check_sampling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check the functionality of `random.sample`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_theme()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Artificial weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 5, 3, 3, 4, 3, 3, 2, 6, 9, 7, 7, 5, 8, 9, 7, 5, 3, 2, 4, 1, 9, 0, 4, 9, 4, 7, 8, 9, 5, 6, 2, 1, 3, 0, 2, 0, 3, 6, 1, 0, 2, 7, 9, 6, 5, 9, 1, 1, 1, 1, 4, 4, 6, 5, 5, 3, 4, 9, 2, 6, 0, 9, 1, 6, 8, 5, 2, 5, 6, 7, 8, 2, 5, 5, 8, 2, 5, 1, 8, 1, 3, 3, 7, 5, 9, 3, 3, 4, 1, 8, 5, 4, 9, 3, 1, 5, 5, 0, 6]\n"
     ]
    }
   ],
   "source": [
    "# Create random weights with `random.choices`.\n",
    "population = range(10)\n",
    "weights = random.choices(population, k=100)\n",
    "print(weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate samples from `weights` and plot the histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Comparing weights and samples')]"
      ]
     },
     "execution_count": 13,
     "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": [
    "# Randomly sampling.\n",
    "samples = [random.sample(weights, k=1)[0] for _ in range(10000)]\n",
    "\n",
    "# Pandas Data Frame.\n",
    "df = pd.concat([\n",
    "    pd.DataFrame({\n",
    "        \"Source\": [\"Weights\"] * len(weights),\n",
    "        \"Value\": weights\n",
    "    }),\n",
    "    pd.DataFrame({\n",
    "        \"Source\": [\"Samples\"] * len(samples),\n",
    "        \"Value\": samples\n",
    "    }),\n",
    "])\n",
    "\n",
    "# Histogram of \"Weights\" and \"Samples\".\n",
    "hist = sns.histplot(\n",
    "    data=df,\n",
    "    x=\"Value\",  # 0, 1, 2, 3, ... -> food_indices\n",
    "    hue=\"Source\",  # Plot histograms respect to \"Weights\" or \"Samples\".\n",
    "    bins=len(population),  # Equivalent to number of food_indices.\n",
    "    multiple=\"dodge\",  # Seperate 2 histograms instead of overlapping them.\n",
    "    stat=\"probability\",  # Normalize histograms.\n",
    "    common_norm=False,  # Normalize seperately for 2 histograms.\n",
    "    shrink=0.6,  # Make bins not too close together.\n",
    ")\n",
    "hist.set(title=\"Comparing weights and samples\")"
   ]
  }
 ],
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