DataDistribution.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle as pkl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = pkl.load(open('ckpts/data_mnist_non_iid_1_num_workers_125_stratify_True_uniform_False_repeat_1.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_samples = []\n",
    "worker_id = []\n",
    "for i, (_) in enumerate(y):\n",
    "    worker_id.append(i)\n",
    "    num_samples.append(len(_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(10,3))\n",
    "plt.bar(worker_id, sorted(num_samples), color='b')\n",
    "plt.xlabel('devices')\n",
    "plt.ylabel('num-samples')\n",
    "plt.title('class-distribution')\n",
    "plt.xlim(left=0, right=125)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(10,3))\n",
    "plt.bar(worker_id, sorted(num_samples), color='b')\n",
    "plt.xlabel('devices')\n",
    "plt.ylabel('num-samples')\n",
    "plt.title('class-distribution')\n",
    "plt.xlim(left=0, right=125)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(num_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1732,)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.bincount(num_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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