Intraday_dist_impact.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch\n",
    "from retail import retail\n",
    "import torch.distributions as d\n",
    "import torch.nn.functional as F\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We define the arguments used by our store. We also define the intra-day distribution as being sampled by a uniform distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define main store arguments as well as sampler size\n",
    "n_customers = 2500\n",
    "n_buckets = 4\n",
    "monte_carlo_size = 100\n",
    "store_args= {'assortment_size': 1000, 'bucket_cov': torch.eye(n_buckets)/100, 'seed' : 1066,\n",
    "             'max_stock': 1000, 'forecastVariance' :0., 'horizon': 100, 'lead_time': 1}\n",
    "bucketDist = d.uniform.Uniform(0,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create the list of the average daily reward for each customer distribution for the chosen policy\n",
    "summed_rewards_policy = []\n",
    "for i in range(monte_carlo_size):\n",
    "    sub_rewards = []\n",
    "    done = False\n",
    "    #Generate the store and its customer repartition throughout the day\n",
    "    torch.manual_seed(i)\n",
    "    sampled = bucketDist.sample((n_buckets,))\n",
    "    sample_bucket_customers = (n_customers*sampled/sampled.sum()).round()\n",
    "    store = retail.StoreEnv(**store_args, bucket_customers = sample_bucket_customers)\n",
    "    while not (done):\n",
    "        #Compute the order according to the policy \n",
    "        customers = sample_bucket_customers.max()\n",
    "        p = store.forecast.squeeze()\n",
    "        std = torch.sqrt(customers*p+(1-p))\n",
    "        order = F.relu(3*std+store.forecast.squeeze()*customers-store.get_full_inventory_position()).round()\n",
    "        # Step the environment and get its observation\n",
    "        obs = store.step(order.numpy())\n",
    "        # Store reward for the specific time step\n",
    "        sub_rewards.append(obs[1])\n",
    "        done = obs[2]\n",
    "    #Append average reward of this customer repartition to the list of rewards\n",
    "    summed_rewards_policy.append(torch.stack(sub_rewards).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2455b750>"
      ]
     },
     "execution_count": 4,
     "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": [
    "sns.kdeplot(torch.stack(summed_rewards_policy).numpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe a skewed distribution, where one distribution yields very high results. \n",
    "\n",
    "Let's investigate this distribution - it corresponds to seed 49."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([  78.,  462.,  414., 1546.])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.manual_seed(49)\n",
    "sampled = bucketDist.sample((n_buckets,))\n",
    "sample_bucket_customers = (n_customers*sampled/sampled.sum()).round()\n",
    "\n",
    "\n",
    "sample_bucket_customers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All customers are coming at the end of the day. This is actually a realistic distribution, as patrons usually visit the store after their working hours. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt(\"imbalance_result.csv\", torch.stack(summed_rewards_policy).numpy(), delimiter=\",\")\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}