EDA/eda.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.datasets import mnist\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "plt.rcParams.update({'font.size': 12})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_set_x, full_set_y = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000,)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000,)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([5923., 6742., 5958., 6131., 5842., 5421., 5918., 6265., 5851.,\n",
       "        5949.]),\n",
       " array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(train_labels, bins = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 80.0)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rWLduHelYZs2otI//P4BfAxdFxJrGwoj4u6Tv5BKZmfVY8+bN47DDDuOaa67hBz/4ARMnTmTGjBnsuuuuDBo0iLVr17J48WIGDx4MwBNPPMGwYcOarefII4/krbfealZ+xRVXcPzxx7cbhyROOOEEJHH22WczadKkNuvPnTuXU089lVmzZjFt2jQuvvhiHnnkkQ7H895773HQQQexaNEizjnnHA455JB2Y+1JKk38nwLWRMR7AJK2ALaJiLcj4sbcojOzHuedd95h5cqVXHDBBUCWfCdOnEhdXR0Affr04cUXX2TIkCHvt4SfeOIJDjjggGbrKk+6nTFz5kwGDhzI8uXLGTNmDPvssw9HHXVUi3XXrVvHihUruOiiiwAYMWIEK1as6FQ8ffr0oVQqsXLlSsaNG8f8+fObHX/oySrt478f6Fc2v20qM7OCWbBgASNHjmSLLbL0MXfu3PdbvEuWLGH33Xdn3rx5myT6hoaGFhP/kUceyYgRI5o97r+/svQycOBAAHbbbTfGjRvH7NmzW6371FNPsffee7PVVlsB8NhjjzF8+KYnHnY0nv79+3PMMce0evyhp6q0xb9NRLw/mElErJK0bU4xmVkPNm/evE0SZnk3zty5cxk2bBivv/46/fv3B2DhwoXce++9XHPNNc3WtTkt/tWrV7NhwwZ22GEHVq9ezbRp0/jud7/7/vLjjjuOG2644f0vh1KpxPPPP8/atWtZt24dl156KVddtekhykriefXVV9lyyy3p378/a9asYfr06Vx44YWd3o9qqDTxr5Y0MiIeA5B0ELCmndeYWS80b948Ro0aBWTdPmvWrGHHHbPhthq/BE488USuueYaXnrpJYYOHcrOO+/MgAEtjsdYkTPOOIMZM2awYsUKBg0axKWXXsoxxxzDuHHjAFi/fj1f+MIXOOmkkwDYsGEDixYt2uSA8Ny5czn99NMZPXo0a9as4eKLL+bQQw/tcCzLli1jwoQJvPfee2zYsIHx48dzyimndHrfqqGiQdokHQzcAvwdEPAh4J8iYk6+4WU8SJs15UHarC3z58/nuuuu48orr6x2KFXV2iBtFbX4I+KvkvYBhqaipyNiXVcGaGbWVfbff//CJ/22dGQ8/oPJbpfYFxgpiYi4IZeozMwsN5UO0nYj8BGgRHb/XMhusuLEb2ZWYypt8dcD+0aed20xM7NuUel5/PPJDuiamVmNq7TFvwvwpKTZwNrGwog4NZeozMwsN5Um/u/lGYSZmXWfSk/nfFjSnsBHI+L+dNVun3xDM+t52jvf3+f6Wy2odFjmrwG3A79IRQOBqTnFZGZmOar04