4OC1dulTHjh3TypUrbe327dunqKgo9erVS7Vr15aLi4t27Nih5cuXq2rVqho9erSt7Ysvvqjvv/9ed999typXrqwzZ85o9erV2r59ux577DHVrFkzz+9JYSCfyKdM5NNl+T1/+s9//qM777xTbdu21bBhw3T+/HnNnj1bN910k4YPH25rl935U1hYmJ555hlNmTJFd999t+69917t2bNH0dHReuCBB9S8eXPb9hMnTtR7772n+++/X2PGjFFAQIAWLVqk9PR0TZ8+3dYuL31miomJUYUKFWxXDxUnsogsykQWXZbfLNq0aZM2bdok6fIf05KSkmwPpbj11lt16623Sir4sffoo48qOjpanTp10lNPPSV3d3fNnj1boaGhevLJJ23tvL299fzzz2vEiBHq0aOHOnTooG+//VbLly/Xiy++qODg4CLtM1fy9EzEItKlSxfj5eVlkpKScmwzcOBA4+7ubk6fPm2MyflRr7/99pvp3r278fPzM0FBQWbkyJFZHtMtyYwYMSLLGFWqVLF7dObZs2fNoEGDTLly5Yyvr6/p0KGD2bt3b5Z2BZGSkmKeeuopExYWZjw9PU3z5s3N2rVrs7TLfPTnlaZOnWqaNWtmgoKCjJubm6lQoYLp3bt3lkeY79+/3/Tv399Ur17deHt7Gy8vL1OvXj0zdepUc+HCBbu27777romKijKhoaHGzc3NBAUFmaioKPPRRx/ZtUtNTTWjR482DRs2NP7+/sbd3d1UqVLFDB482Bw8eDDL/Js2bWrCwsJy/b7s2rXL9O/f31SuXNl4eHiYMmXKmIYNG5onn3zS7hG9xhjz22+/maioKOPr62vKlStnhgwZYnts7NKlS23tBgwYYMqUKZNlrHbt2pl69eplWV6lShXTqVMn29eZj3rdvn27XbvMYy8+Pt5ueXbjLVmyxERGRhpPT09Tu3Zts3TpUtv2heWDDz4wjRs3Np6enqZSpUpm0qRJJi0tza5N5vdy5ftjjDEZGRlm+vTppkqVKsbDw8PUq1fPLF++PNtxfvnlF3PXXXcZHx8fExgYaPr27WtOnDiRpV1R9FmcyCfy6WrkU97lZT9v2LAhy8/Q9u3bTY8ePUzlypWNp6enKVOmjGnSpImZPXu2SU9Pt9t+3rx5pm3btqZcuXLGzc3NlC9f3nTp0sVs2rTJrl18fLwZOnSoqV27tilTpozx8PAwkZGR5vHHH8/yfn355Zemc+fOpkKFCsbd3d34+fmZNm3amKVLl9o9+ru4kU/k09XIp/xbt26d+de//mW8vLxMcHCw6devnzl+/Lhdm5zOn6xWq3nttdfMTTfdZNzd3U1ERES251/GGHPgwAHzf//3f8bf3994e3ub9u3bm23btmVpl5c+9+7daySZMWPGFOxNyCeyiCy6GlmUP5l9Zfe68ufFmIIde8YYc+TIEdO9e3fj7+9vfH19TefOnc2+ffuyndfixYtNrVq1jIeHh6lRo4b5z3/+k+35T1H0eT0WY65TZnUS06ZN07PPPqv4+HjbpcJwDImJiQoODtacOXOyPIoXuBGQT46LfMKNjnxyXOQTbiRkkeMii+DIXEp6Aij9Nm3apIoVK2rIkCElPRUAsEM+AXBU5BMAR0AWwZFR0EKR69Spkw4dOmR7xC0AOAryCYCjIp8AOAKyCI6MghYAAAAAAACcSqn5DC0AAAAAAADcGPJ1hdb8+fNVtWpVeXl5qWXLltq2bds128+ZM0e1atWSt7e3IiIi9MQTT+jixYv5mjAAXAv5BMBRkU8AHBkZBcDp5PWxiCtWrDAeHh7mzTffNL/++qsZMmSICQwMNCdPnsy2fUxMjPH09DQxMTHm4MGD5osvvjDh4eHmiSeeyPMjGQHgWsgnAI6KfALgyMgoAM4oz7cctmzZUs2bN9e8efMkSVarVREREXrsscc0YcKELO1Hjhyp2NhYrV+/3rbsySef1NatW/Xdd99lO0ZqaqpSU1NtX1utVp05c0Zly5aVxWLJy3QBOAFjjBITE1WhQgW5uOT/o/3IJwCFjXwC4MicJaPIJ+DGU1j5dL1Bci01NdW4urqaDz74wG55//79TdeuXbPdJiYmxgQEBJitW7caY4w5cOCAqV27tnnxxRdzHGfq1KlGEi9evG6w15EjR/ISSeQTL168iu1FPvHixcuRX46eUeQTL1437qsg+XQ9bsqD06dPKyMjQ6GhoXbLQ0NDtXfv3my36dOnj06fPq22bdvKGKNLly7pkUce0dNPP53jOBMnTtSYMWNsX58/f16VK1fWkSNH5O/vn5cpA3ACCQkJioiIkJ+fX777IJ8AFAXyCYAjc5aMIp+AG09h5NP15KmglR8bN27U9OnTtWDBArVs2VL79+/X6NGj9fzzz2vy5MnZbuPp6SlPT88sy/39/Qk8oBQr7kvOyScAuUU+AXBkjp5R5BNw4yrKfMpTQatcuXJydXXVyZMn7ZafPHlSYWFh2W4zefJk9evXTw8//LAkqUGDBkpKStLQoUP1zDPPFN29lABuKOQTAEdFPgFwZGQUAGeVp6Tx8PBQ06ZN7T78z2q1av369WrVqlW22yQnJ2cJNFdXV0mSydvn0QNAjsgnAI6KfALgyMgoAM4qz7ccjhkzRgMGDFCzZs3UokULzZkzR0lJSRo0aJAkqX///qpYsaJmzJghSerSpYtmz56tm2++2XY56uTJk9WlSxdb6AFAYSCfADgq8gmAIyOjADijPBe0evXqpfj4eE2ZMkUnTpxQ48aNtXbtWtuHCMbFxdlV6ydNmiSLxaJJkybp6NGjKl++vLp06aIXX3yx8L4LlFqZHzKZkZFR0lNBAbm7uxf5CQ75hOJEPpUerq6ucnNzK9LPeCCfUNwyMjKUnp5e0tNAARVHPklkFIoX51ClQ3Hl07VYjBNcE5qQkKCAgACdP3+eDw28gaSlpen48eNKTk4u6amgEFgsFlWqVEm+vr5Z1jnzz7gzzx35Rz6VPj4+PgoPD5e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      "text/plain": [
       "<Figure size 1200x1200 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "row_num = 4\n",
    "col_num = 4\n",
    "alphas = np.linspace(0, 1, row_num)\n",
    "gammas = np.linspace(0, 10, col_num)\n",
    "\n",
    "fig, axs = plt.subplots(row_num, col_num, figsize=(12, 12))\n",
    "\n",
    "for i in range(row_num):\n",
    "    for j in range(col_num):\n",
    "        ax = axs[i, j]\n",
    "        alpha = alphas[i]\n",
    "        gamma = gammas[j]\n",
    "        result = pd.DataFrame(\n",
    "            {\n",
    "                'ce_loss': ce_loss(y, y_hat),\n",
    "                'focal_loss': focal_loss(y, y_hat, alpha, gamma)\n",
    "            }\n",
    "        )\n",
    "        result.plot.bar(ax=ax)\n",
    "        ax.set_title(f'Alpha={alpha:.4f}, Gamma={gamma:.4f}')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e720345-2b15-488f-a6a6-42959c808316",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}