O45wOHAm/D+TVl2q+SFkvpIelzS79P8EEmzJC2SdKukrToTuJmZdU6liX9tRLzbOCOpL9l5/JU4n+wGLo1+CFwVEXsDbwATK1yPmZl1gUoT/8OSLgL6pXvt/g74z/ZeJGkQ2Vj+v07zIrtr1+2pyhRgbAdjNjOzzVDpWT2TyVrm84CzgT+Qknk7fgx8C9ghze8MrIyI9Wl+CdnxgmYkTQImAdTV1VEqlSoM1WrdtGkvt7n8hBM+xOjRq9qsA9kwvN1dz3+nVgsqGp2zUyuWTgFOjoj/Kelo4F+As4BHUzcPkvYA/hgRbd66xqNzFkslZ85Ua3ROn9VjtWSzRueU9Dwt9OlHxF5tvOxw4FRJJwPbAB8Argb6S+qbWv2DgKWVxGBmZl2jI2P1NNoG+BzQ5i3vI+LbwLcBGlv8EfFFSb8DPks2vv8E4O6OhWxmZpujooO7EfFa2WNpRPyY7KBtZ1wIfEPSIrI+/2s7uR4zM+uESrt6RpbNbkH2C6DisfwjYgYwI00/B4yqOEIzM+tSlSbvH5VNrwdeAMZ3eTRmvYQPAltPVulYPcfkHYiZmXWPSrt6vtHW8ojwzS3NzGpER87qORi4J81/GpgNPJNHUGZmlp9KE/8gYGREvAUg6XvAvRHxpbwCMzOzfFQ6Vs8A4N2y+XdTmZmZ1ZhKW/w3ALMl3ZXmx5INsGZmZjWm0rN6LpP0R+DIVPSViHg8v7DMzCwvlXb1AGwLvBkRVwNLJA3JKSYzM8tRpbdevIRsqIVvp6Itgd/kFZSZmeWn0hb/OOBUYDVARPydjWPsm5lZDak08b8b2cD9ASBpu/xCMjOzPFWa+G+T9AuysfS/BtwP/Cq/sMzMLC/tntWT7pN7K7AP8CYwFPhuREzPOTYzM8tBu4k/IkLSHyLiAMDJ3sysxlXa1fOYpINzjcTMzLpFpVfuHgJ8SdILZGf2iOzHwLC8AjMzs3y0mfglfTgiXgRO7KZ4zMwsZ+119UwFiIjFwJURsbj80dYLJW0jabakuZIWSLo0lQ+RNEvSIkm3StqqS/bEzMwq0l7iV9n0Xh1c91rg2IgYDowATpJ0KPBD4KqI2Bt4A5jYwfWamdlmaC/xRyvT7YrMqjS7ZXoEcCxweyqfQjbSp5mZdZP2Du4Ol/QmWcu/X5qGjQd3P9DWiyX1AeYAewM/BZ4FVkbE+lRlCTCwlddOAiYB1NXVUSqV2t8b6xVGj17V5vJSqdRunWrVa/w7rbSeWTUoG4kh541I/YG7gIuB61M3D5L2AP4YEfu39fr6+vpoaGjIPU7rGcaOndnm8qlTj2i3TrXqTZ16BFDZPpjlTdKciKhvWt6RYZk7LSJWAg8Bh5EN+9D4S2MQsLQ7YjAzs0xuiV/Srqmlj6R+wBhgIdkXwGdTtQnA3XnFYGZmzVV6AVdn1AFTUj//FsBtEfF7SU8Ct0j6PvA4cG2OMZiZWRO5Jf6IeAI4sIXy54BReW3XzMza1i19/GZm1nM48ZuZFYwTv5lZwTjxm5kVjBO/mVnB5Hk6p9kmfDVrc35PrBrc4jczKxgnfjOzgnHiNzMrGCd+M7OCceI3MysYJ34zs4Jx4jczKxgnfjOzgnHiNzMrGCd+M7OCceI3MysYJ34zs4LJ82bre0h6SNKTkhZIOj+V7yRpuqRn0vOOecVgZmbN5Tk653rgmxHxmKQdgDmSpgNnAQ9ExOWSJgOTgQtzjMOs5nkUT+tKubX4I2JZRDyWpt8CFgIDgdOAKanaFGBsXjGYmVlz3TIev6TBwIHALGBARCxLi14GBrTymknAJIC6ujpKpVL+gVquRo9e1ebyxs+4knrt1alWvY7sQx71zCqhiMh3A9L2wMPAZRFxp6SVEdG/bPkbEdFmP399fX00NDTkGqflr9LuikrqtVenWvU6sg951DMrJ2lORNQ3Lc/1rB5JWwJ3ADdFxJ2p+BVJdWl5HbA8zxjMzGxTeZ7VI+BaYGFEXFm26B5gQpqeANydVwxmZtZcnn38hwNnAvMklVLZRcDlwG2SJgKLgfE5xmBmZk3klvgjYiagVhYfl9d2zcysbb5y18ysYJz4zcwKxonfzKxgnPjNzArGid/MrGCc+M3MCsaJ38ysYJz4zcwKpltG57Ta5AHEzHont/jNzArGid/MrGCc+M3MCsaJ38ysYJz4zcwKxonfzKxgnPjNzArG5/H3Il19w3Az653c4jczK5g8b7Z+naTlkuaXle0kabqkZ9Lzjnlt38zMWpZnV8/1wDXADWVlk4EHIuJySZPT/IU5xmBWGF3V1ddYz3qv3Fr8EfEn4PUmxacBU9L0FGBsXts3M7OWdXcf/4CIWJamXwYGdPP2zcwKr2pn9URESIrWlkuaBEwCqKuro1QqdVdoNWv06FXt1imVSu3Wa3yve3K9rtrXrq7Xm94T670U0Wru3fyVS4OB30fE/mn+aeDoiFgmqQ6YERFD21tPfX19NDQ05BZnb9HVp3P25Hpd3Z/dG9479/FbU5LmRER90/Lu7uq5B5iQpicAd3fz9s3MCi/P0zlvBv4CDJW0RNJE4HJgjKRngOPTvJmZdaPc+vgj4oxWFh2X1zbNzKx9vnLXzKxgnPjNzArGid/MrGCc+M3MCsaJ38ysYJz4zcwKxonfzKxgnPjNzArGt16sAR5bxarBf3e9l1v8ZmYF48RvZlYwTvxmZgXjxG9mVjBO/GZmBeOzejqo0jsmmVnn+H8sf27xm5kVTK9v8Xf1vU97+nbNbFM9+X+sWrG5xW9mVjBO/GZmBVOVrh5JJwFXA32AX0eEb7puVqPcrVl7ur3FL6kP8FPgk8C+wBmS9u3uOMzMiqoaXT2jgEUR8VxEvAvcApxWhTjMzAqpGl09A4GXyuaXAIc0rSRpEjApza6S9HSTKrsAKzY3GKlq9SqKvxrxVbiuXaTK3v8qvsft2QVYUYX3rqvqvf831IPf47bqbPI/0NWxVWoz19cleag1XbCve7ZU2GNP54yIXwK/bG25pIaIqO/GkLqU46++Wt8Hx199tboP1ejqWQrsUTY/KJWZmVk3qEbi/yvwUUlDJG0FfB64pwpxmJkVUrd39UTEeknnAv9FdjrndRGxoBOrarUbqEY4/uqr9X1w/NVXk/ugiKh2DGZm1o185a6ZWcE48ZuZFUzNJX5JJ0l6WtIiSZOrHU9nSHpB0jxJJUkN1Y6nPZKuk7Rc0vyysp0kTZf0THresZoxtqWV+L8naWn6DEqSTq5mjO2RtIekhyQ9KWmBpPNTeU18Dm3EXxOfg6RtJM2WNDfFf2kqHyJpVspHt6YTVnq8murjT8M9/A0YQ3bh11+BMyLiyaoG1kGSXgDqIyK3Cz+6kqSjgFXADRGxfyr7d+D1iLg8fQHvGBEXVjPO1rQS//eAVRFxRTVjq5SkOqAuIh6TtAMwBxgLnEUNfA5txD+eGvgcJAnYLiJWSdoSmAmcD3wDuDMibpH0c2BuRPysmrFWotZa/B7uoQoi4k/A602KTwOmpOkpZP/EPVIr8deUiFgWEY+l6beAhWRXwdfE59BG/DUhMqvS7JbpEcCxwO2pvMe+/03VWuJvabiHmvnjKRPANElz0tAUtWhARCxL0y8DA6oZTCedK+mJ1BXUI7tIWiJpMHAgMIsa/ByaxA818jlI6iOpBCwHpgPPAisjYn2qUjP5qNYSf29xRESMJBuh9JzUFVGzIusvrJ0+w8zPgI8AI4BlwI+qGk2FJG0P3AFcEBFvli+rhc+hhfhr5nOIiPciYgTZaAOjgH2qG1Hn1Vri7xXDPUTE0vS8HLiL7I+o1ryS+m0b+2+XVzmeDomIV9I/8gbgV9TAZ5D6lu8AboqIO1NxzXwOLcVfi59DRKwEHgIOA/pLarwQtmbyUa0l/pof7kHSdungFpK2A04A5rf9qh7pHmBCmp4A3F3FWDqsMVkm4+jhn0E6uHgtsDAirixbVBOfQ2vx18rnIGlXSf3TdD+yE0wWkn0BfDZV67Hvf1M1dVYPQDrd68dsHO7hsupG1DGS9iJr5UM2ZMZve/o+SLoZOJpsCNpXgEuAqcBtwIeBxcD4iOiRB1Bbif9osu6FAF4Azi7rK+9xJB0BPALMAzak4ovI+sl7/OfQRvxnUAOfg6RhZAdv+5A1mG+LiP+d/p9vAXYCHge+FBFrqxdpZWou8ZuZ2eapta4eMzPbTE78ZmYF48RvZlYwTvxmZgXjxG9mVjBO/GZAGjnyxCZlF0hqccAtSTMk1dxNts3Aid+s0c1kFwSW+3wqN+tVnPjNMrcDn2ocTz0NJLY7cIakhvIx2JuStKps+rOSrk/Tu0q6Q9Jf0+Pw3PfCrAJO/GZAutp1NtnAeZC19m8D/i0i6oFhwCfSFZyVuhq4KiIOBj4D/LoLQzbrtL7tVzErjMbunrvT80RgfBo6uy9QB+wLPFHh+o4H9s2GqQHgA5K2LxvX3awqnPjNNrobuErSSGBbspu3/AtwcES8kbpwtmnhdeXjnpQv3wI4NCLeySles05xV49ZklriDwHXkbX+PwCsBv4haQAbu4GaekXSxyVtQTbCZKNpwHmNM5JG5BG3WUc58Ztt6mZgOHBzRMwlG3HxKeC3wH+38prJwO+BP5PdTKTRPwP16e5STwJfzy1qsw7w6JxmZgXjFr+ZWcE48ZuZFYwTv5lZwTjxm5kVjBO/mVnBOPGbmRWME7+ZWcH8fwg3JkDYlagaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = np.random.laplace(loc=15, scale=3, size=500)\n",
    "\n",
    "\n",
    "# An \"interface\" to matplotlib.axes.Axes.hist() method\n",
    "n, bins, patches = plt.hist(x=d, bins='auto', color='#0504aa',\n",
    "                            alpha=0.7, rwidth=0.85)\n",
    "plt.grid(axis='y', alpha=0.75)\n",
    "plt.xlabel('Value')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('My Very Own Histogram')\n",
    "plt.text(23, 45, r'$mu=15, b=3$')\n",
    "maxfreq = n.max()\n",
    "# Set a clean upper y-axis limit.\n",
    "plt.ylim(ymax=np.ceil(maxfreq / 10) * 10 if maxfreq % 10 else maxfreq + 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# An \"interface\" to matplotlib.axes.Axes.hist() method\n",
    "n, bins, patches = plt.hist(x=train_labels, bins=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], color='#0504aa',\n",
    "                            alpha=0.7, rwidth=0.8)\n",
    "plt.grid(axis='y', alpha=0.75)\n",
    "plt.xlabel('Training Label')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Distribution of training labels')\n",
    "plt.xticks([0,1,2,3,4,5,6,7,8,9])\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('training_label_distribution.png', dpi = 400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "general_ML",
   "language": "python",
   "name": "general_ml"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}